A SPATIAL EXPLORATORY STUDY ON COVID-19 DISASTER - …

Turkish Journal of Physiotherapy and Rehabilitation; 32(3)

ISSN 2651-4451 | e-ISSN 2651-446X

www.turkjphysiotherrehabil.org 5332

A SPATIAL EXPLORATORY STUDY ON COVID-19 DISASTER - CURRENT

OUTBREAK IN TAMIL NADU, INDIA

Vignesh K.S1, Indhiya Selvan V.N

2, Kalpana Kosalram

3 & Abarajitha A

4

1,3School of Public Health, SRM Institute of Science and Technology,

Chennai 603203, Tamil Nadu India 2Department of Geography, University of Madras, Chennai 600005, Tamil Nadu, India

4Medical Consultant, Chennai

ABSTRACT

Background: The COVID -19 which is also called as Novel Coronavirus Pneumonia (NCP) is a serious

disease which got emerged on December 2019 in China. In the context of India, the first case was diagnosed

and reported in the state of Kerala during the month of January 2020.The distribution of virus inflated

gradually to the different parts of the country. The overall cases recorded in India surged more than 600nos

till March 2019. The concept of medical geography is an emerging tool to identify the spatial distribution,

pattern, concentration of the virus spread and also it determines health status of the local community. The

main aim of the current research work is to figure out the prevailing situation and further to delineate the

COVID Vulnerability Zone (CVZ) for the districts of Tamil Nadu.

Methods: The researchers in the current study categorized the non-spatial data as COVID cases (Till March

28th, 2020). COVID -19 quarantine follow-up cases, Population data, COVID-19 testing centres and

travellers log of the victims. The weighted overlay analysis was performed to identify the COVID

Vulnerability Zone (CVZ) with the inclusion of four thematic layers such as COVID-cases, quadrant ine

follow up cases population density and COVID -19 testing centres. Additionally, the travellers log was also

analysed to determine the source of virus to the state.

Results: As a result, the study highlighted the COVID – 19 Vulnerability Zone (CVZ) which was

categorized into five vulnerability classes such as Very low, Low, Moderate, High and Very high

vulnerability.

Conclusion: Additionally the authors concluded the flow of victims with the travel history to trace out the

spread of diseases in the state of Tamil Nadu. As a conclusion, the GIS have the potential to identify the

spatial distribution, pattern, concentration of the virus spread and also it determines health status of the local

community.

Key words: COVID-19, Spatial Distribution, COVID Vulnerability Zone (CVZ), Vulnerability Classes.

I. INTRODUCTION

The world’s attention is rightly focused on human toll of COVID-19 from December 2019 till date (presume

April, 2020). In accordance to World Health Organization (WHO, 28th March 2020) the confirmed cases were

found to be 5,12,701 persons and death tolled into 23,495 persons in 202 countries around the world. Henceforth,

WHO declared as a Public Health Emergency of International Concern [1].The outbreak has a potential

disastrous implications and most of the country like United States of America (USA), Italy and India announced

COVID -19 as a disaster. With the special reference to the authors Zhu et al. (2019) highlighted that the COVID -

19 which is also called as Novel Coronavirus Pneumonia (NCP) or SARS-CoV-2 was initially reported in

Wuhan, China on December 2019 [2]. Further the spread of diseases inflated exponentially within 60 days of

time duration in Asian, European and other Middle East Countries [3]. On comparing the COVID -19 to the

other recent outbreaks such as SARS, MERS H1N1 and Ebola, the corona virus that has developed in a global

span is disturbing the international community with respect to sense and security [4]. This is because that


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COVID-19 is proving its impact on both transmissibility and curability aspects. The study revealed that the

corona virus is an easily transmitted virus with a fatality rate predicted to be more than any other temporal

influenza [5].

