The main objective of this study is to identify the socioeconomic, meteorological, and geographical factors associated with the severity of COVID-19 pandemic in India. The severity is measured by the cumulative severity ratio (CSR)—the ratio of the cumulative COVID-related deaths to the deaths in a pre-pandemic year—its first difference and COVID infection cases. We have found significant interstate heterogeneity in the pandemic development and have contrasted the trends of the COVID-19 severities between Maharashtra, which had the largest number of COVID deaths and cases, and the other states. Drawing upon random-effects models and Tobit models for the weekly and monthly panel data sets of 32 states/union territories, we have found that the factors associated with the COVID severity include income, gender, multi-morbidity, urbanization, lockdown and unlock phases, weather including temperature and rainfall, and the retail price of wheat. Brief observations from a policy perspective are made toward the end.

中文翻译：

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印度COVID-19大流行的严重程度

这项研究的主要目的是确定与印度COVID-19大流行严重程度相关的社会经济，气象和地理因素。严重程度是通过累积严重程度比率（CSR）来衡量的，该比率是大流行前一年中与COVID相关的累积死亡人数与死亡人数之比-首次差异和COVID感染病例。我们发现大流行病发展过程中存在重要的州际异质性，并且对比了马哈拉施特拉邦和其他州之间的COVID-19严重程度趋势，马哈拉施特拉邦的死亡和病例数量最多。利用32个州/地区的每周和每月面板数据集的随机效应模型和Tobit模型，我们发现与COVID严重性相关的因素包括收入，性别，多发病率，城市化，锁定和解锁阶段，天气（包括温度和降雨）以及小麦的零售价格。从政策的角度简要观察一下，直到最后。