Research Article KD-ACP: A Software Framework for Social ...downloads.hindawi.com/journals/mpe/2015/915429.pdfMathematicalProblems in Engineering a novel approach in social computing

Research ArticleKD-ACP A Software Framework for Social Computing inEmergency Management

Bin Chen Laobing Zhang Gang Guo and Xiaogang Qiu

Research Center of Computational Experiments and Parallel System Technology College of Information System and ManagementNational University of Defense Technology Changsha 410073 China

Correspondence should be addressed to Bin Chen nudtcb9372gmailcom

Received 4 June 2014 Accepted 23 August 2014

Academic Editor Praveen Agarwal

Copyright copy 2015 Bin Chen et alThis is an open access article distributed under the Creative CommonsAttribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

This paper addresses the application of a computational theory and related techniques for studying emergencymanagement in socialcomputing We propose a novel software framework called KD-ACPThe framework provides a systematic and automatic platformfor scientists to study the emergency management problems in three aspects modelling the society in emergency scenario as theartificial society investigating the emergency management problems by the repeat computational experiments parallel executionbetween artificial society and the actual societymanaged by the decisions fromcomputational experimentsThe software frameworkis composed of a series of tools These tools are categorized into three parts corresponding to ldquoArdquo ldquoCrdquo and ldquoPrdquo respectively UsingH1N1 epidemic in Beijing city as the case study themodelling and data generating of Beijing city experiments with settings of H1N1and intervention measures and parallel execution by situation tool are implemented by KD-ACPThe results output by the softwareframework shows that the emergency response decisions can be tested to find a more optimal one through the computationalexperiments In the end the advantages of the KD-ACP and the future work are summarized in the conclusion

1 Introduction

Emergency management attracts the attention of scientistsfrom social computing because the whole process of emer-gent events is deeply coupled with human society and theemergency response decisions need an approach to testifytheir effect without the reappearance of emergent events inthe society As a new paradigm of computing and technologydevelopment social computing helps scientists to understandand analyze individual and organizational behavior andfacilitate emergencymanagement research and application inmany aspects [1]

Based on the fruitful development of computationalmethodology on emergency management research over thelast decade lots of work has been done to solve the problemsin society domain Both the conceptual frameworks in mul-tiple discipline and the technological platforms developedfor the domain requirements are more and more popularin the research on emergency management especially theagent-based modelling and simulation [2] The bottom-uptechnique describes the society in microview by modelling

individual behavior communications in agents and evolu-tion rules of agent organizations It is worth notifying thatthe modelling of agent does not emphasize the intelligence ofindividual Large scale communications and the emergencephenomena are the objects of agent-based modelling andsimulation The agent oriented platforms such as Biowar [3]GASM [4] and EpiSims [5] to study emergency problemshave been proposed in many fields Biowar developed byCarnegie Mellon University is used to study the bioattacksin city with the ability of scalable agent modelling GASM(Global-Scale Agent Model) by Epstein simulates a globalH1N1 epidemic with 65 billion people EpiSims from LosAlamos national lab is used to testify the intervention mea-sures in epidemics of smallpox from United States Depart-ment of Health and Human Services

With the help of agent-based modelling simulationtechnique and the concept of artificial society [6] a novel con-ceptual framework based on artificial systems is introducedin the social computing The conceptual framework calledACP (Artificial Society Computational Experiments ParallelExecution) approach is proposed byWang in 2004 [7ndash9] It is

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2015 Article ID 915429 27 pageshttpdxdoiorg1011552015915429

2 Mathematical Problems in Engineering

a novel approach in social computing to solve the problemsin society domain ACP approach is categorized into threeaspects representing and modelling society with artificialsystems analysis and evaluation by computational experi-ments and control andmanagement of real society by parallelexecution Under the instruction of conceptual framework ofACP approach a wide spectrum of complex systems such astransportation medicine finance and environment can bestudied in the computational manner Actually many real-world applications using ACP approach have been developedto solve the real problems in domains For instance complexsocioeconomic system [10] and the research framework fore-commerce system [11] are the good applications of ACPapproach in the economic area The ACP-based frame-work for integrative medicine [12] is proposed to solve theproblems in medicine An overall framework of emergencyrescue decision support system of petrochemical plant [13]is proposed based on ACP theory to study environmentrisk accidents of petrochemical plant Parallel BRT operationmanagement system [14] based on ACP approach has beenconstructed to detect the quantity of passengers on stationsreal-time traffic flow on stations or at intersections andqueuing length of vehicles on the road A novel parallel sys-tem for Urban Rail Transportation (URT) [15] based on ACPapproach is proposed to address issues on safety efficiencyand reliability of the operation of URT An artificial powersystem [16] is set up on the models of power systems andcomplex power grids to provide a feasible approach for thecontrol and management of the modern power system

Although a lot of work has been done on the concepts andtheory framework to study problems by social computing thefollowing problems of computational experiments in emer-gencymanagement are still not solved from the perspective ofmodelling for social systems theory and software frameworkof platform implementation

(i) The modelling and simulation of emergency man-agement are not given the special considerationThe representation of society focuses on the genericmodelling of agent (represented by Repast [17 18])The description of environments is too simple tomeet the requirements from research on emergencyproblems such as the building size the place relatedagent contact frequency

(ii) The existing tools and platforms cannot support thedesign of experiment Computational experimentscannot be done systematically and automatically

(iii) The existing applications of ACP-based frameworksare still domain specific A generic workflow andintegrated toolkit are needed to implement ACPapproach especially in the application of emergencymanagement

Therefore it is necessary to develop an ACP-based softwareframework for the research on emergency management Theartificial system is the projection of real world in the emergentscenarioThemodelling of the system including the emergentevents modelling and intervention measures modelling thedesign of computational experiments considering the settings

of emergency parameters the settings of large samples exper-iments and the parallel execution with loose connection ofreal society should be considered inside the software frame-work

As a result the purpose of this paper is to proposea software framework called KD-ACP applying the ACP-based computational theory and corresponding methods instudying emergency management problems KD is short forthe Chinese phonetic alphabets of China National Univer-sity of Defense Technology KD-ACP means the softwareframework is developed by National University of DefenseTechnology to implement ACP approach The remainder ofthis paper is organized as follows Section 2 summarizes theexisting agent-based modelling and simulation platformsSection 3 introduces ACP approach first and proposes theKD-ACP platform Section 4 illustrates the modelling ofBeijing city with KD-ACP both the agent models and initialdata are considered Section 5 shows how to do experimentswith KD-ACP using the H1N1 case study in artificial BeijingIn the end the paper is concluded in Section 6

2 Related Works

There have beenmany efforts on social computing especiallyon the emergency management Agent-based modelling andsimulation are popular in the implementation of socialcomputation The related works are mainly categorized intoSWARM-like agent-based modelling and simulation plat-forms and agent-based platforms for emergency manage-ment

21 SWARM-Like Agent-Based Modelling and SimulationPlatforms SWARM [19] originally proposed by Santa Feinstitute is widely used in many research areas such asbiology ecology and society The tool provides a simulationenvironment for simulating agent with the support of a seriesof class libraries It is worth noting that SWARM is the pre-cursor ofmultiagent simulation tool it influences lots ofmul-tiagent simulation platforms such as NetLogo [20] RePast(REursive Porus Agent Simulation Toolkit) MASON [21]and SOARS (Spot Oriented Agent Role Simulator) [22]

NetLogo is a multiagent programming language andmodelling environment for simulating natural and socialphenomena It is particularly well suited for modelling com-plex systems evolving over timeThe language is easy to studyand the agent-based complex systems could be built rapidlyRePast is a software framework for agent-based simulationcreated at theUniversity of Chicago An extensible simulationpackage makes RePast become a generic multiagent simula-tion platform in social science research computing MASONdesigned by George Mason University is used to serve as thebasis for a wide range of multiagent simulation tasks rangingfrom swarm robotics to machine learning to social complex-ity environments The tool is a fast discrete-event multiagentsimulation toolkit in Java SOARS is designed by TokyoInstitute of Technology to describe agent activities under theroles of social and organizational structureDecomposition ofmultiagent interaction is the most important characteristicsin this framework

Mathematical Problems in Engineering 3

All the SWARM-like agent-based modelling and simu-lation platforms provide a portable lightweight and easilyextensible environment for simulating agents in arbitraryresearch areas However the heterogeneity in specific socialcomputing domain is not considered Furthermore most ofthe platforms cannotwell support large scale agent simulationbecause of the lightweight engine The engine cannot affordthe simulation of super cities like Beijing andNewYorkwhichhave millions of people

22 Agent-Based Platforms for Emergency ManagementBiowar proposed by Carnegie Mellon University simulatesthe impact of background diseases bioterrorism attackswithin a city 62 diseases are modeled in this platform tosimulate the outbreaks on the populationrsquos behavior GASM(Global-Scale Agent Model) is designed to study the spread-ing of H1N1 65 billion population is modeled with thesupport of official statistical data A global H1N1 spread fromTokyo is simulated in GASM EpiSimS proposed by LosAlamos lab simulates the spread of disease in regions suchas cities allowing for the assessment of disease preventionintervention and response strategies The daily movementsand interactions of synthetic individuals are representedexplicitly Burke and Epstein propose a computational modelof smallpox epidemic transmission and control [23] Theagents in thismodel interact locally with one another in socialunits such as homes workplaces schools and hospitals

However these platforms cannot provide a generic soft-ware framework to study emergency problems Biowar onlyfocuses on social networks individuals are all modeled as thenodes of social networks Agents in GASM and EpisimS areisomorphic without considering the heterogeneity in specificdomains

To sum up this section briefly reviews the existing mul-tiagent simulation platforms including SWARM-like agent-based modelling and simulation platforms and agent-basedplatforms for emergency management However they can-not satisfy the requirements of simulation performanceadaptability of software framework and heterogeneity ofindividuals in research of different emergency scenarios

3 KD-ACP

KD-ACP is an integrated software framework designed andimplemented based on the principle of the ACP approachshown in Figure 1

31The ACPApproach ACP approach is a social computing-based research paradigm It is composed of three componentsas its name artificial society for A computational experi-ments for C and parallel execution for P The basic idea ofACP approach is listed as follows

(i) Model the complex societies involving human behav-ior and social organizations as artificial societies usingmultiagent modelling techniques in a ldquobottom-uprdquofashion Artificial societies are regarded as a researchplatform to study emergency management

Actual societies Artificial societies

Managementand control

Experimentationand evaluation

Training andlearning

TestingTesting Operation

Figure 1 The parallel execution of ACP approach [8 33]

(ii) Utilize innovative computing technologies to evaluateand analyze various factors in emergency manage-ment quantitatively the computers are regarded asthe experimental social laboratories for investigatingemergency management problems

(iii) Provide an effective mechanism for the control andmanagement of complex actual social society throughcomparison evaluation and interaction with artifi-cial society

It is worth notifying that ldquoPrdquo here is not the ldquoparallelrdquo inldquoparallel simulationrdquo but the representation of ldquoparallel exe-cutionrdquo The idea of parallel execution is to build the parallelscenarios by paralleling the actual societies and artificialsocieties Consequently parallel control and management ofactual societies are implemented with the help of interactionsbetween parallel scenarios The goal of parallel executionis to find the best plans to adjust the methods of controland management based on the comparison and analysis ofdifferences between actual and artificial societies Artificialsocieties provide possible simulated results of evolutions byrepeated computational experiments The simulated resultsprovide evidences for the adjustment plans These plans areused in the control and management of actual societies suchas emergency management After the application of theseplans the observations from actual societies are collected forthe comparison with expectation The differences are used tofeedback to artificial societies The new turn of comparisonand analysis to find best adjustments of control and manage-ment is repeated

The mechanism of ldquoparallel executionrdquo has been provedto be effective for use in networked complex traffic systemsand is closely related to emerging technologies in cloud com-puting social computing and cyber-physical-social systems[24] In order to promote the development of parallel controland management in emergency management the artificialsociety is proposed in ACP approach which is the expansionof ldquoartificial traffic systemsrdquo

Instructed by the ACP approach KD-ACP is also com-posed by three components The details of the architectureand implementation of KD-ACP are discussed below

32 The Software Architecture of KD-ACP The architectureof KD-ACP is shown in Figure 2 the software framework iscomposed of a series of tools These tools are grouped intothree parts to support artificial society modelling computa-tional experiments and parallel execution


A part

C part

P part

Population and GeospatialEnvironment Generation Tool (PGET)

Generic ModelingEnvironment (GME)

Artificial society population andgeospatial environment database

Initialize

InitializeInitialize

Model DevelopmentTool (MDT)

FSM based models

Artificial SocietyEditor (ASE)

Scenario ofartificial society

Artificial societyruntime database

Agent and emergencymodel repository

Experiments DesignTool (EDT)

Experiment plans

InitializeSettings of

emergent eventsEmergent Events

Configuration Tool (EECT)Intervention Measures

Configuration Tool (IMCT) Settings of intervention measuresemergency response plans

Experiments ManagementTool (EMT)

Population data emergent eventsdata and intervention measures

Open source dataRegistration Tool (OsdRT)