In a study done in South Korea on COVID -19 illustrated that disease initiated with a single case and further it

got super speeded exponentially as an event which turned up in 3,900 cases from the community church in the

city of Daegu (Kuhn 2020; Ryall 2020) [6,7]. The spatial distribution study on Gansu Province, China

determined the epidemiological pattern of COVID-19 through hot spot analysishighlighted the decreasing rate of

disease transmissions in China [8]. The Geographical Information System (GIS) plays a vital role in real world

process in particular to the disastrous situations. The authors Verka and Angelina (2008) used GIS techniques in

three various applications such as evaluation of plan based travel management, inventory management of

travellers and analysis of spatial and attribute data to determine the development of tourism [9]. The study

proved the importance of GIS on designing a database dashboard for various travellers from countries with

utilization of remote sensing and global positing system (GPS) [10].

The similar study conducted by Risva et al. (2018) on spatial distribution of Dengue Heamorrhagic Fever (DHF)

in East Kalimantan of Indonesia province investigated the vulnerability level based on the rainfall, population

density, water body and natural water body condition using GIS techniques [11]. An Inverse distance weighting

(IDW) determines the correlation between the distance occurrence and conditioning factors. Ajaj et al. (2018)

focussed on spatial GIS based modelling to estimate the relative risk of various diseases using IDW method. The

study highlighted the simplifying procedure to measure the epidemic state of different diseases [12]. Furthermore,

the spatial hotspot analysis using GIS determined the detection of Kidney diseases in Matale District, Sri Lanka

which motivated the health care facilities to locate the areas of patients and to create awareness for the reduction

of kidney patients [13].

In recent trends, the field of Medical geography plays a vital role on spatial distribution of health care services

and also provided feasibility support for the better health maintenance. The researchers used spatial analysis tool

to delineate the primary health care services and associated community who can utilize the services efficiently

[14]. The other study conducted in Los Angeles mapped the health vulnerable populations such as diabetes

neurological and kidney issues using hotspot analysis [15]. The Thiessen Polygon is GIS based tool which

calculates the nearest service centres using location or point, it generates the polygon which having closer

distance from point [16]. Based on the above evidence, it is clear that GIS can act as a decision making tool in

healthcare systems and also it has a tendency to implement various policies and mitigation strategies. The

weighted overlay analysis is a standard technique used often to solve the multi-criteria problems such as travel

cost and also to generate surfaces determining the site suitability [17]. In this context, the authors attempted to

demarcate the vulnerability zone of COVID – 19 in Tamil Nadu using weighted overlay analysis. Further hotspot

analysis was also performed to delineate the state on infectious disease.

II. MATERIALS AND METHODS

2.1 Dataset Used

The dataset utilized for the study were both spatial and non-spatial data which was geocoded into spatial

information to reveal the outcome of the present analysis. The Table.1 highlighted the dataset incorporated for the

study. The secondary data used for the study were from authenticated sources such Tamil Nadu Ministry of

Health and Family Welfare, Government of Tamil Nadu COVID -19 portal, and Indian Council of Medical

Research (ICMR). In this context, the data used are COVID -19 test centres locations, House wise quarantine

follow-up in all districts and more precisely the travel log of registered positive cases of corona. Additionally, the

Hon’ble Chief Minister review report was also used to perform the research work efficiently. Furthermore Census

India, 2011 data were also used to emphasize population and population density of the study area.

Table 1: Dataset

Sl. No Input Data Source Date

1 COVID-19 Test labs

Ministry of Health and Family Welfare,

Government of Tamil Nadu 27.03.2020

Indian Council of Medical Research (ICMR) 27.03.2020


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2 District wise Home

Quarantine follow-up Hon’ble Chief Minister Review Report 26.03.2020

3 Travel logs of coronavirus

patients

Directorate of Public Health and Preventive

Medicine Health and Family Welfare

Department, Government of Tamil Nadu

27.03.2020

4 COVID-19 Victims

Directorate of Public Health and Preventive

Medicine Health and Family Welfare

Department, Government of Tamil Nadu

28.03.2020

5 Population Data Census India 2011

2.2 Methods

The researchers in the current study categorized the non-spatial data as COVID cases (Till March 28th, 2020).

