COMMAND: COllaboration for Multi-Model Analysis of

iNfectious Diseases

An IIT Bombay-MHRD initiative in collaboration with IIT Gandhinagar, ICMR and Visva-Bharati University

COMMAND: Collaboration for Multi-Model Analysis of iNfectious Diseases

Contributing Members (Alphabetical Order): 1. Prof. Aditi Chaubal, IIT Bombay 2. Ms Adrija Roy, IIT Bombay 3. Mr. Adwait Godbole, IIT Bombay4. Prof. Arindom Chakraborty, Visva Bharati University5. Aritra Das, Epidemiology and Outcome Research, Real World

Solutions, IQVIA6. Prof. Avijit Maji, IIT Bombay 7. Prof D Manjunath, IIT Bombay 8. Ashritha K, IIT Bombay9. Prof. Gopal Patil, IIT Bombay 10. Dr Giridhar Babu, Public Health Foundation of India 11. Prof. Haripriya Gundimeda, IIT Bombay 12. Hrushikesh Loya, IIT Bombay13. Prof. Jayendran Venkateswaran, IIT Bombay 14. Prof. Kalyan Das, IIT Bombay 15. Khem Ghusinga, University of North Carolina, Chapel Hill16. Prof. Mithun Mitra, IIT Bombay 17. Noufal Jaseem 18. Mr. Ojasvi Chauhan

19. Prof. Om Damani, IIT Bombay 20. Pradumn Kumar, IIT Bombay21. Prof. Raghu Murtugudde, IIT Bombay (Visiting Facullty), University of

Maryland 22. Dr Rakesh Sarwal, MHRD, Government of India23. Mr. Sahil Shah, IIT Bombay24. Prof. Sai Vinjanampathy, IIT Bombay 25. Mr. Sandeepan Roy, IIT Bombay 26. Prof. Subhankar Karmakar, IIT Bombay 27. Prof. Subimal Ghosh, IIT Bombay28. Sucheta Ravikanti29. Mr. Sumit Chaturvedi30. Dr. Sushma Prusty, IIT Bombay 31. Dr Tarun Bhatnagar, ICMR School of Public Health 32. Mr Tejasvi Chauhan, IIT Bombay 33. Prof. Udit Bhatia, IIT Gandhinagar 34. Prof. Vinish Kathuria, IIT Bombay 35. Ms. Vandana R.V. , Max Planck Institute for Evolutionary Biology, Plon, 36. Dr Vittal H, IIT Bombay

Objectives

• Model development for epidemiological predictions

• To understand the impacts of different interventions on incidence and mortality due to COVID-19

• To simulate incidence and mortality at a granular level (district to state)

• To develop a new real-time epidemiological risk framework and monitor the same considering epidemiological hazard, vulnerability and exposure.

• To assess the economic impacts of interventions (lockdown)

Outline

• Epidemiological Prediction Model

• Real-time Risk Assessment

• Economic Impacts Assessment

• Outcome in Brief

Epidemiological Prediction Model

The COMMAND team has three modelling groups, who have developed the following models:

1. System Dynamics Model

2. Statistical Model

3. X-SEAIPR Model

Planned Interventions to be used by all models1. No Extension of lockdown after 14th Aprila)With increased testing $$b)Continue the same rate of testing2. 50% reduction in contacts everywhere till 15th Maya)With increased testing $$b)Continue the same rate of testing3. Extending the lockdown till 30th Aprila)With increased testing $$b)Continue the same rate of testing4. Extending the lockdown till 30th April and during 1st May to 15th May 50% reduction in contactsa)With increased testing $$b)Continue the same rate of testing5. Extending lockdown till 15th Maya)With increased testing $$b)Continue the same rate of testing

$$ With increased testing (testing and tracing; 80% symptomatic can be identified and isolated within one day of development of symptoms)

Other Interventions (not simulated by all models)

6. Open Work, Restrict Other (Home at 50%, Contacts outside home, school, work at 50%)a. With increased testing (testing and tracing; 80% symptomatic can be identified and isolated within one day of development of symptoms)b. Continue the same rate of testingc. Same as a) but better compliance for Face Mask and Personal Hygiened. Same as c) but with better compliance for Face Mask and Personal Hygiene, testing and tracing reduced to only 60% symptomatic