Actu

al so

ciet

y Artificial SocietySituation Tool (ASST)

Optimal emergencyresponse plan

Internet

C++ code of modelsdll of models

Figure 2 The software architecture of KD-ACP

In the ldquoArdquo part Generic Modeling Environment (GME)[25] and Model Development Tool (MDT) are the kerneltools in the modelling of artificial society GME is anopen source modelling tool which supports domain-specificmodelling The domains of artificial society are created byGME in our work Models such as agent environmentemergent event and intervention measure are described inspecific domains first in GME With the help of modeltransformation thesemodels are all transformed to the FiniteState Machine (FSM) models Meanwhile code generationsare supported byMDT and thesemodels are all implementedin C++ Artificial Society Editor (ASE) is used to describethe concrete scenario of actual society which defines thescope of models set for artificial society Population andGeospatial Environment generation Tool (PGET) generatesthe population and geospatial environment data with thesupport of statistical data from actual society

In the ldquoCrdquo part Emergency Events Configuration Tool(EECT) initializes the models of emergent events whileInterventionMeasures Configuration Tool (IMCT) initializesthe models of intervention measures Experiments plans aregenerated byExperimentsDesignTool (EDT) Based on theseplans Experiments Management Tool (EMT) is used to runand manage the computational experiments to study theemergency problems

In the ldquoPrdquo part Artificial Society Situation Tool (ASST)seemed as the monitor of running artificial society Thestatistical data and situation are shown by ASST at runtimeIn the meantime the emergency response plans are made by

emergency decision organizations Parts of the influences ofemergency plans are reflected on Internet Open source dataRegistration Tool (OsdRT) is used to register the open sourcedata from Internet to artificial society

KD-ACP is developed using the BrowserServer archi-tecture the tools are integrated in the home page of KD-ACP as shown in Figure 3 Each tool is activated by the clickon the link For example Artificial Society Editor is startedwhen the link of ASE is clicked The working environmentand programming languages of tools in KD-ACP are listed inTable 1

Moreover the implementation of KD-ACP is mainlycomposed of modelling phase and computational experi-ments phase It will be discussed in the next section

33 The Modelling of Artificial Society in KD-ACP It is acritical problem to focus on the key parts of society in socialcomputing Based on the ACP approach the bottom-upmodelling is used to build the artificial society As a resultmodelling of artificial society is composed of three basic ele-ments agents environments and rules for interactionsHow-ever we still meet the problem that specific features shouldbe supported in artificial society For example emergentevents and intervention measures are required in artificialsociety for emergency management The modelling of onlybasic elements cannot cover the specific features in domainsTherefore domain-specific modelling [26] is introduced tosolve the problems in modelling artificial society





Emergent EventsConfiguration Tool (EECT)

Intervention MeasuresConfiguration Tool (IMCT)



Artificial SocietySituation Tool (ASST)

Open Source DataRegistration Tool (OsdRT)

Generic ModelingEnvironment (GME) Artificial

society (A)Computationalexperiments (c)

MDT EECT IMCT

ASST OsdRT

ASE PGET EDT EMT

Parallel execution (P)

Figure 3 The implementation of KD-ACP

Table 1 The working environment and programming languages of tools

Tools Working environment Programminglanguages

Developmentplatform User type


General computer (desktopapplication) NULL NULL Domain experts

Model Development Tool(MDT)

General computer (desktopapplication) C++ Visual Studio Model developers

Artificial Society Editor(ASE)

General computer (desktopapplication) C Visual Studio Domain experts

Population and GeospatialEnvironment generationTool


Emergency EventsConfiguration Tool (EECT)

General computer (withInternet Explorer clientside)

C ASPNET Domain experts






C ASPNET

Users of computationexperimentServer of EMT

Nodes in supercomputer(Console Program serverside)

C++ Visual Studio

Runtime Infrastructure ofEMT


C++ Visual Studio

Artificial Society SituationTool (ASST)

General computer (desktopapplication) C++ Visual Studio Domain experts



Java JSP Domain experts


M2T transformation

Modeling in domain

GME Generic Modeling Environment

Metamodeling

Instance of

FSM

Metamodeling for artificial society

DEVS

Petri Net State charts

Agentmetamodel

Environmentmetamodel

Emergency eventmetamodel

Interventionmetamodel

DAE

Domain-specific metamodels Semantic well definedmetamodels

M2M transformation

Modeling of artificial society

Interventionmodel

Emergency eventmodel

Environmentmodel

Agentmodel

FSM based emergent andintervention models

FSM based environmentmodels

FSM based agent modelsDomain-specific models

Semantic well defined models

M2M transformation

Agent model code Environment model code

Agent behavior model

Agent mentality model

Disease model code

Basic population model Disease spreading model

Disease state transformmodel

Intervention model code

Agent social networkmodel

Basic environmentinformation model

Roadnet model

Vaccination

Infectors isolation

Contactors isolation

Close work officesAgent contact model

Domain-specific models relevant codes

MDT Model Development Tool

Implementation

Figure 4 From metamodelling and modelling by GME to implementation by MDT of artificial society

331 The Principle of the Modelling of Artificial SocietyAccording to the principle of domain-specific modellingthe modelling of artificial society contains the followingsteps first metamodelling the basic elements of artificialsociety second modelling the specific features in domain ofemergency management third implementing the models ofartificial society in codes The whole process is illustrated inFigure 4The first and second steps are implemented in GMEwhile the third step is implemented in MDT

The first step is metamodelling which mainly focuses onconstructing the metamodels of artificial society Metamod-elling tries to study the common patterns of artificial societyThe outputs of metamodelling are metamodels which repre-sents the abstraction of the whole systemThe basic elementsof artificial society are described in metamodel The processof metamodelling is divided into four phases The first is theconstruction of the domain-specific metamodels As shown

in Figure 4 agent metamodel environment metamodelemergent event metamodel and intervention metamodelcompose the metamodel of artificial society The second isthe construction of the metamodels described by typicalmodelling formalisms such as FSM DAE DEVS and PetriNet [27] These formalisms are all semantically well definedThe third is the definition of the model transformationfrom domain-specific metamodels to metamodels of typicalmodelling formalisms The transformation standardizes themetamodels of artificial society by typical modelling spec-ifications The fourth is the definition of the transforma-tion templates from metamodels to code framework Thetemplates list the basic abstract interfaces of metamodels ofartificial societyThese abstract interfaces are implemented inthe specific-domain modelling and code generations

The second step is modelling the models of artificialsociety such as agent model environment model emergent


Figure 5 The metamodels of artificial society in GME

event model and intervention model are built Actuallythe models are the instantiation of metamodels in the laststep Different from the general modelling environment likeUML [28] the domain-specific modelling provides a familiarmodelling environment for the domain experts in artificialsociety For example emergency response experts only con-cern emergent eventmodel and interventionmodel inheritedfrom metamodels After constructing the domain-specificmodels based on domain-specific metamodels domain usersexecute the model transformation defined in the first stepAll the models of artificial society are transformed into FSMmodels As a result the models are implemented in this uni-fied modelling formalism (FSM) The model transformationmakes the simulation of the models possible

The third step is the generation of executable codes ofmodels The executable code framework is generated bymapping template from metamodels to code frameworkdefined in the first step Moreover domain developers alsoadd necessary codes to the framework to integrate thedynamic semantics of the models The code frameworkoutputs the dynamic link libraries by compilingThe dynamiclink libraries are loaded in the large scale artificial societyruntime infrastructure in computational experiments

332 The Metamodelling and Modelling of Artificial Societyby GME GME is used to build metamodels and modelsin our work As mentioned before the abstraction andcommon patterns of society are represented in metamodelsAccording to the bottom-up modelling style metamodels ofagent environment and communications are described inGME Figure 5 shows part of metamodels of artificial societyThe features of an agent metamodel are extracted from thecensus figures and statistical data Environment metamodelsimulates the geospatial places for the behaviors of agentsThemetamodel of communications among agents ismodeledto simulate the interactions such as infection in epidemicsand rumor propagation in public opinion formation eventsIt is worth notifying that the metamodel of communications

includes both the emergent event metamodel and interven-tion metamodel

From the perspective of modelling the details from spe-cific domains are considered in the models by the instantia-tion frommetamodels of artificial society For example socialrelationships based on complex networks are added in agentmodel to support the communications Agent activity is alsoused to quantify the agent activity under different scenarioEnvironment models are linked with the help of transporta-tion services subways and roads are modeled while the pathsearch is encapsulated in the services Emergent event modeland intervention model are also the domain-specific modelsThe modelling of artificial Beijing in GME will be discussedin detail in next section

333 The Implementation of Models of Artificial Society byMDT As mentioned before MDT is used to implementmodels such as agent environment emergent event andintervention According to the template of code frameworkthe implementations of models are generated by MDT Theimplementations are classified into two categories FSMmodels and services FSM models such as agents andenvironments are built under the specification of FiniteState Machine (FSM) [29] in MDT while all the servicessuch as transportation are encapsulated under the PublicService Standard This standard provides a generic interfacespecification for modelers to encapsulate public commonservices in artificial society FSM models like agent are builtstatistically from the quantitatively analyzed characteristicssuch as demographic attributes social behaviors emergencybehaviors and social networks Social behaviors describe thedaily behaviors of individuals while emergency behaviorsdescribe the individual behaviors in emergent events Forexample infected individuals are all isolated in hospital inSARS Isolation ismodeled as a typical emergency behavior inour work Correspondingly services are used to simulate themacroactual society Take transportation service for instancethe path search is needed by almost every agent duringmoving from spot to spot


Artificial society description in emergency

Description ofemergency

ArtificialSociety Editor

(ASE)

Agent behavior set

Agent role setAgent status set

Environment setAgent relationship set

Statistical data of real society

Descriptionof artificial

societyframework

Terminal of agent modeling andartificial society describing

middot middot middotAgent

metamodeling

Customized agentmodeling

Relationship modelDailyEmergency

Mod

el D

evelo

pmen

t Too

l (M

DT)

Agent modeling

Agent model Encapsulate

Encapsulate

Behavior model

Intervention

Emergency

Response

Daily

Social relationshipmodeling

Socialbehaviormodeling

Socialbehaviormodel

Emergencybehavior

model

Emergencybehaviormodeling

Building

Geospatial statistical data

Population statistical data Datacollection

Actualsociety

Artificial societypopulation and

geospatial database

Organizationservice

Transportationservice+

MappingAgent model andservice repository+ emergency model

repository

Climateservice

Geographicalservice

+

Agent status in emergencyEmergency management

Emergent event set

Social relationship statistical data

Environment statistical data

Population behaviorstatistical data

Emergency statistical data

Emergency organization statistical data

Geospatial and SocialEnvironment Generation

Tool (GSeT)

Agent model repository andpopulation database

C++C++C++C++

Modeling

Statistical modelJob ageSex location

Public service modeling

Organizationmodel

Transportationmodel

Geographicalmodel

Climatemodel

Interventionservice

Publ

ic se

rvic

e sta

ndar

d

Figure 6 The editing and initialization of KD-ACP

MDT provides domain experts with a tool to obtain thecode implementations of models With the help of compileenvironment like visual studio MDT also supports thefurther programming development of the specific domaindetails which cannot be described in modelling step

Both the FSM models and services are developed by theMDT first and then stored in the agent model and servicerepository The repository manages the models according tothe requirements from emergency problems and provides themodels for EDT to make the experiment plans

34 The Editing and Initialization of Artificial Society byASE and PGET As shown in Figure 6 ASE is used to editthe scenarios of artificial society within emergent eventsThe editing is composed of two parts (1) the statisticalinformation of artificial society in daily life such as the rolesof agents the relationships of agents and the types of environ-ments and (2) the statistical information of artificial societyin emergency including the statistical data of emergent

events the emergency organization and emergency relatedbehaviors of agents

According to the requirements of the editing thesestatistical data are collected from actual society manually bythe domain experts Based on these statistical data PGETgenerates the artificial society population and geospatialenvironment database The database supports the instanti-ation of artificial society at individual level For examplethe attributes such as age and gender of each agent can befound in the database With the support of the databaseFSM models and service repository discussed before itis sufficient for domain experts to study the emergencyproblems by computational experiments

35The Computational Experiments and Parallel Execution inKD-ACP The tools of ldquoCrdquo part and ldquoPrdquo part in KD-ACP areused to support the process of computational experimentsand parallel execution The working process is shown inFigure 7 EECT and IMCT are both the starting and ending


Agent model andservice repository

repository

Emergency EventsConfigurationTool (EECT)


Intervention MeasuresConfigurationTool (IMCT)

Load

Output Output

Internet

Emergencyresponse plans

Load

∙ Input models of emergencyevents∙ Output settings of agentbehavior in emergent events+ settings of emergent events

∙ Input models of interventionmeasures∙ Output settings of agentbehavior in interventionmeasures + settings ofintervention measures

+ emergency model

∙ Input data from internetnetworks∙ Output population data +emergency events data +intervention measures data


geospatialenvironment database


OutputThe most optimal

emergencyresponse plan

Actual society

OutputOutputOutput

Input

Load

Load

Load

Load

Output


Emergency decisionorganization

ClusterRuntime infrastructure

Deploy

Deploy

Experimentsdesign tool (EDT)

