Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective

Hemant Bherwani; Saima Anjum; Suman Kumar; Sneha Gautam; Ankit Gupta; Himanshu Kumbhare; Avneesh Anshul; Rakesh Kumar

doi:10.1007/s10668-020-00849-0

Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective

Environ Dev Sustain. 2021;23(4):5846-5864. doi: 10.1007/s10668-020-00849-0. Epub 2020 Jul 8.

Authors

Hemant Bherwani^{1

2}, Saima Anjum¹, Suman Kumar¹, Sneha Gautam³, Ankit Gupta^{1

2}, Himanshu Kumbhare¹, Avneesh Anshul¹, Rakesh Kumar^{1

2}

Affiliations

¹ CSIR-National Environmental Engineering Research Institute (CSIR-NEERI), Nehru Marg, Nagpur, Maharashtra India.
² Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh India.
³ Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu India.

Abstract

Originating from Wuhan, China, COVID-19 is spreading rapidly throughout the world. The transmission rate is reported to be high for this novel strain of coronavirus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as compared to its predecessors. Major strategies in terms of clinical trials of medicines and vaccines, social distancing, use of personal protective equipment (PPE), and so on are being implemented in order to control the spread. The current study concentrates on lockdown and social distancing policy followed by the Indian Government and evaluates its effectiveness using Bayesian probability model (BPM). The change point analysis (CPA) done through the above approach suggests that the states which implemented the lockdown before the exponential rise of cases are able to control the spread of the disease in a much better and efficient way. The analysis has been done for states of Maharashtra, Gujarat, Madhya Pradesh, Rajasthan, Tamil Nadu, West Bengal, Uttar Pradesh, and Delhi as union territory. The highest value of Δ (delta) is reported for Gujarat and Madhya Pradesh with a value of 9.6 weeks, while the lowest value is 4.7, evidently for Maharashtra which is the worst affected. All of the states indicate a significant correlation (p < 0.05, tstat > tcritical) for Δ, i.e., the difference in the time period of CPA and lockdown with cases per population (CPP) and cases per unit area (CPUA), while weak correlation (p < 0.1 and tstat < tcritical) is exhibited by delta and cases per unit population density (CPD). For both CPP and CPUA, tstat > tcritical indicating a significant correlation, while Pearson's correlation indicates the direction to be negative. Further analysis in terms of identification of high-risk areas has been studied from the Voronoi approach of GIS based on the inputs from BPM. All the states follow the above pattern of high population, high case scenario, and the boundaries of risk zones can be identified by Thiessen polygon (TP) constructed therein. The findings of the study help draw strategic and policy-driven response for India, toward tackling COVID-19 pandemic.

Keywords: Bayesian probability; COVID-19; Change point analysis; SARS-CoV-2; Voronoi diagram.