COVID -19 quarantine follow-up cases, Population data, COVID-19 testing centres and travellers log of the

victims. Further, the non-spatial data were geocoded into spatial information. The Thiessen polygon method is

used calculate the service area of each centres with help of its geographical locations. The weighted overlay

analysis was performed to identify the COVID Vulnerability Zone (CVZ) with the inclusion of four thematic

layers such as COVID-cases, quarantine follow up cases population density and COVID -19 testing centres.

Additionally, the travellers log was also analysed to determine the source of virus to the state.

III. STUDY AREA

The Tamil Nadu state has been chosen as a study area for the present study. According to Census India, 2011 the

state ranked the 10th largest state by area and subsequently, 6

th largest state in terms population. The total

population of state is 72,147,039 persons which comprises of 36,137,975 males, 36,009,055 females covers

1,30,058 sq.km of land in India [18]. The latitudinal and longitudinal locations of the state are 78° 39' 24.8076'' E

and 11° 7' 37.6428'' N respectively. The Fig.1 portrayed the location of study area. The state is lineated at

southeast extremity of the Indian Peninsula which comprises of Bay of Bengal on the east and on the west by

Indian Ocean. The state has four major ports and four international airports which emphasized the vulnerability

of COVID-19 due to the transit of international passengers. The state is renowned for public health quality on

providing health care services with an affordable cost to the rural community. To be noted, the state enacted a

Public Health act in 1939 and also has a unique public health cadre in the district level.

Figure 1: Study Area.

IV. RESULTS AND DISCUSSION

4.1 COVID – 19 Testing Centres


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The COVID -19 testing centres are established in 14 locations across Tamil Nadu in which 10 centres are under

the control of government and remaining belongs to private health organizations. The proportionality of the virus

testing centres is analysed with respect to the population concentration. The Fig 2 depicted the location of 14 test

centres precisely.

4.2 COVID-19 Cases in Tamil Nadu

The district wise COVID-19 cases recorded in Tamil Nadu as on 28th March, 2020 was 42 which was further

analysed spatially to identify the distribution pattern of the cases. Based on the in-depth investigation the study

area was grouped into five classes (Fig 3) ranging from 0 to 1, 1 to 2, 2 to 3, 4 to 6 and 7 to 17 cases respectively

in accordance with the concentration of cases admitted. The concentration of cases is very high (7 to 17) in the

districts of Chennai, Erode, Salem followed by high cases (4 to 6) in Vellore, Madurai, Moderate cases are found

in Kancheepuram, Thanjavur, Tiruppur, and Tirunelveli. The remaining districts in the study area recorded with

the minimal victims or no victims. The researchers based on the analysis emphasized that the higher

concentration of cases was recorded in regions of Chennai, Kancheepuram and Tiruvallur. These areas are known

to be hub for transportation such as Airways and Railways in which the most of the travellers logged in for

various purposes.

Figure 2: Test Centres.


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Figure 3: COVID -19 Cases.

4.3 COVID-19 House Quarantine Follow-up

The COVID-19 home quarantine follow-up is an important data which can be used to predict the future

distribution of Cases based on the current concentration. The total COVID-19 home quarantine follow-up cases

in the study area was 96,063 (as on 28th March, 2020) in which the Chennai records with highest cases with

20,240 and minimum in Tirupattur with 278 cases. The home quarantine follow-up is grouped into five classes

(Fig.4) such as 397 to 895, 896 to 1495, 1496 to 3038, 3038 to 4493 and 4493 to 20240 persons respectively. The

maximum (4493 to 20240) number of cases is found in Chennai, Kancheepuram and Tiruvallur, followed by the

high (3038 – 4493) in Coimbatore, Madurai, Kanniyakumari, Thanjavur, Tiruchirappalli and Ramanathapuram.

Comparatively, the moderate cases region were Sivagangai, Pudhukkottai, Nagapattinam, Villupuram,

Thoothukkudi, Tiruvarur and Vellore districts respectively

Figure 4: District Wise COVID-19 Home Quarantine.

