Intelligent Identification on State-Wise COVID-19 Infection in India

The COVID-19 pandemic has significantly impacted nations worldwide, with India being no exception. This case study delves into the state-wise variations in the severity of COVID-19 infections across India, providing a nuanced understanding of the diverse challenges faced by different regions. Through an extensive analysis of epidemiological data, healthcare infrastructure, and public health responses, this study aims to identify key factors influencing the varying degrees of severity observed. The research employs a multidisciplinary approach, combining statistical models, geographical mapping, and qualitative assessments to elucidate the dynamic nature of the pandemic across Indian states. This is examined through different prototype models as Two-Way ANOVA Model, Holt Winter's Method and Logistic Regression. By examining infection rates, mortality rates, and healthcare resource utilization, the study seeks to highlight patterns and trends that may be indicative of regional disparities in preparedness, response, and resilience. Furthermore, the case study explores the impact of socio-economic factors, population density, and vaccination coverage on the severity of COVID-19 outcomes. This holistic analysis aims to provide actionable insights for policymakers, public health officials, and researchers, contributing to the ongoing efforts to optimize strategies for pandemic management at the state level.