COVID-19 Spread: A Demographic Analysis

Abstract

The relationship of COVID-19 cases with growth rate, literacy, and other data points that describe people of a country or their living standards, might be insightful in making predictions and decisions in the long run. Leading Health Boards across the world have analyzed various related statistics with a conclusion that COVID-19 infection is going to stay here for some upcoming time. Making a thoughtful and informed decision after analyzing the situation is much better than doing trial and errors and playing with the health of people, thus, to make a relationship analysis between growth and development parameters of the country to the cases and deaths due to virus reported by that country, following main data points were collected for different countries across the globe: Literacy Rate, GDP, Percentage of GDP spent on Health, Total number of Corona Cases reported, Total Cases per million of population, Population density, Gross Income per capita, Number of internet users, Total Deaths due to COVID-19 Virus, Percentage of population below poverty line (BPL), and Health workers density. Work presents a relational demographic analysis with K-means clustering on parameters mentioned to establish a correlation between infection spread and associated parameters, with certain exceptions and reasons for it. The work proposed clearly outlines under-rated parameters that potentially impact the spread of COVID-19 spread, majorly low literacy, machine-dependent lifestyle, low economic stability, high population density, large migrants, limited healthcare infrastructure, and less gross national income per capita raised insecurities and contributed to infection spread. However, exceptions to the above exist, citing reasons such as stringent measures such as complete lockdown, environmental conditions, and effectiveness of diversified vaccinations already existing on COVID-19 such as BCG, all under research.