Artificial societyinitial data file

Experimentplan

Artificial societymodels

Experiments Management

large scale artificial societyruntime infrastructure

Tool(EMT) +super computerTIANHE-1A

Load

∙ Input experiment plan +artificial society models +artificial society initial data file∙ Output artificial societyruntime state data

∙ Input description of artificialsociety + artificial society populationand geospatial environment society+ emergent events configuration +

intervention measures configuration∙ Output experiment plan + artificialsociety models + artificial societyinitial data file

∙ Input runtime statistical data∙ Output graphics charts ofartificial society statistical data +situation of artificial society

Figure 7 The computational experiments and parallel execution of KD-ACP

point The emergent events and intervention measures areconfigured by EECT and IMCT respectively The configu-rations of emergent events are used to simulate both thereal emergencies like SARS and H1N1 and the supposedemergencies for experiments Similarly the configurationsof intervention measures are also used to reproduce thereal one and simulate the supposed one The repeat of theemergency is used to verify the models while the supposedconfigurations are used to obtain the optimized decision planto the response of the possible emergencies

With the input of artificial society model and servicerepository artificial society population and geospatial envi-ronment database and the configurations discussed beforeEDT generates the experiment plans to meet the require-ments of research on emergency management The outputof EDT includes artificial society models artificial societyinitial data files and experiment plan The models aredownloaded from the repository while the data file is thecollection of data from the database to initialize the modelsWhen the models and data files are ready EMT loads theexperiment plan and deploys the models and data to thecluster or TIANHE-1A supercomputer [30] which was theworldrsquos fastest supercomputer built by National University of

Defense Technology (NUDT) in China in 2010 According tothe plan the experiments are done repeatedly on the largescale artificial society runtime infrastructure [31 32] by themultisample settingsThework process is the implementationof computational experiments in ACP approach

Traditionally emergency response plans are made byemergency management theories and experiences The onlyway to test the effective of plans is the feedback results of realworld ACP approach provides a novel method to supportemergency response plan making by parallel execution Asshown in Figure 7 the work process of KD-ACP is composedof two loops The inner loop composed by red arrowsdescribes the process of computational experiments while theouter loop of yellow arrows illustrates the process of parallelexecution During the runtime of computational experi-ments the statistical data of artificial society is collected andstored in the artificial society runtime database Based on thedatabase ASST outputs the customized situation of runningartificial society by graphics charts and situation mapsThe information is sent to the organizations of emergencydecision to support making the emergency response plansWith the help of computational experiments loop these plansare simulated repeatedly to find the most optimal one


Open Source Data Registration Tool (OsdRT)



∙ Web information collecting

∙ Deep mining in social media

∙ Web information denoising

∙ Information filtering

Data acquisition

Actual society

Open source informationin social media

Internet

∙ Basic element extracting∙ Individuals and organizationsextraction∙ Sentiment analyzing andopinion mining∙ Social network analyzing

Data extraction

∙ Domain knowledge description∙ Information fusion and collisiondetection∙ Domain knowledge construction∙ Social media informationstandardization

Data standardization

Knowledge

Figure 8 The compositions of OsdRT [34]

Moreover the most optimal plan is used to the responseof emergency in actual society According to the idea ofparallel control in [24] the feedback of actual society ispartly collected from Internet networks by OsdRT As shownin Figure 8 OsdRT is composed of three components dataacquisition data extraction and data standardization Dataacquisition collects mines and filters information fromsocial sensing networks Data extraction includes basicelement extracting individual and organization extractingsentiment analyzing and social networks analyzing Datastandardization specifies the useful knowledge and sent it toconfiguration tools in KD-ACP

By processing in OsdRT the knowledge about emergentevents and intervention measures are analyzed first andregistered in the EECT and IMCT The registration updatesthe settings of emergent events and intervention measuresThis loop composed of yellow arrows implements the parallelexecution in ACP approach The implementation of parallelcontrol and management provides a data-driven approachthat considers both the engineering and social complexityfor modelling analysis and decision making in emergencymanagement

4 Modelling Beijing City with KD-ACP

41 How to Build the Artificial Beijing According to themodelling of artificial society in KD-ACP discussed beforethe Beijing city is modeled as follows

To meet the requirements of emergency managementsthe basic elements of artificial society are extended Asshown in Figure 9 six elements are required to simulate thecity agents environments transportation activity schedulecommunication and agent activity

411 Modelling Artificial Beijing Figure 10 shows the mainGUI of GME for the modelling of artificial modelling inpublic health events Metamodels are listed in the left area ofFigure 10 the list provides basic syntax elements for domainexperts to model artificial society Domain experts build

models based on the knowledge of their own MeanwhileGME supports hierarchy for building large scale systemsThesyntax symbol listed in GUI can be extended in new tab bydouble clicking Take agent for example the model of agentcan be detailed by edition in another tab page of agent

As shown in the center of Figure 10 the models ofartificial Beijing consist of five parts models of agent andenvironment domain models of public health events inter-ventionmodels controllermodels and services Agentmodeldescribes individuals in society it is composed of basicpopulation information action social relationships activityschedule and disease related information Activity schedulerepresents individualrsquos physical actionmodel focusing on thedaily action of agents Environment model includes physicalentities such as buildings playground transportations andagents contained in environment Domain models of publichealth events are composed of the propagation model of dis-ease disease state transition model and so on Interventionmodels include the settings of vaccination isolation and soon The models mentioned before are all FSM models Themechanisms of these models will be detailed in next sectionsController models and services are the public service mod-ules they are implemented in the development in MDT

412 Modelling Agents and Environments Under the speci-fication of FSM agent and environment models are imple-mented in two parts the state space and state transitionsThe state space is composed of the demographic attributesand behavior related attributes The transitions are triggeredwhen the conditions of states are satisfied As shown inFigure 11(a) the action of agent is changed when the ldquonexttimerdquo condition is satisfied in agent model while the agentslist is changed when the agent arrival condition is satisfied inthe environment model

413 Modelling Activities Agent activities come from theagent state transitions of actions such as movements andcommunications The actions of agents are instructed by theactivity schedule shown in Table 2 Activity schedule lists


Transportation EnvironmentsEnvironments

Agents

Artificial Beijing

Agent activityActivity schedule

Agent activity

Communication by socialrelationship networks


Figure 9 The basic elements of artificial society

Syntax elements of DSM

Services

Models of agentenvironment andactivity schedule

Domain models of publichealth events

Intervention models

Modelingaspect ofdomainexperts

Controllermodels

Figure 10 The models of artificial Beijing in GME

all the actions with probability in one day for agents inboth normal and emergent situation [38] There are severaltypes of activity schedule in artificial Beijing student agentactivity schedule worker agent activity schedule emergentagent schedule and so on For example Table 2 gives an agentactivity schedule Upon the instruction of activity schedulestudent agent changes the actions by 119901

119894after state transitions

The119901119894in the tablemeans the action probability in the relevant

period In the duration from 0800 to 1200 a student agenteither goes to classroom to have class or goes to libraryto study The probability of class action is 119901

2while the

probability of study action is 1 minus 1199012

Agent behaviors are decided by the settings of activityschedule In addition to the daily activity schedulementionedbefore emergent activity schedules are also considered inour work Take public health events for instance an infectedagent changes schedule from a normal one to emergentone The workflow of a susceptible agent is illustratedin Figure 12 to show the change of behaviors After theinfection the agent is set in incubation phase Not all theincubation agents will become symptomatic Some of themturn back to being susceptible and some of them becomesymptomatic The symptomatic agents change their activityschedule from normal to emergent In the emergent case


Next actionNext environment (location)

Advance to next actiontime

Find next environment inactivity chain by action

Current actionCurrent environment (location)

Q

q0

120575 Find next action inactivity schedule by time

F

Σ

(a) The state transitions of agent

Q

q0

120575 F

Current agent list

Current pollution level

Next agents list

Next pollution level

Agents entered

Infected agents entered

Add agents to currentagents list

Compute pollution levelby infected agents

Σ

(b) The state transitions of environment

Figure 11 The state transitions of models

Table 2 The activity schedule of a student agent

Duration (Δ119905) Activity Location Probability 119879Location (minute)0000ndash0600 Sleep Dormitory 119875

0(100) 360

0600ndash0800 Breakfast Dormitoryrestaurant 1198751(068) 120

Sports-breakfast-travel Playground-dormitoryrestaurant 1 minus 1198751(032)

0800ndash1200 Class Classroom 1198752(077) 240

Study Library 1 minus 1198752(023)

1200ndash1400 Lunch Restaurant 1198753(090) 120

Lunch-shopping Restaurant-convenience store 1 minus 1198753(010)



1800-1900Dinner Dormitoryrestaurant 119875

5(063)

60Sports-shopping Playgroundconvenience store 1198756(025)

Study Classroomlibrary 1 minus (1198755+ 1198756) (012)

1900ndash2200Rest Dormitory 119875

7(045)

180Dinner-rest Dormitoryrestaurant-dormitory 1198758(020)


2200ndash2400 Rest Dormitory 1198759(072) 120

Sleep Dormitory 1 minus 1198759(018)

infected agents go to hospital according to the treatmentschedule or stay in dormitory according to isolation scheduleAfter the treatment in hospital or self-immunoprocess agentsbecome healthy and immune of disease If the agents aretreated in the hospital they are not allowed to get out untilrecovered Moreover activity schedules are also influencedby the emergency response plans For example in the case ofisolation in emergency response plans the activity scheduleof agents who had contacted with infected is changed Onlythe locations including home and dormitory compose thisemergent schedule Likewise based on the statistical datafrom emergency response plans the additional possibilities

are added into activity schedulesThe behaviors of agents alsochange with activity schedules In the view of ACP approachthe injection of new emergency response plans implementsthe parallel execution of microagent behaviors

In another aspect contact frequency is also anothercrucial element to determine the infected rate in public healthevent It is important to model the contact frequency andcontact time of individuals Based on the studies on thecontact behavior of human being by questionnaire surveyEdmunds found that the contact frequency of individualcould be fitted approximately into a normal distributionand the mean and the standard deviation distributions are


Susceptible agent ImmuneYes

Healthy and immune

No

Choose schedule

Self-isolation Go to hospital

Staysymptomatic

No

Receive treatment

Contact withinfected agents

Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes

Figure 12 The workflow of a susceptible agent

168 and 85 [39] So it is possible to apply normal randomvariable to model the contact frequency In our work Box-Muller method [40] is used to generate the random variableof contact frequency shown in the following

119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)

in which 119865119862is the random variable of contact frequency 120583

119865

is the mean value of normal distribution 120590119865is the standard

deviation of normal distribution and 1205741and 120574

2are the

uniform random variables distributed in the interval [0 1]Based on (1) and survey data [41] the contact frequenciesof agent 119865contact(119860 119894) are discretized as in Table 3 within theconsideration of activity differences

Similarly the duration per contact between individualsis another key factor of AHC transmission The duration ofcontact could also bemodeled by the normal randomvariablein the following [42]

119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)

in which 119879119862is the random variable of duration per contact

120583119879

is the mean value of normal distribution 120590119879

is thestandard deviation of normal distribution and 120574

1and 120574

2

are the uniform random variables distributed in the interval

[0 1] In addition because the time spent in specific location119879Location(119860 119894) also follows the random distribution like (3)in our work In order to make the duration of contact119879contact(119860 119894) shorter than the 119879Location(119860 119894) the mean andstandard deviation of (2) is set up in (3) and (4) [42]

120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)

in which 119879Location is the time of an activity in a specificlocation set in Table 2 120583

119879and 120590

119879are the mean and standard

deviation of contact frequency As a result the duration ofagent 119879contact(119860 119894) in specific locations (listed in Table 2) isalso discretized in Table 3 within the consideration of activitydifferences

414 Modelling Transportation In artificial Beijing threekinds of travel models are considered walking inside thedistrict travelling by road networks and travelling by subwaynetworks According to the traveling models the agentmovements are implemented by the compositions of travelingmodels before So it is important to build the basic road


Table 3 The contact frequency and duration in different locations and activities by social relationships in case of student and teacher

Social relationship Activity Location 119879contact(119860 119894) (minute) 119865contact(119860 119894)

Student-student

Sleep Dormitory 0 0

Breakfast Restaurant 119873(52) 119873(32)Breakfast Dormitory 119873(52) 119873(21)Sports Playground 119873(21) 119873(53)Class Classroom 119873(21) 119873(21)Study Library 119873(21) 119873(21)Lunch Restaurant 119873(158) 119873(32)

Shopping Convenience Store 119873(203) 119873(21)Dinner Restaurant 119873(108) 119873(21)Dinner Dormitory 119873(158) 119873(32)Rest Dormitory 119873(63) 119873(52)

Teacher-student Class Classroom 119873(21) 119873(21)

Subway network of Beijing city (red lines) Road network of Beijing city (green lines)

Figure 13 The implementation of transportation for artificial Beijing

and subway networks With the help of Google map thekey points of road are sampled first and the road networksare generated by the links among point Similarly subwaynetworks are generated by the links between subway stationsmapped from the real stations in Google map Figure 13shows the road and subway networks in artificial Beijingwith the road networks in green and the subway networks inred Most of the districts in Beijing city are covered by thesetwo networks The algorithm of hierarchical route planningis proposed as follows