4.4 Population Density


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The population density was calculated for the study area with district level density. The ratio of population to the

area of the individual district determines the population density which is a dominant parameter to assessthe

vulnerability range COVID-19. Further, the density categorized into five groups (Fig.5) from low to very high

density. The regions with dense population (1106 – 26903 per sq.km) are Tiruvallur, Chennai, Kancheepuram

and Kanniyakumari which credited the high level growth and opportunity. The region falling into high density

(823 – 1106 per sq.km) are Vellore, Cuddalore, Thanjavur, Nagapattinam, Salem, Coimbatore, Madurai, and

Tiruchirappalli.

Figure 5: Population Density.

4.5 Thiessen Polygon (Testing Centre Covering Area)

Figure 6: Area Covered by Testing Centres.

The proportionality of testing centres is accessed using two methods like measuring of population (statistical

method) ratio with that of testing centres and assessing geographical coverage of testing centres using Thiessen


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polygon (spatial Method). Based on the location, the service area covered by the testing centres was calculated

using Thiessen polygon method. The 14 polygons were created and grouped into five classes (Fig.6) ranging

from 18 – 21331 sq.km. The classification divided into five groups such as 18 – 973 sq.km, 974 – 4745 sq.km,

4746 – 10455 sq.km, 10456 – 15998 and 15999 – 21331 sq.km.. The present statistical information highlighted

that the testing centres in Tamil Nadu are very minimal compared to the population ratio. Increase in the

screening centres can help in tracking and cracking of COVID -19 victims. The Table.2 illustrated the area

covered by the testing centres.

Table 2: Area Covered by the Testing Centres

Sl. No Location Testing Centres Area sq.km

1 Madurai Madurai Medical College 15898.29

2 Tiruvarur Tiruvarur Medical College 9464.87

3 Villupuram Government Medical College 12571.13

4 Salem M.K Medical College 21337.18

5 Chennai Rajiv Gandhi Government General Hospital 893.4

6 Trichy Government Medical College 14468.19

7 Tirunelveli Tirunelveli Medical College 13723.33

8 Theni Theni Medical College 10132.22

9 Porur Sri Ramachandra Medical College & Research Institute 4743.25

10 Chennai Neuberg Ehrlich Lab Pvt Ltd 18.29

11 Chennai Apollo Hospitals Enterprise Ltd 82.13

12 Chennai King Institute of Preventive Medicine & Research 983.25

13 Vellore Dept. of Clinical Virology, CMC, Vellore 10455.5

14 Coimbatore Coimbatore Medical College 15287.92

Total Area 1,30,058

4.6 COVID Vulnerability Zone (CVZ)

The weighted overlay tool was used to demarcate the COVID -19 Vulnerability Zone (CVZ) for the study area.

For an easy understanding and analyses purpose, the researchers presumed the total weightage as 100 in which an

individual parameter can be categorized depending on the degree of influence. The present study comprised of

four parameters (Table 3) such as COVID Cases, COVID Home Quarantine cases, Population Density and

Thiessen Polygon which was assigned with suitable influential weights. The authors tried to justify the assigned

weights in such a way that (i) COVID Cases with 35 assigned the highest weightage, due to the maximum

transmission level of virus from affected to the community (ii) COVID Home Quarantine, with weightage of 30

because of the risk level associated and also the possible transmission due to direct or indirect contact with the

victims, followed by (iii) Population Density was assigned weightage of 20 which can lead to community

spreading and finally (iv) Thiessen Polygon area was assigned 15 for the estimation of the spatial coverage of

each testing centre.

Table 2: Weighted Overlay Analysis

Parameter Sub classes Potential Weightage Rank

COVID Cases in nos.

0 – 1 Very Low

35

1

1-2 Low 2

3-4 Moderate 3

4- 6 High 4

7 - 17 Very High 5

COVID Home Quarantine Cases in nos.

