Orginal Research
2020 December
Volume : 8 Issue : S1


Spatial modeling for COVID-19 analysis: An Indian case study

Iyyanki M, Prisilla J, Kandle S

Pdf Page Numbers :- 19-32

Muralikrishna Iyyanki1, Jayanthi Prisilla2,*, and Sudarshan Kandle3

 

1Former Dr. Raja Ramanna Distinguished Fellow DRDO and Director R&D JNT University, Hyderabad, Telangana, India

2The Airport Authority of India, Shamshabad, Hyderabad, Telangana 500409, India

3Department of Geography, Osmania University, Amberpet, Hyderabad, Telangana 500007

 

*Corresponding author: Prisilla Jayanthi, The Airport Authority of India, Shamshabad, Hyderabad, Telangana 500409, India. Email: prisillaj28@gmail.com

 

Received 11 August 2020; Revised 26 October 2020; Accepted 19 November 2020; Published 27 November 2020

 

Citation: Iyyanki M, Prisilla J, Kandle S. Spatial modeling for COVID-19 analysis: An Indian case study. J Med Sci Res. 2020; 8(S1):19-32. DOI: http://dx.doi.org/10.17727/JMSR.2020/8S1-3

 

Copyright: © 2020 Iyyanki M et al. Published by KIMS Foundation and Research Center. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

The coronavirus disease 2019 (COVID-19) outbreak in India from January 31, 2020, onwards to June 15, 2020, has reached confirmed cases over 3,32,424 that are being reported. The aim of this study is to predict and explore the spatial distribution of COVID-19 data of India using three models – geographical weighted regression (GWR), generalized linear regression (GLR), and ordinary least square (OLS). In this paper, the swift rise in COVID-19 cases is experiential after the lockdown period. This is explored using ArcGIS on the confirmed case of June 15, 2020, as the response with the explanatory of COVID-19 cases, i.e March 15, 2020, April 7, April 12, May 12, and June 1, 2020. The confirmed cases of the dataset is classified into three cases ie. case-1: June 15, 2020, vs March 15 and April 7, 2020; case-2: June 15, 2020 vs April 12, May 12 and June 1, 2020; and case-3: June 15, 2020 Vs all dates mentioned in discussion Hence, the prediction using GWR gave the much closer values for June 16, 2020. AICc of GWR (618.9038) was found to have the minimum value over GLR and OLS models. The day-wise increase and samples tested per day in twelve different states is analyzed using STATA. The number of testing varies with states to states, depending on the population and testing labs available. The percentage for each slope is achieved as m1 (-5.714 %), m2 (39.393%), m3 (6.521%) and m4 (46.938%).

 

Keywords: COVID-19; GIS; spatial data; spatial models; testing samples

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