(i) Find the next position where agent will be located innext action

(ii) Obtain the travelingmodels from the statistical trans-portation data

(iii) Search the path from the starting position to thenearest road entry or subway stations

(iv) Search the optimal path from the starting station orroad entry to the nearest station or road exit of targetposition

(v) Search the path from the target subway station or roadexit to the target position

(vi) Attach the subway train number or the bus numberincluding the transfer information to the path accord-ing to the timetable of subway or bus

(vii) Connect all the paths obtained before Generate thepath from the starting position to the target position

Based on the algorithm for each agent in artificial Beijingthe transportation is simulated during the computationalexperiments It is worth noting that the activities insidethe buses or train cars are considered because the agentcommunication during travelling is also necessary to besimulated


(a) The implementation of agent in MDT (b) The code framework of agent

Figure 14 The agent model implementation in MDT [35]

415 The Implementation of Models by MDT All the modelsare developed in the MDT Figure 14 shows the GUI of thetool MDT is used to implement the state space and statetransitions of FSM models The code framework is shownin Figure 14(b) Using the C++ inheritance features and thetechniques of dynamic link library (dll) the models areencapsulated in the dll componentsThe flexible compositionmechanism is designed to support the models evolution incomputational experiments

416 The Description of Artificial Beijing by ASE As men-tioned in Section 34 ASE describes the artificial Beijingin macroview The statistical information of Beijing city iscollected by ASE Table 4 lists part of the description ofartificial Beijing generated by ASE

42 How to Obtain the Data of Beijing City

421 Population and Geospatial Environment Database ofBeijing City Based on the models developed for ArtificialBeijing it is necessary to generate the initial data to do thecomputational experiments Because the individual level dataare not available it is necessary to construct an individual-based population database for both accurate computationalexperiment and determining optimal decisions

According to the state spaces of models discussed inSection 412 the geospatial and population database of Arti-ficial Beijing is designed as shown in Figure 15 The kernelpart of database consists of the tables such as agent list tablegeospatial environment list table household list table andagent distribution table Agent list table is an individual leveltable used to store the data to initialize the state space of agentmodel such as id and gender age Geospatial environmentlist table is also an individual level table which stores thedata of environment attributes such as id street id andtype Household list table and agent distribution table storethe statistical data for the data generation for agent andenvironment models With the help of the geospatial andpopulation database the data generating of artificial Beijingis proposed in the next section

422 Generating Geospatial and Social Environment Datafor Artificial Beijing by PGET The algorithm of generatinggeospatial and social environment data for artificial Beijingis used to quantify the spatial distribution of population andformalize geospatial behavior of each agent The algorithmshown in Figure 16 is capable of generating a syntheticartificial society which allowsmultiresolution statistical datasocial interactive behavior and multilayer social networksto be integrated together The synthetic population canrepresent individual agents in the form of households andhousehold members and the synthetic population is statis-tically equivalent to a real population For each householdcharacteristics such as address household size family typesand relationships are generated Each person is described bycharacteristics such as age gender social role and correlatedlocations The algorithm provides an effective methodologyto reconstruct the computing environment in high resolutionby using statistical data in low resolution leading to betterprediction and management of emergencies The details ofalgorithm are illustrated in [36]

As shown in Figure 17 the implementation of the algo-rithm is embedded inside the GEPTThe collective input datasuch as the data files of population distribution is collected inthe tool first then the algorithm is activated to generate thedatabase The generation lasts almost twelve hours one timewith the specified parameters settings When the generationis finished the data of artificial Beijing is obtained in thedatabase with 19610000 agents and 16000 environments

5 H1N1 Experiments in ArtificialBeijing with KD-ACP

With the support of the artificial Beijing modeled before thecomputational experiments and parallel execution are alsoimplemented by KD-ACP The experiments are designed tofind the most optimal emergency decision response plansEpidemic in city is a typical scenario in emergency manage-ment H1N1 epidemic in Beijing in 2009 is used to be the usedcase to test KD-ACP According to Section 34 it is necessarytomodel emergencymanagement first in order to support thecomputational experiments


Table 4 The description of artificial Beijing generated by ASE

Statistical features ofartificial Beijing Descriptions of statistical features Data sourcedefault values

Agent roles Baby child primary student middle school student college student worker retired

Demographic data file ofBeijing

Environment types Super market store restaurant park stadium hospital school cliniccommercial building dormitory building residential building


Demographic data Age distribution of population sex distribution of population householddistribution of population retired age distribution children distribution


Environment data Distribution of all types of environments Environment distributiondata file of Beijing

Relationships Classmate relative college friendship family Demographic data file ofBeijing

Varying parameters

Age difference of couples119873(120583120590) 119873(03) isin [0 12]Minimummarriage age (male female) (22 20)Age difference of children119873(120583120590) 119873(13) isin [1 10]Enrollment rate of kindergarten 120572 08Enrollment rate of primary school 120573 1Enrollment rate of middle school 120574 1Enrollment rate of college 120575 08Entrance age of kindergarten 1205721015840 3Entrance age of primary school 1205731015840 7Entrance age of middle school 1205741015840 13Entrance age of middle school 1205751015840 19

Age distributioninformation table

Agent settable

Geospatialenvironment set table

Household settable

Figure 15 The geospatial and population database of Beijing city

51 Modelling Emergency Management The modelling ofemergency management consists of two parts the modellingof emergent events and the modelling of intervention mea-sures H1N1 and the intervention measures for H1N1 aremodeled in our case

511 Modelling H1N1 by MDT H1N1 Model in agent simu-lates the states transitions of health status and the relevantactions Referring to SIR (SusceptibleInfectiveRecovered)[43 44] models agent has three health statuses susceptibleinfected and healthy with immunity Only susceptible agentcan be infected by the contact with infected agents After

the three infected phases (incubation being symptomaticand recovery) of H1N1 model agent is healthy again It isworthy mentioning that the recovered agents can be infectediteratively when the immunity disappears gradually by timeThe state transitions are illustrated in Figure 18

According to [45] the latent period of H1N1 was in aWeibull distribution within people from one to seven days[46 47] The distribution was usually centered in the rangebetween one and three days AWeibull random variable [40]is used to model the latent period as described in

119879Lat = 120572 times [minus ln (1 minus 120574)]1120573+ 120592 (119879Lat ge 120592) (5)


Censusdata

Generate GUID

Assign individualhousehold

Generate familystructure

Assign family roleto every member

Endow individualwith age gender

social role

Distribute environmentlocations on the map

School Workplace Entertainmentlocations

Consumptionlocations

Assign school orworkplace to

individual

Correlatedindividual withother locations

Generate friendshipin school

Generate coworkerrelations inworkplace

Assign individualadministrative

region

Generate housebuildings

Locate householdto house building

Generate familyrelation

Generateneighborhood

relation

PopulationEnvironment

Environment listSocial network

Figure 16 The algorithm of generating geospatial and social environment data [36]

Edit

Edit

Edit

Edit

Edit

Edit

BrowseStatistical characteristic of population

Geospatial population data

Population data of street

Population types

Geospatial and social environment generation tool

Workspaces

Parameters distribution settings

Household types

Roles in household

Household structure

Social roles

BrowseBrowseBrowseBrowseBrowseBrowse

Population distribution data

File path

Retired distributionPop distributionHousehold numHousehold distributionSex distributionAge distributionData type

Figure 17 The GUI of Population and Geospatial Environment generation Tool (PGET)


Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery

Increase before beingsymptomatic symptomatic

Decrease after being

Decrease by time

Infectivity

Immunity

Figure 18 The transitions of agent health status

119879Lat denotes the duration of latent period 120592 120572 and 120573 arethe location parameter scale parameter and shape parameterof Weibull distribution respectively 120574 is a uniform randomnumber in the range [0 1] According to statistics [47] 120592 120572and 120573 are set as 0 18 and 121 respectively Then the mean(standard deviation) of latent period is calculated as 159 days(058 day2) Furthermore infectious period is set as 7 days

A susceptible studentrsquos probability of getting infecteddepended on the infectivity of infectious agents his ownimmunity level the duration of the contact action and soon The infectivity of an infected agent evolved with timeduring the infectious period According to the statistics ofinfectivity in the chart with red bars in Figure 19 an infectedstudent had the highest level of infectivity in the second dayafter he had the first symptom The infectivity levels in otherdays in comparison with the second day are in Table 5 Theday labeled as ldquominus1rdquo means the day before the starting of thesymptomatic period

Based on the algorithm discussed before theH1N1modelis developed with the help of MDT The settings of influenzamodel will be discussed in Section 521

512 Modelling Intervention Measures by MDT In corre-spondence intervention measures model is used to ceasethe outbreak of H1N1 As discussed in [42 48 49] if theappropriate measures are taken when the infectious diseaseemerges the transmission of the disease could be sloweddown and the damage could be decreased Therefore it isnecessary and important to design emergency interventionmeasures in the artificial Beijing According to the MinistryofHealth inChina the intervention policies [50] are designedto control the spread of influenza such as H1N1 The inter-ventionmeasures of these policies including are interventionsactivated time vaccination rate antibiotic delay to hospitalisolation duration close workspace duration and limitationof activities listed in Table 6 Similarly the models of theseintervention measures are also developed with the help ofMDT Using the reasonable data ranges listed in Table 6intervention models can be initialized to do the computa-tional experiments These experiments are used to show how

Symptomatic1 2 3 4 5 6 7minus1

Recovered

Tim

e (da

y)

Infe

ctiv

ity

Figure 19 The distribution of infectivity rate of an infected agent[37]

Table 5 The proportion of infectivity

Days (119894) minus1 1 2 3 4 5 6 6Proportion of infectivity 03 05 1 08 04 02 01 005

to restrain the H1N1 transmissionThe settings of these inter-vention measures in IMCT are introduced in Section 522

52 Experiment Settings

521 H1N1 Settings by EECT EECT is designed to initializethe emergent events models such as H1N1The settings comefrom the specified emergent event models As shown inFigure 20 in theH1N1 used case the settings are composed ofthe distribution of infection source infection rate infectionperiod and so on The infection rate changes with envi-ronments and infection periods The settings are initializedfrom both the research of medicine on H1N1 and the data ofH1N1 outbreak in Beijing in 2009The process of influenza inartificial Beijing is simulated to repeat the possible scenariosof H1N1 break During the computational experiments ofartificial Beijing theH1N1 settings such as the period of beingsymptomatic are updated by the dynamic data registrationsfrom OsdRT The updating corrects the H1N1 model duringthe experiments With the help of OsdRT artificial Beijing isable to approach actual society It is also an implementationof parallel execution

522 Intervention Measures Settings by IMCT In the H1N1case the settings of intervention measures are shown inFigure 21 in IMCT These measures are designed under theinstruction of Emergency Decision Organizations like ChinaDisease Center (CDC) The measures such as vaccinationrate the rate of closedworkspace and the starting time can beinitialized in the tool Emergency response plans are imple-mented in these settings As illustrated in Figure 7 all the pos-sible compositions within value settings in reasonable inter-vals are tested to find the most optimal emergency responseplan For example different compositions of measures suchas vaccination rates and rates of workspace closed are usedto design the plans of experiments By the analysis of theresults the best composition of interventionmeasures can beobtainedThe analysis will be discussed in case study in detail


Table 6 The intervention measures of influenza

Intervention measures ValuesInterventions activated Time 119879Intervention isin [0 80th day]Vaccination rate 119877Vaccination isin [0 1]Antibiotic rate 119877Antibiotic isin [0 1]Delay to hospital 119867ospital(119860 119894) 119879infected lt 119905 lt 119879recovered + 119879HDelay 119879HDelay isin (0 3th day]Isolation duration 119868solation(119860 119894) 119879Intervention lt 119905 lt 119879Intervention + 119879IDuration 119879IDuration isin (0 14th day]Close workspace duration 119862lose(119864119894) 119879Intervention lt 119905 lt 119879Intervention + 119879CWDuration 119864119894 isinWorkspace 119879CWDuration isin (0 7th day]Limitation of agent activity duration 119871 imitation(119860 119894) 119879Intervention lt 119905 lt 119879Intervention + 119879LAADuration 119879LAADuration isin (0 7th day]119877Vaccination means the vaccination rate of artificial Beijing while 119877Antibiotic means the antibiotic rate used for infected agents in hospital119867ospital (119860119894) means that the infected agent 119860119894 is sent to hospital he or she will receive the retreatment immediately119868solation (119860119894) means that the infected agent 119860119894 is isolated at home he or she can only contact with the other agents until isolation duration is over119862lose (119864119894) means that the environment 119864119894 typed of workspace is closed until the close workspace duration is over119871 imitation (119860119894) means that the activities of agent 119860119894 are limited only the workspace and home are allowed until limitation duration is over