7. Open Everything Except School. (School Remains closed for 90 days only, as in 6)a. With increased testing (testing and tracing; 80% symptomatic can be identified and isolated within one day of development of symptoms)b. Continue the same rate of testingc. Same as a) but better compliance for Face Mask and Personal Hygiened. Same as c) but with better compliance for Face Mask and Personal Hygiene, testing and tracing reduced to only 60% symptomatic

Effectiveness of Testing, Tracing, Social Distancing and Hygiene in Tackling Covid-19

in India: A System Dynamics Model

Jayendran Venkateswaran1 and Om Damani2

1 Industrial Engineering and Operations Research,

2 Department of Computer Science, and associated with Center for Policy Studies,

IIT Bombay

18th April 2020

Acknowledgements: We acknowledge the support and contributions of Pooja Prasad, Shreenivas Kunte, Vanessa Beddoe, Rishav Deval and Priyesh Gupta in data collection from various sources and preliminary analysis.

Model Objective

• To gauge the relative impacts of various interventions in managing Covid-19 pandemic in India

• If some intervention is many times more effective than another then that will be useful to know even if absolute numbers given by the models do not play out.

Approach

• A mathematical model of Covid-19 pandemic in India, based on System Dynamics (SD) methodology is presented.

• The detailed age-structured compartment-based model endogenously captures various disease transmission pathways, expanding significantly from the standard SEIR model.

• Model calibrated based on India data, and state-wise data.

• Interventions modeled and simulated

High level stock flow diagram of proposed model

Susceptibles (S) Exposed (E)Asymptomatic

infectives (A)

InfectiousSymptomatics

(I)

Hospitalised

patients (H)Critical

patients (C)

Recovered (R)

Dead (D)

Infection Rate (IR) Infection Setting

Rate (ISR)Incubation

Rate (InR)Disease progress

rate (DPR)Worsening rate (WR)

Critical care

Recoveries (CRR)

Dying (DR)

Asyms Recovering

(ARR) Syms Recovering

(SRR) Hosp Recovering

(HRR)Infectivity

base infectivity

Hygiene and mask

usage multipler v(t)

Adjustment factor (y)

Infectious contacts

per susceptible

Contact Rate per

agegroup C ij

Intervention effect on

contact rate, u(t)s

Total Population (N)

Quarantined

Asymptomatics

(Q^A)

IsolatedAsymptomatics

(L^A)

QuarantinedSymptomatics

(Q^I)

IsolatedSymptomatics

(L^I)

Quarantining

Asymtomatics

(QAR)Isolating

Asymtomatics (LAR) Quarantining

Symtomatics (QSR)Isolating

Symptomatics (LSR)

Quarantinee

Incubation Rate

(QInR)

Isolatees Incubation

Rate (LInR)

Q Disease progress

rate (QDPR)

Isolated Disease

progress rate (LDPR)

Recovered Q and I

Q Asym Recoveries

(QARR)

Q Sym Recoveries

(QSRR)Isolated Asym

Recoveries (LARR)

Isolated Sym

Recoveries (LSRR)

fraction symptomatics

tested (f4)

awareness efforts

fraction (f3)

awareness effort

fraction (f1)

contact tracing

fraction (f5)

fractionasymptomatics tested

(f2)

External arrivals of

Asymptomatics infected

(XAR)

Base contact rate per

agegroup at [Home, Work,

School, Other]

fraction developing

symptoms (fs)fraction becoming

serious (fh)

fraction becoming

critical (fc)

fraction

dying

<Population from

various compartments>

Intervention points are shown in Red.The ‘flow’ of patients is from left to right across the compartments, capturing the spread of Covid-19

Key Assumptions of the model• Population is homogeneous and well mixed; interacts at various locations (at home, at

work, at school and at other locations)

• Interactions are age-dependent. Progression of the disease is age-dependent.

• Patients exposed to Covid-19 show symptoms after an incubation period of ~ 5 days; and infective only ~48 hours prior to onset of symptoms.

• Covid assumed to be imported in to India through arriving passengers only. No further imported cases occurs after April 10th.

