The First Wave of COVID-19 in India: Demographic and Economic Analysis

Rohan S. Kulkarni
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Abstract

This paper provides a comprehensive overview of COVID-19 related deaths within India over the first eight months of 2020 for two different Kaggle data sets. Analyzing first data set provided by the Kaggle for the period included Indian Nationality, states, and counts for total cases, deaths, and cured demonstrated that the states are statistically significant in a regression model. Furthermore, the second Kaggle data set provided by the Kaggle for the period for age, gender, nationality, and all states in the country, I drew conclusions concerning correlations between COVID-19 deaths and the four factor categories and found that the overall logistics regression model was statistically significant. The studyconcluded that within the first eight months of 2020, the both sexes are affected equally by the virus while age and states of residence play important roles in life and death due to the virus. Higher urban populated states with higher GDP creation have seen highest virus related deaths and may explain the forced avoidance of social distancing effect.
2019冠状病毒病在印度的第一波:人口和经济分析
本文针对两个不同的Kaggle数据集,全面概述了2020年前8个月印度境内与COVID-19相关的死亡情况。分析由Kaggle提供的第一个数据集,包括印度国籍、各邦,以及总病例数、死亡人数和治愈人数,表明各邦在回归模型中具有统计显著性。此外,通过Kaggle提供的关于年龄、性别、国籍和该国所有州的第二个Kaggle数据集,我得出了关于COVID-19死亡与四个因素类别之间相关性的结论,并发现整体logistic回归模型具有统计学意义。该研究得出的结论是,在2020年的前8个月内,男女都受到该病毒的影响,而年龄和居住地在该病毒导致的生死中起着重要作用。城市人口较多、GDP创造较高的州,与病毒相关的死亡人数最高,这可能解释了被迫避免社交距离效应的原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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