Comparative Study of Coronavirus Pandemic in the United States and India Through Predictive Modeling

Abstract

Objective: This study aims to develop predictive models of the coronavirus pandemic for the US and India and also to do a comparison of various parameters of COVID-19 for the two countries. The USA and India have faced serious challenges during the COVID-19 pandemic and many lives have been lost. Both countries differ significantly in terms of population density, healthcare infrastructure, and other socioeconomic factors. The population density is much higher in India than in the USA. The rationale for comparing the USA and India using the generalized additive model (GAM) is to see how disparities in the two countries can be accounted for by socio-economic parameters and healthcare infrastructure. Generalized Additive Model (GAM) is a crucial tools for predicting mortality due to various COVID-19 risk factors at any given time point. GAM is flexible and robust because it can capture both linear and nonlinear effects by adjusting the degrees of freedom. It also allows us to include covariates that may initially smoothen, and subsequently, the effect of the main variable can be added to predict mortality. The predictive model is useful in providing valuable insights to enhance resource allocation, risk management, planning, and resource allocation, ultimately resulting in better pandemic management and improved health outcomes.  Method: The data is extracted from 1 April 2020 to 30 April 2022 (25 months) for a total of 790 days. It consists of: daily confirmed cases, active cases, total confirmed or cumulative cases, recovered cases, mortality per day, cumulative mortality, and true active cases. The association between daily confirmed cases and mortality was established using the generalized additive model (GAM) with natural spline smoothers at (12,2,2) degrees of freedom. Results: During this period, there were 1017381 deaths in the US and 523807 in India, with mortality rates of 1339 and 689 per day for the US and India, respectively. It has been observed that the lag effects of daily confirmed cases (DCC) on mortality are significant. The relative risk shows a significant increase in mortality for every 1000 daily confirmed cases in the US and India. Conclusion: Using the applications of the GAM model, it has been observed that the lag effects of daily confirmed cases (DCC) on mortality were significant. Monthly and yearly comparisons were also significant.