397 - 895 Very Low

30

1

895 - 1495 Low 2

1495 - 3038 Moderate 3

3038 - 4493 High 4

4493 - 20240 Very High 5

Population Density

288 - 399 per sq.km Very Low

20

1

399 - 533 per sq.km Low 2

533 - 823 per sq.km Moderate 3

823 - 1106 per sq.km High 4

1106 - 26903 per sq.km Very High 5

Thiessen Polygon

18 – 973 sq.km Very Low

15

1

947 – 4745 sq.km Low 2

4746 – 10455 sq.km Moderate 3


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10456 – 15998 sq.km High 4

15999 – 21333 sq.km Very High 5

As a result of the analysis, the study highlighted the COVID – 19 Vulnerability Zone (CVZ) which was

categorized into five vulnerability classes (Fig.7) such as Very low, Low, Moderate, High and Very high

vulnerability. On spatial Distribution, very high vulnerable region are found in Madurai, Salem, Chennai and

Kancheepuram, High regions arescattered over Vellore, Thanjavur, Erode, Tirunelveli, Kanniyakumari and

Kancheepuram whereas the moderate vulnerable regions are located in Tiruvallur, Tiruchirappalli, Cuddalore and

Coimbatore. The east coast districts from Villupuram to Tuticorin falls under the Low Vulnerability and

contrarily, the central districts areemphasized as Very Low Vulnerable region respectively.

Figure 7: COVID Vulnerability Zone (CVZ).

4.7 Hotspot Analysis

The most spotted region to COVID -19 was identified using hotspot analysis performed from the quarantine

follow-up data from the month of February to March, 2020. The analysis (Fig.3) shredded light on the region

which is more prone to transmission of COVID-19. The hotspot analysis determines the statistical clustering of

values based on the weightage from hotspot and cold spot in which hotspot denoted the high level of incidence

and cold spot as very low incidence. The result was classified into seven types like Cold spot – 99 per cent

confidence, Cold Spot – 95 per cent confidence, Cold Spot – 90 per cent confidence, Not Significant, Hot Spot –

90 per cent confidence, Hot Spot – 95 per cent confidence and Hot Spot – 99 per cent confidence. The result

highlighted that the most active and sensitive region are identified like Chennai, Tiruvallur and Kancheepuram

due to high quarantine cases and also high COVID cases. Comparatively, the other districts recorded very low

quarantine cases. The researchers stressed that the regions at hotspot have a 30,000 cases under quarantine and

henceforth there is significant chance to spread the diseases positively.


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Figure 8: COVID – 19 Hotspot Regions.

4.8 Flow Map of Infected Travellers with Travel Log

Figure 9: Flow Map of Infected Travellers with Travel Log.

The authors furthermore emphasized the study to assess the origin source of COVID -19. The travellers log of

infected victims were traced by government from different which mapped (Fig.9) for better visualization. The

results (Table.3) showed the departure of 23 victims from international destinations which are at maximum

percentage (75 per cent) domestic destinations were around 25 per cent from Delhi, Bangalore and Chennai.

Also more specifically, the Chennai recorded the maximum passengers which are highly prone to the

transmissible disease. On contrary, it is known that USA is the leading carrier for COVID-19 and hence the

departure of USA victims (04 nos.) should be quarantined effectively to avoid the threat.

Table 3: Travel Log

Travel Mode Location No. of Victims (%) Case Ratio

International USA 3 9.68 23 (74.19%)


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London 4 12.90

Singapore 4 12.90

Indonesia 4 12.90

Dubai 2 6.45

New Zealand 2 6.45

Oman 1 3.23

Switzerland 1 3.23

Ireland 1 3.23

Spain 1 3.23

National

Delhi 4 12.90

8 (25.81%) Bangalore 3 9.68

Chennai 1 3.23

Total 31 100 31 (100%)

V. CONCLUSIONS

The study utilized the GIS technique to assess of COVID Vulnerability Zone (CVZ) for the state of Tamil Nadu.

The results have drawn a special attention to reduce the level of risk for corona virus. The weighted overlay

analysis depicted the vulnerability region of the study area. The analysis of testing centres recommended the

authorities to establish more centres based on the population density distributed over the study area. The hotspot

analysis performed also determined the hottest spot for the spread of diseases. Additionally the authors

concluded the flow of victims with the travel history to trace out the spread of diseases in the state of Tamil

Nadu. As a conclusion, the GIS have the potential to identify the spatial distribution, pattern, concentration of the

virus spread and also it determines health status of the local community.

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A SPATIAL EXPLORATORY STUDY ON COVID-19 DISASTER - …