Incubation Weibulldistribution

Symbolic LogNormaldistribution

Recovering LogNormaldistribution

H1N1influenza

Settings of H1N1 influenza model

Disease model

Transmissionmodel

Figure 20 The settings of H1N1 in EECT

It is worth notifying that the settings of interventionmeasures are also influenced by OsdRT For example theH1N1 outbreak areas are notified by OsdRTThe interventionmeasures such as close workspace are used only in theoutbreak areas Therefore the unnecessary computation ofintervention measures is avoided So the data injection byOsdRT not only makes artificial society approach actualsociety but also increases the performance of computationalexperiments

53 Experiments by EMT When the initialization of arti-ficial Beijing is ready EMT is used to do the computa-tional experiments EMT is composed of two parts thecentral controller in the central node and the residen-tial service deployed in the computational nodes Centralcontroller loads the experiment plans and controls the whole

experiments process the GUI of controller is shown inFigure 22 EMT manages the computational experimentsin four steps cluster configuration load experiment plansmodels deployment and running experiments

531 Cluster Configuration Before the configuration clusterinformation is collected from the residential services insidethe cluster The information includes nodes amount nodenames node IPs and CPU occupations It is used to quantifythe computing power of the clusterThe cluster configurationis based on this information The nodes are selected forexperiments Then the LP (local process) [51] number foreach node is set according to the number of the CPU kernelsAfter the configuration the experiment plans are loaded tocustomize the models and data


Streets of close workspaces

Limitations ofactivities

IsolationsRespirator

Closeworkspace

Antibiotic

VaccinationHospital

Surveillance

Figure 21 The settings of intervention measures in IMCT

Information ofexperiment plan

List of experiment plans

Start | Pause | Reset | Stop | Speedup | Slowdown | Back

NodeName | CPU occupation | Memory occupation |Network | Models | Status

Figure 22 The deployment and running of experiments in EMT

532 Load Experiment Plan As discussed in Section 35 thedescription of experiment plans is composed of the infor-mation in three aspects models and the initial data for theexperiments the deployment mapping tables from the mod-els to the nodes and the plan of experiment executionWhenthe experiment plan is loaded by the EMT the file namesand model descriptions of models and data are listed Withthe support of cluster configuration models can be mappedto the nodes in the GUI according to the computationrequirements EMT also provides the setting of experimentsitself including the running times start time and end time

533 Models and Data Deployment Models are stored inthe agent population and services repository According tothe settings in experiment plan the customized models aredownloaded from the repository first then the models suchas agent model environment model emergent events modeland intervention measures model are integrated to be thecompositionmodels of artificial BeijingThemodels and datalocations in the nodes are configured in the ldquomodel pathrdquoand ldquodata pathrdquo in the GUI of central controller Finallythe models are uploaded to the nodes in cluster with theconsideration of partition The partition of artificial society


models is different from the multiagent system in [52]Models in artificial society are divided into three levels agentsand environments grids and city Agents and environmentsare encapsulated in grids which compose the whole cityThus the partition of artificial society becomes the partitionof grids The parallelism degrees are found to support thepartition of models agent independence and grid indepen-dence According to the parallelisms agents are partitioned toLPs according to the population distribution in grids Agentmovements among grids are simulated by the movementevents among grids in different LPs As a result a two-tierparallel architecture is proposed to support the simulation ofartificial society The architecture is detailed in [53]

534 Running of Experiments When the preparation isready the experiments can be done by the central controllerof EMT The running and control commands of the experi-ments are grouped into three aspects start pause or stop theexperiments get the running information of the nodes suchas the snapshot of the desktop of node With the help of thesecommands users can run the computational experimentsto do the research on the emergency management It isworth notifying that the simulation engine is optimized inorder to support the ten million agent simulation UsingGPU as coprocessor a two-tier parallel simulation engine isdesigned with support of MPI and OpenCL through phasedsynchronization [54] One-sided communication is used forreflection of remote simulation objects and message passingbetween processes A general kernel function prototype iselaborately designed and conditionally compiled for execu-tion on both CPU and GPU Moreover the optimizationoperations like load balance is developed under the instruc-tion of activity-based simulation [55] The densely populatedgrids are given more CPU and memory resource accordingto the activity predictions

54 Results of H1N1 Experiments with KD-ACP On thebasis of artificial society models and the intervention mea-sures mentioned in Section 512 a series of computationalexperiments are performed to study the H1N1 influenza inBeijing In our case of artificial Beijing 19610000 agentsare simulated in the cluster within 48 CPU cores and 128Gmemories the nodes are connected by kilomega networks Ittakes 18 hours to simulate a 250-day disease spreading

In order to illustrate how the optimal plans are obtainedbyKD-ACP the experiments are divided into four groups theexperiments ofmodel validation the experiments of sensitiv-ity analysis of vaccination rate the experiments of sensitivityanalysis of isolation and the experiments of the combinationof interventionmeasuresThese four groups show the generalprocess of the research on emergency management in H1N1influenza Firstly the models of H1N1 are validated withthe support of historical data Secondly the traditionalmedical interventions of pandemic such as vaccination areanalyzed to find the optimal plan Thirdly the traditionalnonmedical interventions of pandemic such as isolation areanalyzed to find the optimal plan Finally the combination ofinterventions is tested to show the combined effect

It is worth mentioning that all the experiments in ourwork are performed 100 times These experiments are ini-tialized with different settings of interventions in IMCT Theresults shown in the figures are the mean values

541 The Experiments of Model Validation In our artifi-cial Beijing the models of H1N1 influenza are built basedon the existing researches Wang and her colleagues hadstudied the H1N1 influenza by SEIR (SusceptibleExposedInfectiveRecovered) models [56] With the support of theirwork we build the models of artificial Beijing in the mannerof multiagents

In Figure 23 the control group shown in red line isdrawn based on the realistic statistical data collected in H1N1influenza in Beijing in 2009 The influenza lasts more thansix months and more than 170000 people are infectedComparing with the control group the simulated resultswithout interventions are drawn in blue It is obvious that thesimulated case fits the control group well before 70 days Butthe ascending of control group is slowed after 70 days andthe peak value (17883) is much less than the simulated results(25135) It is because the vaccination is activated at day 67 incontrol group according to the intervention measures usedin 2009 The restrain by vaccination becomes effective eightdays later The change of infected number after vaccinationis shown by red spotted line The trend of control group isdifferent from the simulation data after 75 days

However the simulated results (blue line) in compu-tational experiments fit the control group (as shown inFigure 23) in the first 70 days when no intervention mea-sures are executed The key features such as effective basicreproduction number (119877

0) are also almost the same in both

cases Therefore it can be concluded that the models ofartificial society built in microview are validated by therealistic statistical data in influenza in Beijing in 2009

In this group of experiments no interventions are exe-cuted in order to validate the models Therefore the simu-lated results of this extreme case which cannot happen todaycould be obtained As shown by blue line the number ofinfected agents without interventions reaches peak value atday 91 with 25135 consisting of 8052 in symptomatic phaseand 28950 in recovery phase It is obvious that the totalnumber of infected agents grows slowly at the beginningtime Then the number increases quickly when the influenzabreaks and it reaches its maximum value with 383870 in theend This phenomenon can be explained as in the followingAt the beginning of influenza due to the limited numberof infected agents the propagation of the disease remainsslow but many agents become susceptible When the numberof infected agents increases rapidly the spreading becomessignificant However the number of infected agents decreasesafter day 91 It is because the disease spreading relies on thesocial networks and spatial contact networks [49] It meansthat the infected agents can only infect agents either in thesocial relationship networks or in the spatially contacted

542 The Experiments of Sensitivity Analysis of VaccinationRate According to Section 512 vaccination is a typical


0

Infe

cted

agen

ts

0 50 100 150 200 250

Statistical dataSimulation data

25000

20000

15000

10000

5000

Day d

Figure 23 The results with no intervention executed and the com-parison with realistic statistical data in Beijing in 2009

medical intervention in influenza Traditionally the effective-ness of vaccination can only be evaluated after the influenzaand the key settings such as vaccination rate are determinedby the experience It is always difficult for emergency man-agement organizations to find the most optimal vaccinationrate in advanceTherefore the sensitivity of vaccination rate isanalyzed in this group of experiments The settings are listedin Table 7 average results are shown in Figure 24

Vaccination rates are set linearly (10 30 50 and70) to test the changes of influenza The peak value ofinfected agents decreases from 25135 22392 15787 6907to 638 respectively It shows that the more the agents arevaccinated the smaller the peak value of the infected agentswill be In the case of 70 vaccination the total infected agentis only 13799 It means that the influenza in this case does nothappen due to the high vaccination rate

The change of peak values also shows an interestingphenomenon Vaccination rates increase linearly the peakvalues decrease slower than the ascending of vaccination ratesat beginning but the descending becomes more and morefaster with the linear increase of vaccination rate It meansthat the small scale of vaccination (less than 30) does notmake senseWith the increase of vaccination rate the numberof susceptible agents decreases significantly In sequencethe possibility of infections is lowered greatly Additionallyit could be found that the decrease of peak values comeslater than the increase of the vaccination rates This is quitereasonable according to Mei et alrsquos work in [57]

As a result it is concluded that high vaccination rate iseffective in the control of H1N1 influenza though costly

543 The Experiments of Sensitivity Analysis of IsolationAccording to Section 512 isolation is a typical nonmedicalintervention It is the most used intervention measure in

Table 7 The initial settings of experiments of vaccination

Parameters ValueAgent count 19610000Initial infected count 40Interventions activated time(119879Intervention)

0

Vaccination rate (119877Vaccination) 0 10 30 50 70

0

Infe

cted

agen

ts

Loading intervention of vaccination

No vaccination10 vaccination30 vaccination

50 vaccination70 vaccination

0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d

Figure 24 The results of loading intervention of vaccination

the emergency management in influenza As mentionedbefore a problem also exists that the measure cannot bevalidated before the intervention Therefore the sensitivityof isolation is analyzed in this group of experiments It isworth mentioning that the isolation is usually combinedwith the hospital policy It means the infected individual issent to hospital meanwhile the people who contact withthe infected are all isolated As a result parameters delay tohospital and isolation duration are designed together in thesettings of experiment Delay to hospital stands for the periodfrom infection time to hospital time while Isolation durationrepresents the period when an agent cannot contact others ifhe has contact with an infected agent The settings of theseparameters are listed in Table 8 average results are shown inFigure 25

Firstly the parameter delay to hospital is simulated aloneto test the sensitivity The simulated results are shown insolid line in Figure 25 The peak value decreases from 6708to 2218 when the delay to hospital decreases from 15 daysto 1 day The half day change brings the 67 reductionMoreover the peak value almost approximates to zero inthe case of 05 days The influenza does not happen in thiscase It can be concluded that the earlier the infected agents


Table 8 The initial settings of experiments of isolation

Parameters ValueAgent count 19610000Initial infected count 40Interventions activated time (119879Intervention) 80Delay to hospital (119879HDelay) isolation duration (119879IDuration) (15 0) (1 0) (05 0) (15 3) (1 3) (05 3)

0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts

Loading intervention of isolation

Delay to hospital = 15



Delay to hospital = 15 isolation duration = 3



Day d

Figure 25 The results of loading intervention of isolation

are sent to hospital the less the agents will be infectedSo the construction of effective mechanism to find infectedindividuals is important in the emergency management ofinfluenza

Secondly the parameter isolation duration is added tosimulate the isolation of interventionObviously the isolationmeasure decreases the infected agents greatly Compared tothe parameter delay to hospital of 15-day case the peak valuedecreases from 6708 to 362when the isolation is added in theinterventionThe reduction of 6346 agents comes from the 3-day isolation of agents who had contacted with the infectedagents However the decrease of parameter delay to hospitalcannot bring the similar reduction in the 3-day isolationcases According to the figure the peak value decreases from362 to 51 from 15-day case to 05-day case only 311 agents aresaved from infection As a result the sensitivity of simulatedresults does not change obviously with delay to hospital inthe case of isolation It can be concluded that isolation is amore effective measure Though it is not easy for emergencymanagement organizations to decrease the delay to hospitaldue to the current monitoring mechanism the influenza canstill be controlled by isolation

544 The Experiments of Combination of Intervention Mea-sures Isolation is not the only nonmedical intervention ininfluenza close workspace is another common measureWhen close workspace is activated the workspaces are allclosed agents can only stay home at the closed time Luckilyisolation and close workspace are independent from oneanother It is possible for emergency management organi-zations to use these two measures together So this groupof experiments is designed to obtain the effectiveness of thecombination of intervention measures The parameters areinitialized with different settings listed in Table 9

Parameters delay to hospital and isolation duration is setin the middle values (1 day 3 days) according to the lastsection According to the experience close duration is set asseven days It is known that close workspace is a really costintervention measure the activated time is a key parameterWith the fixed parameters mentioned before the changeof interventions activated time is analyzed in this group ofcomputational experimentsThe average results are shown inFigure 26

Compared with the simulated results of experimentsdiscussed above Figure 26 strongly illustrates that influenzais ceased when the intervention measures are combinedtogether The peak value of infected agents is 141 in the ldquo80thdays of close-workspace and isolation with 1 day delay tohospitalrdquo case while 25135 in ldquono interventionrdquo case 2218 inldquo1 day delay to hospitalrdquo case 203 in ldquo1 day delay to hospitaland isolationrdquo case The interventions bring a great reductionof infection the peak values decrease from ten thousandslevel to hundred level

