Exploring dependence of COVID-19 on environmental factors and spread prediction in India

COVID-19 has taken the world by storm, with the majority of nations still being challenged by the novel coronavirus. The present work attempts to evaluate the spread of COVID-19 in India using the Susceptible-Exposed-Infectious-Removed (SEIR) model to establish the impact of socio-behavioural aspects, especially social distancing. The impact of environmental factors like temperature and relative humidity (RH) using statistical methods, including Response Surface Methodology (RSM) and Pearson’s correlation, is also studied on numbers of COVID-19 cases per day. Here we report the resultant changes of lockdowns-unlocks initiated by the Government of India for COVID-19, as against the scenario of total lockdown. The phased unlocks and crowded gatherings result in an increase in the number of cases and stretch the mitigation timeline of COVID-19 spread, delaying the flattening of the curve. The SEIR model predictions have been fairly validated against the actual cases. The daily spread of COVID-19 cases is also fairly correlated with temperature in Indian cities, as supported by well-established causation of the role of higher temperatures in disrupting the lipid layer of coronavirus, but is greatly undermined by the key factor of social distancing and gets confounded with other multiple unknown co-varying environmental factors. However, the analysis couldn’t clearly establish the role of RH in affecting daily COVID-19 cases. Hence, it becomes essential to include environmental parameters into epidemiological models like SEIR and to systematically plan coordinated laboratory experiments and modeling studies to draw conclusive inferences, assisting policymakers and stakeholders in formulating comprehensive action plans to alleviate the COVID-19 spread.