• Lockdown reduces interactions by 80% at work and other zone by 70%; and 100% at school

• In ‘All-India’ model, the interaction among states is assumed to be implicit.

• State level models are considered independent of each other, i.e., no interactions among states is assumed.

• Infectivity: It represents the probability of an individual contracting the disease upon interaction with an infected person.

• Contact Rate: It represents the average number of persons a person interacts with in a day.

Other Assumptions

• A conscious decision taken by the modelers to not fine-tune the parameter values to exactly replicate the reported cases of Covid-19 in India, or to minimise some statistical measure of error. Instead it was decided to replicate the trend in behaviour by estimating a few parameters only.

• Data from www.covid19india.org used for model calibration• Models calibrated based on the daily cases reported only and not for deaths.

• Some key parameter values:• Base infectivity = 0.015

• Reduction in infectiousness of asymptomatics =0 5

• Age and zone specific Contact Matrices (from work of Prem et al., 2017)

• The different delays used among stages (from multiple sources)

Calibrated All India model

Calibration parameters• Control measures of indicate

effect of lockdown on contacts.

• Proportion of symptomatic infectious individuals who are isolated after testing.

• Proportion of asymptomatic infectious individuals who are quarantined through contact tracing.

• Time dependent adjustment factor

fitrunSA.vdfx

CoviddataFull.vdfx

50.0% 75.0% 95.0% 100.0%

Sum New Cases Reported[AllIndia]

2000

1500

1000

500

024-Feb 07-Mar 20-Mar 01-Apr 14-Apr

Date

Actual data of Daily Reported Cases shown in Red.Thin blue line is as per calibrated value. Grey bands are the sensitivity confidence bounds

State-wise model separately calibrated on these parameters

Effect of interventions

Policies with low contact tracing: exhibits drastic rate of increase in

new casesPolicies with high contact tracing:

shows lower rate of increase in new

cases

Daily cases reported (All India)

State-wise, Intervention policy 4A (Lockdown till 30th Apr, reduced

contact till May 15th)

Pandemic well managed during interventions

Effect of other interventions

State-wise, Policy 6C (Increased testing and tracing; improved use of facemask/ hygiene, school closed; 50% reduction in all contacts)

State-wise, Policy 7C (Increased testing and tracing; improved use of facemask/ hygiene, school closed; no reduction in other contacts)

Pandemic well managed and under check

Pandemic only shows asymptotic growth

Summary of Findings

• Even after a lock-down, some non-trivial number of infections (asymptomatic) are likely to be left and the pandemic will resurface.

• However Pandemic can be effectively controlled with• High rate of testing of those who show Covid-19 like symptoms, isolating them if they

are positive,

• Contact tracing all contacts of positive patients and quarantining them,

• Improved hygiene, use of face masks, social (physical distancing), improve sanitisation

• Recommendation: During the lockdown, put in appropriate measures in place to do the above

• It will help keep the pandemic in check while we can slowly reopen economic activities.

Statistical Modelby

Kalyan Das and ArindomChakraborty

Plan : To predict the number of infections and mortality till a certain day through a statistical Model

Three models 1. Infection Model2. Mortality Model 3. Model for Reproduction number(secondary infection )

Input Datai) Number of infectionsii) Number of Deathsiii) Infection fatality ratio ( weighted average over the age distribution )—Assumed to be 1.8%

• Average number of secondary infections per infected individual varies from 2-3

• The Reproduction number Rt controls the spread

• Target is to reduce Rt by implementing various interventions.

Estimate of infections and deaths for India when R 0 = 2.60

Left-Daily number of infections, bars are reported infections, bands are predicted infections,Middle- daily number of deaths, bars are reported deaths, bands are predicted deaths, Right-time-varying reproduction number , dark 50% CI, light 95% CI. Icons are interventions shown at the time they occurred.

Indian Scenario Under Lockdown as only intervention

MaharashtraUnder different interventions

Conclusions from the study

• Evident that interventions are right steps for curbing spread

•Strict adherence of Interventions is a must for slowing down the high values of the number of cases and deaths (as predicted ) in the next two months.