On the other hand the peak value even decreases to 108when the execution time of close workspace is 10 days earlierThe date of maximum value of infected agents is advancedfrom the 135th day to the 108th day The whole circle of H1N1influenza is reducedThe results of this group of experimentsshow that intervention measures should be activated as soonas possible and the compositions of interventions are moreeffective in emergency management of H1N1 influenza

In summary based on the general process lots of exper-iments could be done by KD-ACP to study emergency man-agement in H1N1 influenza Many conclusions are obtainedbased on the analysis of the experiments in this sectionAccording to these conclusions themost optimal response inour case study is the case of ldquo70th days of close-workspace andisolation with 1 day delay to hospitalrdquo It is because this casehas the fewest infected agents relatively But the modelling ofH1N1 influenza is not sufficient Economic and social factorsof interventions such as the money cost by vaccination andisolation the influence of the close-workspace in society


Table 9 The initial settings of experiments of isolation and closeworkspace

Parameters ValueAgent count 19610000Initial infected count 40Interventions activated time (119879Intervention) 70th day 80th dayDelay to hospital (119879HDelay) 1 dayIsolation duration (119879IDuration) 3 daysClose workspace duration (119879CWDuration) 7 days

50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts

Loading intervention of isolation and close workspace

Delay to hospital = 1 isolation duration = 3Start close time = 70 close duration time = 7Delay to hospital = 1 isolation duration = 3Start close time = 80 close duration time = 7

Day d

Figure 26The results of loading interventions of isolation and closeworkspace

and even opinion transmission of H1N1 in internet are notconsidered in our case study Without the analysis of thesefactors it does not make sense to talk about the most optimalintervention measures However KD-ACP had provided asoftware framework and general process of the research onemergency management Supported by KD-ACP the expertsof emergency management are able to study emergent eventssuch as H1N1 influenza as detailed as possible Based on theexperiments and analysis again and again the most optimalintervention measures will be found ultimately

55 Parallel Execution by ASST and OsdRT As discussed inSection 35 ASST andOsdRT are used to support the parallelexecution of artificial Beijing The statistical data is collectedand stored in artificial society runtime database at runtimeASST downloads the infection data from the database anddisplays it in the map As shown in Figure 27 the infectedagents are distributed in the districts in BeijingThe snapshotgives the geospatial distribution of the H1N1 epidemic Thelegends of the agents in different status are listed in the left

right corner in Figure 27 Five statuses are shown in the figurehealth without vaccination health with vaccination incuba-tion being symptomatic and convalescence ASST providesa very direct feeling for the emergency experts Partly withthe situation from ASST Emergency Decision Organizationmakes the emergency response plans to the response of theemergency scenario With the help of plans the epidemicin actual society can be restrained In the meantime theinfluenza information is reflected in the Internet Accordingto Figure 8 the data acquisition data extraction and datastandardization of H1N1 influenza are processed in OsdRTOsdRT is implemented by Dr Caorsquos team from Institute ofAutomation from Chinese Academy of Sciences [58] Thetool provides not only the reports of H1N1 SARS hand-foot-mouth disease and intestinal diseases are all monitored Theweb page is shown in Figure 28 H1N1 reports from Internetare collected first Based on the data extraction of the reportsH1N1 case of distribution is obtained and shown in theincidencemap As a result H1N1 incidence data is regarded asthe knowledge which is sent to EECT and IMCTThe settingsof emergent events and intervention measures are modifiedin order to make artificial society approach actual society innext turn of computational experiments With the help ofOsdRT the loop of parallel execution process integrates ACPapproach

6 Conclusion

This work provides an integrated software framework forsocial Computing in emergencymanagement From a systemperspective KD-ACP provides a reliable flexible low costand effective platform for the scientists to do the research onemergency response problems The analysis and predictionof these problems with inherent complexity can be solvedby the repetition of possible alternative experiments onthe software framework Therefore KD-ACP which is anattempt to implement ACP approach seemed as an effectivecomputational framework to support the decision making inemergency response

Currently KD-ACP is used to study the H1N1 epidemicin Beijing in 2009 The most optimal compositions of inter-vention measures are testified to support the response tonext influenza However it is still a long way ahead beforethe automation systematization and practicalization of KD-ACP A lot of work will be carried out along several directionsas in the following

(i) The parallel execution process in KD-ACP is actuallynot strongly connected with the emergency responseorganization Traditional experience and theory stillplay a dominant role in the decision of emergencymanagement It is necessary to propose a mechanismof hall for workshop to facilitate the parallel executionloop in actual society

(ii) Section 33 introduces the principle and process ofmodelling artificial society Metamodels and modelsof artificial Beijing are built by GME Model transfor-mation and code generation are used to implementcodes from FSM based models But many details


Fangshan district Daxing district Fengtai district Xicheng district

Mentougou district Haidian districtShijingshan district Dongcheng district

Incubation

Symptomatic

Convalescence

Health withoutvaccination

Health withvaccinationLegend of agent

health status

Figure 27 The situation of artificial Beijing by ASST

H1N1

Districts of Beijing

H1N1 case ofdistribution

Incidence map

Distribution ofH1N1 reportsfrom internet

Incidence of H1N1

Fangshan districtLiangxiang town

Population 19000Incidence 135789

(1100000)

Figure 28 The implementation of OsdRT by Institute of Automation from Chinese Academy of Sciences

are still not illustrated due to the topic of thispaper The key techniques such as template of codeframework and model transformations from specificdomains to FSM will be focused and introduced indetail in our next paper

(iii) The performance of computational experiments inKD-ACP is still slower than expectation The opti-mization of simulation engine and models structureis necessary to improve the performance So it ispossible to run artificial society with the real worldin parallel

In summary KD-ACP paves a new way for scientists inboth emergencymanagement and computation simulation to

collaborate with each other in solving the emergency man-agement problems by social computing

Research Highlights

(i) KD-ACP is implemented based on ACP approach

(ii) Artificial Beijing is built with the help of KD-ACP

(iii) H1N1 is modeled to simulate the influenza in Beijingin 2009

(iv) Computational experiments testify the effectivenessof intervention measures


Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work was supported in part by the National ScienceFoundation of China (nos 91024030 71303252 61403402and 91324013)

References

[1] W Mao and F-Y Wang Advances in Intelligence and SecurityInformatics Academic Press Oxford UK 2012

[2] E Bonabeau ldquoAgent-based modeling methods and techniquesfor simulating human systemsrdquo Proceedings of the NationalAcademy of Sciences of the United States of America vol 99 no3 pp 7280ndash7287 2002

[3] K M Carley D B Fridsma E Casman et al ldquoBioWar scalableagent-basedmodel of bioattacksrdquo IEEETransactions on SystemsMan and Cybernetics A Systems and Humans vol 36 no 2 pp252ndash265 2006

[4] J M Epstein ldquoModelling to contain pandemicsrdquo Nature vol460 no 7256 p 687 2009

[5] Los Alamos National Laboratory Reports about EPISIMS LosAlamos National Laboratory 2003

[6] C H Builder and S C Bankes ldquoArtificial societies a conceptfor basic research on the societal impacts of informationtechnologyrdquo RAND Report 149 RAND Corp 1991

[7] F-Y Wang ldquoComputational theory and method on complexsystemrdquo China Basic Science vol 6 no 5 pp 3ndash10 2004

[8] F-Y Wang ldquoToward a paradigm shift in social computing theACP approachrdquo IEEE Intelligent Systems vol 22 no 5 pp 65ndash67 2007

[9] F-Y Wang and S Tang ldquoArtificial societies for integratedand sustainable development of metropolitan systemsrdquo IEEEIntelligent Systems vol 19 no 4 pp 82ndash87 2004

[10] D Wen Y Yuan and X-R Li ldquoArtificial societies computa-tional experiments and parallel systems an investigation ona computational theory for complex socioeconomic systemsrdquoIEEE Transactions on Services Computing vol 6 no 2 pp 177ndash185 2013

[11] F Wang F Zeng and Y Yuan ldquoAn ACP-based approach forcomplexity analysis of E-commerce systemrdquo Complex Systemsand Complexity Science vol 5 no 3 pp 1ndash8 2008 (Chinese)

[12] F-Y Wang and P K Wong ldquoResearch commentary intelligentsystems and technology for integrative and predictivemedicinean ACP approachrdquoACMTransactions on Intelligent Systems andTechnology vol 4 no 2 pp 1ndash6 2013

[13] J Sifeng X Gang F Dong and H Chunpeng ldquoStudy on theemergency rescue decision support system of petrochemicalplant based on ACP theoryrdquo in Proceedings of the 6th Manage-ment Annual Meeting (MAM rsquo11) 2011 (Chinese)

[14] X Dong D Fan G Xiong F Zhu Z Zhang and Y Yao ldquoPar-allel Bus Rapid Transit (BRT) operation management systembased on ACP approachrdquo in Proceedings of the 6th ManagementAnnual Meeting (MAM rsquo11) Beijing China 2011

[15] B Ning F-Y Wang H-R Dong R-M Li D Wen and L LildquoParallel systems for Urban rail transportation based on ACP

approachrdquo Journal of Transportation Systems Engineering andInformation Technology vol 10 no 6 pp 23ndash28 2010 (Chinese)

[16] F-Y Wang J Zhao and S-X Lun ldquoArtificial power systemsfor the operation and management of complex power gridsrdquoSouthern Power System Technology vol 2 no 3 pp 1ndash11 2008(Chinese)

[17] M J North T R Howe N T Collier and J R Vos ldquoRepastsimphony development environmentrdquo in Proceedings of theAgent 2005 Conference on Generative Social Processes Modelsand Mechanisms 2005

[18] M J North T RHowe N T Collier and J R Vos ldquoRepast Sim-phony runtime systemrdquo in Proceedings of the Agent Conferenceon Generative Social Processes Models and Mechanisms 2005

[19] SWARM Development Group Swarm 211 Reference Guide2000 httpwwwswarmorg

[20] S Tisue and U Wilensky ldquoNetLogo a simple environment formodeling complexityrdquo in International Conference on ComplexSystems pp 16ndash21 2004

[21] S Luke C Cioffi-Revilla L Panait K Sullivan and G BalanldquoMASON a multiagent simulation environmentrdquo Simulationvol 81 no 7 pp 517ndash527 2005

[22] M Ichikawa H Tanuma Y Koyama andH Deguchi ldquoSOARSintroduction as a social microscope for simulations of socialinteractions and gamingrdquo in Organizing and Learning throughGaming and Simulation Proceedings of the 38th Conference ofthe International Simulation and Gaming Association (ISAGArsquo07) pp 149ndash158 2007

[23] D S Burke J M Epstein D A T Cummings et al ldquoIndividual-based computational modeling of smallpox epidemic controlstrategiesrdquo Academic Emergency Medicine vol 13 no 11 pp1142ndash1149 2006

[24] F-Y Wang ldquoParallel control and management for intelligenttransportation systems concepts architectures and applica-tionsrdquo IEEE Transactions on Intelligent Transportation Systemsvol 11 no 3 pp 630ndash638 2010

[25] Z Molnar D Balasubramanian and A Ledeczi ldquoAn introduc-tion to the genericmodeling environmentrdquo in Proceedings of theTOOLS Europe 2007 Workshop on Model-Driven DevelopmentTool Implementers Forum Zurich Switzerland 2007

[26] S Kelly and J-P Tolvanen Domain Framework in Domain-Specific Modeling Enabling Full Code Generation JohnWiley ampSons Hoboken NJ USA 2007

[27] B P Zeigler H Praehofer and T G Kim Theory of Modelingand Simulation Integrating Discrete Event and ContinuousComplex Dynamic Systems Academic Press San Diego CalifUSA 2000

[28] G Booch J Rumbaugh and I JacobsonThe Unified ModelingLanguage User Guide Addison-Wesley Reading Mass USA1999

[29] F Wagner R Schmuki T Wagne and P Wolstenholme Mod-eling Software with Finite State Machines A Practical ApproachCRC amp Taylor amp Francis Boca Raton Fla USA 2006

[30] T Espiner ldquoChina builds worldrsquos fastest supercomputerrdquo ZDNetUK October 2010

[31] B Chen and G Guo ldquoA two-tier parallel architecture for arti-ficial society simulationrdquo in Proceedings of the ACMIEEESCS26th Workshop on Principles of Advanced and DistributedSimulation (PADS rsquo12) pp 184ndash186 July 2012

[32] G Guo B Chen X G Qiu and Z Li ldquoParallel simulation oflarge-scale artificial society on CPUGPU mixed architecturerdquo


in Proceedings of the ACMIEEESCS 26th Workshop on Prin-ciples of Advanced and Distributed Simulation (PADS rsquo12) pp174ndash177 July 2012

[33] S-X Lun ldquoResearch on the classification of parallel executionmodes of ACP theoryrdquo Acta Automatica Sinica vol 38 no 10pp 1602ndash1608 2012

[34] F-YWang ldquoA framework for social signal processing and anal-ysis from social sensing networks to computational dialecticalanalyticsrdquo Science China vol 43 no 12 pp 1598ndash1611 2013

