Applying Machine Learning Techniques to Mitigate Impact of COVID-19 Pandemic

Abstract

Since March 2020, COVID-19 has played a very influential role in our lives. Totaling over 300 million cases and 5.5 million deaths worldwide it has been one of the most transmittal viruses humans have seen in recent generations. Even after the mass distribution of vaccines, COVID-19 shows no signs of stopping. This is because many communities that are especially struggling during this time period have not been identified and are not being helped adequately enough. By better understanding how different factors in communities such as ethnic percentages, poverty rates and much more can help us determine which communities need to be addressed to slow the spread of COVID-19. To identify the most significant of these demographic factors an in depth data analysis using machine learning models and regression analysis were carried out on various datasets. The results highlighted that for COVID-19 cases the most influential factor was Population Density. For deaths, the most significant factors were poverty rates in communities as well as education level. From this analysis and results, in order to mitigate the impact of the COVID-19 pandemic in the future it is of utmost importance to address the needs of underprivileged communities by providing access to low cost and high quality medical resources for all.