• Quantification of 50% reduction in contact and intensive testing is rather hard to incorporate in the model for the reproduction number.

X-SEAIPRA Generalised Compartmental Model for India-Centric

Interventions against COVID-19

Last Updated: 16 April, 2020

Sai Vinjanampathy [1], Mithun K. Mitra [1]

W/ Sanit Gupta [1], Sahil Shah [1], Parvinder Solanki [1], Sumit Chaturvedi [1], Pranav Thakkar[1], Adwait Godbole [1], Pradumn Kumar [1], Sucheta Ravikanti [1], Hrushikesh Loya [1], Noufal Jaseem [1], Khem Ghusinga [2], Vandana R.V. [3]

Aritra Das [4], Giridhara Babu [5] & Tarun Bhatnagar [6]

[1] IIT Bombay, India[2] University of North Carolina Chapel Hill[3] Max Planck Institute for Evolutionary Biology, Plön.[4] Epidemiology and Outcome Research, Real World Solutions, IQVIA[5] Indian Institute of Public Health-Bengaluru, Public Health Foundation of India[6] National Institute of Epidemiology, Indian Council for Medical Research

Overview of the Model

1. Generalises the SEIR-type model to include tested fraction, age stratification & states.

2. Explicitly models lockdown.

3. Incorporates uncertainties and heterogeneity in testing rates for different states.

4. Incorporates district level modelling.

5. Incorporates realistic transport data.

6. Incorporates Bayesian techniques to make predictions with requisite uncertainties.

7. Open-source code for peer review and extension.

Policy Level Questions

[1] Impact of targeted lockdown.

[2] Relative importance of testing vs. lockdown.

[3] Relative importance of distancing vs. lockdown.

[4] Predict developing hotspots for targeted interventions.

Nomenclature Parameters

MODEL DETAILS

States are linked via the “working age population”, as shown in the illustrative

figure above. Used Prem et.al.PLOS Comp. Bio (2017), for age stratified data.

Diagrammatic representation of the model is given below

Important Parameters & Compartments

❏ 𝞫1 : A quantitative measure of the leakiness of lockdown. 𝞫1=0 implies perfect lockdown, while 𝞫1=1 implies a completely leaky lockdown. Can be used to inform policy on lockdown measures.

❏ 𝞳t : Incorporates the effects of testing fraction (ftest) and test turnover time. The testing fraction depends on the true number of infected population vs. the tested number. An estimate of true infected numbers essential to accurate modeling of disease spread.

❏ P : Number of people tested positive. This compartment and mortality can be fit to reported data.

Transportation matrix

1. District level transportation matrix created using worker population ratio estimated using Census India 2011 data and district level GIS map

1. The district level transportation matrix can be used to construct state-level transportation matrix

2. Allows simulation and forecasting at district and state levels for spatially heterogeneous interventions Transportation effects kick in when lockdown is

lifted

Avijit Maji, Sandeepan Roy, M.B. Sushma (IIT-B)

Results - Base scenario (Lockdown till 3rd May)

Red DOTS show reported number of cases for each state

Error bars

are obscured

due to large

average

number on

the y-axis

Results - Base scenario (Lockdown till 3rd May)

Red DOTS show reported number of cases for each state

Preliminary conclusions

1. Increased identification/testing and quarantine offers best strategy to combat spread.

2. States with good testing and health infrastructure (KL, MH) control epidemic effectively in lockdown period.

3. Proper physical distancing measures to reduce contact may be an effective long-term measure to mitigate

spread.

4. Post-lockdown, migration from underperforming neighboring states negatively affect health outcomes even for

states with good testing rates.