[35] G GuoOne Model Userrsquos Manual version 12 National Univer-sity of Defense University 2011

[36] Y Ge R Meng Z Cao X Qiu and K Huang ldquoVirtual citymdashanindividual-based digital environment for human mobility andinteractive behaviorrdquo Simulation Transactions of the Society forthe Modeling and Simulation International 2014

[37] M Kretzschmar and R T Mikolajczyk ldquoContact profiles ineight European countries and implications for modelling thespread of airborne infectious diseasesrdquo PLoS ONE vol 4 no6 Article ID e5931 2009

[38] W Duan and X Qiu ldquoFostering artificial societies using sociallearning and social control in parallel emergency managementsystemsrdquo Frontiers in Computer Science vol 6 no 5 pp 604ndash610 2012

[39] W J Edmunds C J OrsquoCallaghan and D J Nokes ldquoWhomixes with whom Amethod to determine the contact patternsof adults that may lead to the spread of airborne infectionsrdquoProceedings of the Royal Society B Biological Sciences vol 264no 1384 pp 949ndash957 1997

[40] J Banks J S Carson B L Neison and D M Nicol Discrete-Event System Simulation Prentice Hall Englewood Cliffs NJUSA 4th edition 2007

[41] H Yongxia ldquoInvestigation and analysis of college studentsrsquo workand restrdquo Value Engineering vol 31 no 3 2012 (Chinese)

[42] W Duan Z Cao Y Wang et al ldquoAn ACP approach to publichealth emergency management using a campus outbreak ofh1n1 influenza as a case studyrdquo IEEE Transactions on SystemsMan and Cybernetics Part A Systems and Humans vol 43 no5 pp 1028ndash1041 2013

[43] HWHethcote ldquoThemathematics of infectious diseasesrdquo SIAMReview vol 42 no 4 pp 599ndash653 2000

[44] G Chowell E Shim F Brauer and et al ldquoModelling thetransmission dynamics of acute haemorrhagic conjunctivitisapplication to the 2003 outbreak in Mexicordquo Statistics inMedicine vol 25 no 11 pp 1840ndash1857 2006

[45] Y Ge L Liu B Chen X Qiu and K Huang ldquoAgent-basedmodeling for InfluenzaH1N1 in an artificial classroomrdquo SystemsEngineering Procedia vol 2 pp 94ndash104 2011

[46] A R Tuite A L Greer MWhelan et al ldquoEstimated epidemio-logic parameters andmorbidity associatedwith pandemicH1N1influenzardquo CMAJ vol 182 no 2 pp 131ndash136 2010

[47] L Brouwers B Cakici M Camitz A Tegnell and MBoman ldquoEconomic consequences to society of pandemic H1N1influenza 2009mdashpreliminary results for Swedenrdquo Euro Surveil-lance vol 14 no 37 pp 1ndash7 2009

[48] Y Ge L Liu X Qiu H Song Y Wang and K Huang ldquoAframework of multilayer social networks for communicationbehavior with agent-based modelingrdquo Simulation Transactionsof the Society for the Modeling and Simulation International vol89 no 7 pp 810ndash828 2013

[49] W Duan X Qiu Z Cao X Zheng and K Cui ldquoHeterogeneousand stochastic agent-based models for analyzing infectious

diseasesrsquo super spreadersrdquo IEEE Intelligent Systems vol 28 no4 pp 18ndash25 2013

[50] The Preparation and Emergency Response Plans to PandemicPublished by Ministry of Health Ministry of Health 2005(Chinese)

[51] R M Fujimoto Parallel and Distributed Simulation SystemsWiley Interscience 2000

[52] F Cicirelli A Giordano and L Nigro ldquoDistributed simulationof situated multi-agent systemsrdquo in Proceedings of the 15thIEEEACM International Symposium on Distributed Simulationand Real Time Applications (DS-RT rsquo11) pp 28ndash35 September2011

[53] B Chen and G Guo ldquoA two-tier parallel architecturefor artificial society simulationrdquo in Proceedings of the 26thACMIEEESCS Workshop on Principles of Advanced and Dis-tributed Simulation (PADS rsquo12) pp 184ndash186 July 2012

[54] G Guo B Chen and X Qiu ldquoParallel simulation of large-scale artificial society with GPU as coprocessorrdquo InternationalJournal of Modeling Simulation and Scientific Computing vol4 no 2 Article ID 1350005 2013

[55] B Chen L-B Zhang X-C Liu and H Vangheluwe ldquoActivity-based simulation using DEVS increasing performance by anactivity model in parallel DEVS simulationrdquo Journal of ZhejiangUniversity Science C vol 15 no 1 pp 13ndash30 2014

[56] X-L Wang P Yang Z-D Cao et al ldquoQuantitative evaluationon the effectiveness of prevention and control measures againstpandemic influenza A (H1N1) in Beijing 2009rdquoChinese Journalof Epidemiology vol 31 no 12 pp 1374ndash1378 2010 (Chinese)

[57] S Mei A D van de Vijver L Xuan Y Zhu and P M ASloot ldquoQuantitatively evaluating interventions in the influenzaA (H1N1) epidemic on China campus grounded on individual-based simulationsrdquo Procedia Computer Science vol 1 no 1 pp1675ndash1682 2010

[58] X Qiu Z Cao and Z Li Mid-Term Reports of China NationalScience Foundation 91024030 2014

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of


Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of


Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of


Mathematical PhysicsAdvances in

Complex AnalysisJournal of


OptimizationJournal of


CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of


Operations ResearchAdvances in

Journal of


Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Algebra

Discrete Dynamics in Nature and Society



Decision SciencesAdvances in

Discrete MathematicsJournal of


Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


a novel approach in social computing to solve the problemsin society domain ACP approach is categorized into threeaspects representing and modelling society with artificialsystems analysis and evaluation by computational experi-ments and control andmanagement of real society by parallelexecution Under the instruction of conceptual framework ofACP approach a wide spectrum of complex systems such astransportation medicine finance and environment can bestudied in the computational manner Actually many real-world applications using ACP approach have been developedto solve the real problems in domains For instance complexsocioeconomic system [10] and the research framework fore-commerce system [11] are the good applications of ACPapproach in the economic area The ACP-based frame-work for integrative medicine [12] is proposed to solve theproblems in medicine An overall framework of emergencyrescue decision support system of petrochemical plant [13]is proposed based on ACP theory to study environmentrisk accidents of petrochemical plant Parallel BRT operationmanagement system [14] based on ACP approach has beenconstructed to detect the quantity of passengers on stationsreal-time traffic flow on stations or at intersections andqueuing length of vehicles on the road A novel parallel sys-tem for Urban Rail Transportation (URT) [15] based on ACPapproach is proposed to address issues on safety efficiencyand reliability of the operation of URT An artificial powersystem [16] is set up on the models of power systems andcomplex power grids to provide a feasible approach for thecontrol and management of the modern power system

Although a lot of work has been done on the concepts andtheory framework to study problems by social computing thefollowing problems of computational experiments in emer-gencymanagement are still not solved from the perspective ofmodelling for social systems theory and software frameworkof platform implementation

(i) The modelling and simulation of emergency man-agement are not given the special considerationThe representation of society focuses on the genericmodelling of agent (represented by Repast [17 18])The description of environments is too simple tomeet the requirements from research on emergencyproblems such as the building size the place relatedagent contact frequency

(ii) The existing tools and platforms cannot support thedesign of experiment Computational experimentscannot be done systematically and automatically

(iii) The existing applications of ACP-based frameworksare still domain specific A generic workflow andintegrated toolkit are needed to implement ACPapproach especially in the application of emergencymanagement

Therefore it is necessary to develop an ACP-based softwareframework for the research on emergency management Theartificial system is the projection of real world in the emergentscenarioThemodelling of the system including the emergentevents modelling and intervention measures modelling thedesign of computational experiments considering the settings

of emergency parameters the settings of large samples exper-iments and the parallel execution with loose connection ofreal society should be considered inside the software frame-work

As a result the purpose of this paper is to proposea software framework called KD-ACP applying the ACP-based computational theory and corresponding methods instudying emergency management problems KD is short forthe Chinese phonetic alphabets of China National Univer-sity of Defense Technology KD-ACP means the softwareframework is developed by National University of DefenseTechnology to implement ACP approach The remainder ofthis paper is organized as follows Section 2 summarizes theexisting agent-based modelling and simulation platformsSection 3 introduces ACP approach first and proposes theKD-ACP platform Section 4 illustrates the modelling ofBeijing city with KD-ACP both the agent models and initialdata are considered Section 5 shows how to do experimentswith KD-ACP using the H1N1 case study in artificial BeijingIn the end the paper is concluded in Section 6

2 Related Works

There have beenmany efforts on social computing especiallyon the emergency management Agent-based modelling andsimulation are popular in the implementation of socialcomputation The related works are mainly categorized intoSWARM-like agent-based modelling and simulation plat-forms and agent-based platforms for emergency manage-ment

21 SWARM-Like Agent-Based Modelling and SimulationPlatforms SWARM [19] originally proposed by Santa Feinstitute is widely used in many research areas such asbiology ecology and society The tool provides a simulationenvironment for simulating agent with the support of a seriesof class libraries It is worth noting that SWARM is the pre-cursor ofmultiagent simulation tool it influences lots ofmul-tiagent simulation platforms such as NetLogo [20] RePast(REursive Porus Agent Simulation Toolkit) MASON [21]and SOARS (Spot Oriented Agent Role Simulator) [22]

NetLogo is a multiagent programming language andmodelling environment for simulating natural and socialphenomena It is particularly well suited for modelling com-plex systems evolving over timeThe language is easy to studyand the agent-based complex systems could be built rapidlyRePast is a software framework for agent-based simulationcreated at theUniversity of Chicago An extensible simulationpackage makes RePast become a generic multiagent simula-tion platform in social science research computing MASONdesigned by George Mason University is used to serve as thebasis for a wide range of multiagent simulation tasks rangingfrom swarm robotics to machine learning to social complex-ity environments The tool is a fast discrete-event multiagentsimulation toolkit in Java SOARS is designed by TokyoInstitute of Technology to describe agent activities under theroles of social and organizational structureDecomposition ofmultiagent interaction is the most important characteristicsin this framework






3 KD-ACP

















A part

C part

P part




Initialize



FSM based models






Experiment plans








Actu

al so

ciet



Internet






















MDT EECT IMCT

ASST OsdRT

ASE PGET EDT EMT






















C ASPNET



C++ Visual Studio



C++ Visual Studio







M2T transformation

Modeling in domain


Metamodeling

Instance of

FSM


DEVS


Agentmetamodel




DAE


M2M transformation


Interventionmodel


Environmentmodel

Agentmodel





M2M transformation




Disease model code






Roadnet model

Vaccination

Infectors isolation





Implementation


















(ASE)

Agent behavior set





societyframework



metamodeling



Mod

el D

evelo

pmen

t Too

l (M

DT)

Agent modeling


Encapsulate

Behavior model

Intervention

Emergency

Response

Daily



Socialbehaviormodel

Emergencybehavior

model


Building



Actualsociety


geospatial database

Organizationservice



repository

Climateservice

Geographicalservice

+


Emergent event set







Tool (GSeT)


C++C++C++C++

Modeling



Organizationmodel

Transportationmodel

Geographicalmodel

Climatemodel

Interventionservice

Publ

ic se

rvic

e sta

ndar

d










repository




Load

Output Output

Internet


Load



+ emergency model







Actual society

OutputOutputOutput

Input

Load

Load

Load

Load

Output




Deploy

Deploy



Experimentplan





Load


















Data acquisition

Actual society


Internet


Data extraction



Knowledge














Agents

Artificial Beijing


Agent activity





Services



Intervention models


Controllermodels






2while the








Q

q0


F

Σ


Q

q0

120575 F

Current agent list


Next agents list


Agents entered




Σ





0(100) 360










5(063)




7(045)










Healthy and immune

No

Choose schedule


Staysymptomatic

No

Receive treatment


Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes



119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)


119865



2are the



119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)


120583119879



1and 120574

2



120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)


119879and 120590







Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra














3 KD-ACP

















A part

C part

P part




Initialize



FSM based models






Experiment plans








Actu

al so

ciet



Internet






















MDT EECT IMCT

ASST OsdRT

ASE PGET EDT EMT






















C ASPNET



C++ Visual Studio



C++ Visual Studio







M2T transformation

Modeling in domain


Metamodeling

Instance of

FSM


DEVS


Agentmetamodel




DAE


M2M transformation


Interventionmodel


Environmentmodel

Agentmodel





M2M transformation




Disease model code






Roadnet model

Vaccination

Infectors isolation





Implementation


















(ASE)

Agent behavior set





societyframework



metamodeling



Mod

el D

evelo

pmen

t Too

l (M

DT)

Agent modeling


Encapsulate

Behavior model

Intervention

Emergency

Response

Daily



Socialbehaviormodel

Emergencybehavior

model


Building



Actualsociety


geospatial database

Organizationservice



repository

Climateservice

Geographicalservice

+


Emergent event set







Tool (GSeT)