Summary Plots from all the ModelsSystem Dynamics Model Statistical Model X-SEAIPR Model

Dai

ly N

ew In

fect

ion

sD

aily

New

Mo

rtal

ity

Dai

ly N

ew M

ort

alit

yD

aily

New

Mo

rtal

ity

Estimating potential passenger trips during and after lockdown

Principal Investigators:

Avijit Maji, IIT Bombay

Udit Bhatia, IIT Gandhinagar

Supported by:

Dr. M.B. Sushma

Mr. Sandeepan Roy

Objectives

• Estimating expected inter-district and inter-state daily work related passenger trips during COVID-19

• Origin and destination of the trips

• Preferred choice of modes for daily work related essential travel

• Effect of various transportation related interventions

Challenges and Assumptions

• Challenges:• Limited data availability on different types of trips• Studies on metropolitan cities are available but cannot be scaled• Capacity and resilience of transportation infrastructure are not known

• Assumptions:• Work related trips: Expected when transportation is resumed

• Service industry: Trips depend on policy of restarting • Self-employed: Expected maximum number of trips• Business: Surge depends on policy of restarting

• Essential service related trip: Some surge but people would combine with work related trip to minimize exposure

• Education related trips: Depends on policy• Recreation or leisure trips: Expected to be almost zero for some period from the end

of lockdown

Data and Analysis Framework

2011 Census data on inter-

state migration

Analyze migration in

less nine years

Identify Origin and Destination

States of migration

Estimate expected

inter-state trips by migrant workers

2011 Census data on district

wise worker trip length

Analyze home to work trip

length frequency

Concentric segmentation

of districts based on trip

length

Estimate expected inter-district and intra-district trips

from each concentric segment

Inter-state trips by migrant workers

Inter-district and intra-district work related trips

Data and Analysis Framework

Trip details for 809 major railway

stations within India

Analyze travel pattern

Establish state and district wise

origin and destination pairs

Correlate with inter-district and inter-state travels estimated from

2011 census data

Estimate expected trips by

train

Inter-state and inter-district trips by train

Key Results

Origin-destination of migrantsInter-district trips by train in Maharashtra

Key Results: Expected Inter-district trip matrix

Observations

• In absence of work, migrant workers may move to their origin states. States such as Maharashtra and Delhi have comparatively higher number incoming migrants and states such as UP and Bihar have comparatively higher number of out going migrants.

• Most North-Eastern States (Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, Tripura) and UTs such as Daman & Diu, Dadra & Nagar Haveli, Lakshadweep, Andaman & Nicobar Islands and Puducherry have comparatively less incoming or outgoing migrants. Hence, these states may not observe high influx of infected COVID-19 patients due to migrant workers.

• Analyses of home to work trip length information as recorded in 2011 census data can provide reasonable estimation of inter-state, intra-state, inter-district and intra-district travel demand, however, nation wide travel demand model needs to be developed for better evaluation of transportation related interventions during contagious pandemic like COVID-19

Real time Monitoring ofEpidemiological Risk

Real-time Epidemiological Risk Assessment (COVID-19)

Adrja Roy, Raghu Murtugudde, Subhankar Karmakar, Subimal Ghosh, Tejasvi Chauhan, Vittal H.

Indian Institute of Technology Bombay

Mumbai, India

Defining RiskIn the present study, epidemiological risk consists of three major components, namely Vulnerability

(V), Hazard (H), and Exposure (E), and quantifies the marginal contributions of all three factors.

• Vulnerability: describes the lack of resistance to harmful influences

• Hazard: gives the probability of occurrence of the disastrous event

• Exposure: refers to the values/humans that are likely to be affectedRisk adaptive / mitigative measures

according to “Perception of Risk” by Slovic, Science, 1987; Crichton, 1999; UN 1992; Kron(2005); Barredo et al. (2007); and Oppenheimer et al., 2014, AR 5, IPCC

In general, a risk map shows the magnitude and nature of the risk, which depicts the levels of

expected negative impacts at a spatial scale during a particular time period for a particular

disaster/health event, and transfers the risk information to different end-users in an easy and

understandable way.

( ) ( ) ( )ExposureityVulnerabilHazardRisk =

Vulnerability• Vulnerability refers to the propensity or

predisposition to be adversely affected.Vulnerability encompasses a variety ofconcepts and elements including sensitivity orsusceptibility to harm and lack of capacity tocope and adapt.

• A broad set of factors such as wealth, socialstatus, and gender determine vulnerability torisk.