C++C++C++C++

Modeling



Organizationmodel

Transportationmodel

Geographicalmodel

Climatemodel

Interventionservice

Publ

ic se

rvic

e sta

ndar

d










repository




Load

Output Output

Internet


Load



+ emergency model







Actual society

OutputOutputOutput

Input

Load

Load

Load

Load

Output




Deploy

Deploy



Experimentplan





Load


















Data acquisition

Actual society


Internet


Data extraction



Knowledge














Agents

Artificial Beijing


Agent activity





Services



Intervention models


Controllermodels






2while the








Q

q0


F

Σ


Q

q0

120575 F

Current agent list


Next agents list


Agents entered




Σ





0(100) 360










5(063)




7(045)










Healthy and immune

No

Choose schedule


Staysymptomatic

No

Receive treatment


Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes



119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)


119865



2are the



119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)


120583119879



1and 120574

2



120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)


119879and 120590







Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










A part

C part

P part




Initialize



FSM based models






Experiment plans








Actu

al so

ciet



Internet






















MDT EECT IMCT

ASST OsdRT

ASE PGET EDT EMT






















C ASPNET



C++ Visual Studio



C++ Visual Studio







M2T transformation

Modeling in domain


Metamodeling

Instance of

FSM


DEVS


Agentmetamodel




DAE


M2M transformation


Interventionmodel


Environmentmodel

Agentmodel





M2M transformation




Disease model code






Roadnet model

Vaccination

Infectors isolation





Implementation


















(ASE)

Agent behavior set





societyframework



metamodeling



Mod

el D

evelo

pmen

t Too

l (M

DT)

Agent modeling


Encapsulate

Behavior model

Intervention

Emergency

Response

Daily



Socialbehaviormodel

Emergencybehavior

model


Building



Actualsociety


geospatial database

Organizationservice



repository

Climateservice

Geographicalservice

+


Emergent event set







Tool (GSeT)


C++C++C++C++

Modeling



Organizationmodel

Transportationmodel

Geographicalmodel

Climatemodel

Interventionservice

Publ

ic se

rvic

e sta

ndar

d










repository




Load

Output Output

Internet


Load



+ emergency model







Actual society

OutputOutputOutput

Input

Load

Load

Load

Load

Output




Deploy

Deploy



Experimentplan





Load


















Data acquisition

Actual society


Internet


Data extraction



Knowledge














Agents

Artificial Beijing


Agent activity





Services



Intervention models


Controllermodels






2while the








Q

q0


F

Σ


Q

q0

120575 F

Current agent list


Next agents list


Agents entered




Σ





0(100) 360










5(063)




7(045)










Healthy and immune

No

Choose schedule


Staysymptomatic

No

Receive treatment


Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes



119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)


119865



2are the



119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)


120583119879



1and 120574

2



120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)


119879and 120590







Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





















MDT EECT IMCT

ASST OsdRT

ASE PGET EDT EMT






















C ASPNET



C++ Visual Studio



C++ Visual Studio







M2T transformation

Modeling in domain


Metamodeling

Instance of

FSM


DEVS


Agentmetamodel




DAE


M2M transformation


Interventionmodel


Environmentmodel

Agentmodel





M2M transformation




Disease model code






Roadnet model

Vaccination

Infectors isolation





Implementation


















(ASE)

Agent behavior set





societyframework



metamodeling



Mod

el D

evelo

pmen

t Too

l (M

DT)

Agent modeling


Encapsulate

Behavior model

Intervention

Emergency

Response

Daily



Socialbehaviormodel

Emergencybehavior

model


Building



Actualsociety


geospatial database

Organizationservice



repository

Climateservice

Geographicalservice

+


Emergent event set







Tool (GSeT)


C++C++C++C++

Modeling



Organizationmodel

Transportationmodel

Geographicalmodel

Climatemodel

Interventionservice

Publ

ic se

rvic

e sta

ndar

d










repository




Load

Output Output

Internet


Load



+ emergency model







Actual society

OutputOutputOutput

Input

Load

Load

Load

Load

Output




Deploy

Deploy



Experimentplan





Load


















Data acquisition

Actual society


Internet


Data extraction



Knowledge














Agents

Artificial Beijing


Agent activity





Services



Intervention models


Controllermodels






2while the








Q

q0


F

Σ


Q

q0

120575 F

Current agent list


Next agents list


Agents entered




Σ





0(100) 360










5(063)




7(045)










Healthy and immune

No

Choose schedule


Staysymptomatic

No

Receive treatment


Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes



119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)


119865



2are the



119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)


120583119879



1and 120574

2



120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)


119879and 120590







Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










M2T transformation

Modeling in domain


Metamodeling

Instance of

FSM


DEVS


Agentmetamodel




DAE


M2M transformation


Interventionmodel


Environmentmodel

Agentmodel





M2M transformation




Disease model code






Roadnet model

Vaccination

Infectors isolation





Implementation


















(ASE)

Agent behavior set





societyframework



metamodeling



Mod

el D

evelo

pmen

t Too

l (M

DT)

Agent modeling


Encapsulate

Behavior model

Intervention

Emergency

Response

Daily



Socialbehaviormodel

Emergencybehavior

model


Building



Actualsociety


geospatial database

Organizationservice



repository

Climateservice

Geographicalservice

+


Emergent event set







Tool (GSeT)


C++C++C++C++

Modeling



Organizationmodel

Transportationmodel

Geographicalmodel

Climatemodel

Interventionservice

Publ

ic se

rvic

e sta

ndar

d










repository




Load

Output Output

Internet


Load



+ emergency model







Actual society

OutputOutputOutput

Input

Load

Load

Load

Load

Output




Deploy

Deploy



Experimentplan





Load


















Data acquisition

Actual society


Internet


Data extraction



Knowledge














Agents

Artificial Beijing


Agent activity





Services



Intervention models


Controllermodels






2while the








Q

q0


F

Σ


Q

q0

120575 F

Current agent list


Next agents list


Agents entered




Σ





0(100) 360










5(063)




7(045)










Healthy and immune

No

Choose schedule


Staysymptomatic

No

Receive treatment


Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes



119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)


119865



2are the



119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)


120583119879



1and 120574

2



120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)


119879and 120590







Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





















(ASE)

Agent behavior set





societyframework



metamodeling



Mod

el D

evelo

pmen

t Too

l (M

DT)

Agent modeling


Encapsulate

Behavior model

Intervention

Emergency

Response

Daily



Socialbehaviormodel

Emergencybehavior

model


Building



Actualsociety


geospatial database

Organizationservice



repository

Climateservice

Geographicalservice

+


Emergent event set







Tool (GSeT)


C++C++C++C++

Modeling



Organizationmodel

Transportationmodel

Geographicalmodel

Climatemodel

Interventionservice

Publ

ic se

rvic

e sta

ndar

d










repository




Load

Output Output

Internet


Load



+ emergency model







Actual society

OutputOutputOutput

Input

Load

Load

Load

Load

Output




Deploy

Deploy



Experimentplan





Load


















Data acquisition

Actual society


Internet


Data extraction



Knowledge














Agents

Artificial Beijing


Agent activity





Services



Intervention models


Controllermodels






2while the








Q

q0


F

Σ


Q

q0

120575 F

Current agent list


Next agents list


Agents entered




Σ





0(100) 360










5(063)




7(045)










Healthy and immune

No

Choose schedule


Staysymptomatic

No

Receive treatment


Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes



119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)


119865



2are the



119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)


120583119879



1and 120574

2



120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)


119879and 120590







Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra













(ASE)

Agent behavior set





societyframework



metamodeling



Mod

el D

evelo

pmen

t Too

l (M

DT)

Agent modeling


Encapsulate

Behavior model

Intervention

Emergency

Response

Daily



Socialbehaviormodel

Emergencybehavior

model


Building



Actualsociety


geospatial database

Organizationservice



repository

Climateservice

Geographicalservice

+


Emergent event set







Tool (GSeT)


C++C++C++C++

Modeling



Organizationmodel

Transportationmodel

Geographicalmodel

Climatemodel

Interventionservice

Publ

ic se

rvic

e sta

ndar

d










repository




Load

Output Output

Internet


Load



+ emergency model







Actual society

OutputOutputOutput

Input

Load

Load

Load

Load

Output




Deploy

Deploy



Experimentplan





Load


















Data acquisition

Actual society


Internet


Data extraction



Knowledge














Agents

Artificial Beijing


Agent activity





Services



Intervention models


Controllermodels






2while the








Q

q0


F

Σ


Q

q0

120575 F

Current agent list


Next agents list


Agents entered




Σ





0(100) 360










5(063)




7(045)










Healthy and immune

No

Choose schedule


Staysymptomatic

No

Receive treatment


Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes



119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)


119865



2are the



119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)


120583119879



1and 120574

2



120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)


119879and 120590







Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra











repository




Load

Output Output

Internet


Load



+ emergency model







Actual society

OutputOutputOutput

Input

Load

Load

Load

Load

Output




Deploy

Deploy



Experimentplan





Load


















Data acquisition

Actual society


Internet


Data extraction



Knowledge














Agents

Artificial Beijing


Agent activity





Services



Intervention models


Controllermodels






2while the








Q

q0


F

Σ


Q

q0

120575 F

Current agent list


Next agents list


Agents entered




Σ





0(100) 360










5(063)




7(045)










Healthy and immune

No

Choose schedule


Staysymptomatic

No

Receive treatment


Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes



119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)


119865



2are the



119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)


120583119879



1and 120574

2



120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)


119879and 120590







Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra

















Data acquisition

Actual society


Internet


Data extraction



Knowledge














Agents

Artificial Beijing


Agent activity





Services



Intervention models


Controllermodels






2while the








Q

q0


F

Σ


Q

q0

120575 F

Current agent list


Next agents list


Agents entered




Σ





0(100) 360










5(063)




7(045)










Healthy and immune

No

Choose schedule


Staysymptomatic

No

Receive treatment


Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes



119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)


119865



2are the



119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)


120583119879



1and 120574

2



120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)


119879and 120590







Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra











Agents

Artificial Beijing


Agent activity





Services



Intervention models


Controllermodels






2while the








Q

q0


F

Σ


Q

q0

120575 F

Current agent list


Next agents list


Agents entered




Σ





0(100) 360










5(063)




7(045)










Healthy and immune

No

Choose schedule


Staysymptomatic

No

Receive treatment


Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes



119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)


119865



2are the



119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)


120583119879



1and 120574

2



120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)


119879and 120590







Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra














Q

q0


F

Σ


Q

q0

120575 F

Current agent list


Next agents list


Agents entered




Σ





0(100) 360










5(063)




7(045)










Healthy and immune

No

Choose schedule


Staysymptomatic

No

Receive treatment


Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes



119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)


119865



2are the



119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)


120583119879



1and 120574

2



120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)


119879and 120590







Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra











Healthy and immune

No

Choose schedule


Staysymptomatic

No

Receive treatment


Symptomatic

No

Self-immunoprocess

Yes

Recovered

Yes

Incubation

Infection

No

Yes No

NoYes



119865119862= 120583119865+ 120590119865(minus2 ln (120574

1))12 cos (2120587120574

2) (1)


119865



2are the



119879119862= 120583119879+ 120590119879(minus2 ln (120574

1))12 cos (2120587120574

2) (2)


120583119879



1and 120574

2



120583119879=119879Location(120583119865+ 120590119865) (3)

120590119879=119879Location(10120583119865+ 10120590

119865) (4)


119879and 120590







Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra












Student-student

Sleep Dormitory 0 0






































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra
































Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra




















Varying parameters



Agent settable


Household settable








Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Censusdata

Generate GUID





social role





individual





region





relation




Edit

Edit

Edit

Edit

Edit

Edit




Population types


Workspaces


Household types

Roles in household

Household structure

Social roles



File path




Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Infected

SusceptibleHealth

withimmunity

Infected

by contac

t

with in

fected

agents

Recovery



Decrease by time

Infectivity

Immunity







Recovered

Tim

e (da

y)

Infe

ctiv

ity














H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra















H1N1influenza


Disease model

Transmissionmodel










Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra













Closeworkspace

Antibiotic

VaccinationHospital

Surveillance























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra























0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










0

Infe

cted

agen

ts

0 50 100 150 200 250


25000

20000

15000

10000

5000

Day d









0


0

Infe

cted

agen

ts




0 50 100 150 200 250

25000

20000

15000

10000

5000

Day d







0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra












0 50 100 150 200 2500

1000

2000

3000

4000

5000

6000

7000

Infe

cted

agen

ts








Day d












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra












50 100 150 200 2500

50

100

150

200

Infe

cted

agen

ts



Day d





6 Conclusion








Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra












Incubation

Symptomatic

Convalescence



health status


H1N1



Incidence map


Incidence of H1N1



(1100000)






Research Highlights








Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra












Acknowledgment


References






































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra













































Volume 2014




Journal of











Journal of


Function Spaces






Algebra
















Volume 2014




Journal of











Journal of


Function Spaces






Algebra









Documents

Research Article KD-ACP: A Software Framework for Social ...downloads.hindawi.com/journals/mpe/2015/915429.pdfMathematicalProblems in Engineering a novel approach in social computing