• Vulnerability Indicators are defined as variableswhich are an operational representation of anattribute, such as quality and characteristics ofa system regarding the susceptibility, copingcapacity and resilience of a system to animpact of a disaster/health event (Gallopin,2006; Birkmann, 2007)

Relevant Social Indicators*(1) Total population(2) Total household(3) Children population(4) SC-ST Population(5) Illiterate population(6) Marginal worker(7) Non working population(8) Elderly Population(9) Disable population(10) Bad household Condition(11) % household with no drainage(12) % household with no latrine(13) Drinking water away(14) No Electricity(15) Medical Facility

*Based on the availability of demographic data with Census

of India (CoI) and relevance to epidemiological disaster 2011 Census of India data <http://censusindia.gov.in/Data_Products/Library/Indian_perceptive_link/Census_Terms_link/censusterms.html>

Vulnerability (contd.)

• We implemented Cutter, S. L., & Finch, C. (2008). Temporal and spatial

changes in social vulnerability to natural hazards, Proceedings of the National

Academy of Sciences, 105(7), 2301-2306.

Methodology

The social vulnerability (SoV) is a unitless, spatial measure, and its importance

is in its comparative value across geographic locations, not its absolute value.

Please note: We consider SoV as an algorithm for quantifying social

vulnerability rather than a simple numerical index that can be ground-truthed

with direct observational data. For interpretive reasons, high social

vulnerability is defined as those districts with SoV scores >= 2 SD from the

mean, whereas districts low in social vulnerability have SoV scores <= -2.5 SD

from the mean.

The most common method to assess vulnerability is the indicator approach (Gbetibouo et al. 2010):

• Scalability(household,district,andnational levels)

• Comparability• Multi-

dimensionality,differentiability

• Trend

• Data arrangement – let there be M regions and K indicators. Let Xij be the value

of indicator ‘j’ corresponding to region ‘i’, Vulnerability Index (VI) of an indicator:

Xmin is the minimum number of all units, Xmax is themaximum number of all units, and a and b define therange within which all VI values fall (may be 0.01 and 1,respectively).

• On the basis of this index (in ordinal sense), different regions are ranked and

grouped to be relatively less or more vulnerable (here in 0.01 - 1 scale).

Hazard and Exposure

Hazard: defined as theprobability that a randomlyselected infected person fromthe country belongs to a specificdistrict

Calculated for ith district as ൗ𝑛𝑖

σ 𝑛𝑖

For districts with zero infected, avery low value is assigned to 𝑛𝑖(less than 1, say 0.5)

Exposure:

Value of exposure is assigned as:

(i) 1, when the district of interesthas at least one infected person

(ii) 0.5, when the district ofinterest has 0 infected, but anyof the neighbouring district hasat least one infected person

(iii) 0.1, else (considering the lowpossibility of lack of detectiondue to lack of testing)

Assumptions and Limitations

• Hazard is based on number of infected persons as available fromdifferent sources; however, there are many infected persons butasymptomatic and not tested.

• Although the present study successfully maps social vulnerability asone of the components in epidemiological risk, it has certainlimitations. Few relevant indicators (such as population below povertylevel, number of single-parent households, and patterns of migration)have not been considered in the present study, primarily due tounavailability in Census data.

Outcome and ResultsRisk Map as on 16th April, 2020

Advantages: • This risk map is not just based on number of

infected persons as done in majority of theassessments.

• This considers, vulnerability and exposure alongwith number of infected.

• The computed risk is not only associated with thepresent condition and but also considers thecoping capacity (which refers to capacities thatallow a system to protect itself in the face ofadverse consequences) to a possible futureoutbreak.

• As for example a conventional risk map of earlyMarch 2020 would show very high risk overKerala than other states (if we only considernumber of infected), but the present conditionshows Kerala has highest resilience due to verylow vulnerability. This aspect is considered in thisrisk map.

Economic Impacts Assessment

Economic Costs of COVID-19 lockdown on Indian Economy

Haripriya Gundimeda(Professor, Humanities and Social Sciences)

Vinish Kathuria(Professor, SJM School of Management)

20th April 2020(with inputs from Nitin Lokhande, Gowtham M,

Dhanyashree Bhuvandas, Research scholars, HSS and SOM)

Introduction and Objectives

26-04-2020 52

• COVID-19 has brought the entire country to halt and normal life has been hit. Given the nature of infection and uncertainty regarding the number of cases in India, social distancing and lockdown are the immediate solutions to save lives and hardships. However, this has huge repercussions on the Indian economy especially due to loss in livelihoods due to lack of economic activity.

• Objectives• The objective is to estimate the economic impact of lockdown on Indian economy at

district level under various scenarios. The direct impact of COVID 19 is estimated on a) the marginal workers (casual labourers and workers involved in MNREGA), b) the consumption expenditure and c) the state domestic product (SDP).

Approach1. Agriculture, forestry and fishing

1.1 Crops1.2 Livestock1.3 Forestry and logging1.4 Fishing and aquaculture

2. Mining and quarryingPrimary

3. Manufacturing

4. Electricity, gas, water supply & otherutility services

5. Construction

SecondaryIndustry

6. Transport, storage, communication & services related to broadcasting

6.1 Railways6.2 Transport by Means Other than Railways6.3 Storage6.4 Communication & Services Related to Broadcasting

7. Trade, repair, hotels and restaurants8. Financial services9. Real estate, ownership of dwelling &

professional services

10. Public administration11. Other services

Tertiary

• Looked in sectoral composition of state domestic products at the district level

• All sectors equally impacted

• Effect on the district depending on relevance of the subsector to a particular district

• For estimating losses in consumption and employment, latest round of NSSO survey data has been used

• For estimating the loss in state domestic product, CSO data state-wise GSDP (gross date domestic product) estimates have been used.

• To estimate foregone consumption expenditure, suitable assumptions have been made - complete drop in expenditure for entertainment, and travel and reduced expenditure for some activities.

• For calculation of loss in GSDP, some sectors would be affected immediately (hotel and restaurants), some with somewhat lag (manufacturing, financial services) and some will not be affected (public administration or utilities).

Results• The estimates suggest wide-variation in the values across the districts and states depending

upon their dependence on these services and the structure of the state economy.

• At an aggregate level, for an Indian economy of the size of Rs. 140.78 lakh crore (2018-19 estimates), the lost income to marginal workers is nearly Rs. 58,000 crore (0.41% of GDP) for three weeks lock down.

• The impact soars to Rs. 1,65,000 crore (1.18% of GDP (Gross domestic product) if lockdown persists for two months.

• Not unexpectedly, the most impact is to the marginal workers from Uttar Pradesh, Telangana, Bihar, West Bengal, and Madhya Pradesh, forming the top five states, as they forgo nearly 45% of this total income.

• The corresponding figures for lost consumption expenditure are Rs. 1,10,542 crores (0.79%) and Rs. 3,15,834 crores (2.24%) for 21 days and 2 months respectively. Maharashtra, Uttar Pradesh, West Bengal, Tamil Nadu, and Andhra Pradesh are the top five states affected by this, again forming nearly 45% of the lost expenditure.

• Regarding lost GSDP due to lockdown, the figures are 3.46% of GDP for 21 days lockdown and over 12.0% of GDP for a lockdown of 2 months. As expected, Maharashtra, Gujarat, Tamil Nadu, Uttar Pradesh, and Karnataka suffer most with a nearly 47% hit is GSDP.

Way forward

• Estimates reflect only the direct impact on the economic sector, the social sector, and the household consumption sector but not the aftermath of the pandemic – the financial anguish, bankruptcies, and increased unemployment.

• The model will be revised further to include district level impacts

• Working to produce a policy brief on economic impacts and the way forward for the Governments

Outcome in brief

• Epidemiological Prediction• Very high uncertainty across the models in simulating incidence, mortality and the

time when the number of infections reaches maximum• This attributes to different model assumptions, different methodologies and

different sources of data sets.• There is a model consensus that lockdown helps, only when it is associated with

increased rate of testing, tracing and isolation.

• Monitoring Risk• Risk and selecting hotspots, just based on hazard, may not be an appropriate method• The risk should also consider vulnerability of the region• Example: Kerala had highest number at some point of time in the past, but due to

high vulnerability the state could achieve highest control over the number of infections.

• A new framework is developed and risk is being monitored on real-time

For details, please visit http://www.civil.iitb.ac.in/~covid19india/

Stay Safe

Thank you