Machine Learning Application in Health

TL;DR

This paper explores how machine learning can analyze the impact of flight patterns on COVID-19 cases and deaths in the Washington DC area, highlighting the role of air travel in disease spread.

Contribution

It applies machine learning techniques to assess the relationship between flight volume and COVID-19 outcomes in a specific region.

Findings

Higher flight numbers correlate with increased cases.

Flight data can predict COVID-19 spread patterns.

Insights aid in public health decision-making.

Abstract

Coronavirus can be transmitted through the air by close proximity to infected persons. Commercial aircraft are a likely way to both transmit the virus among passengers and move the virus between locations. The importance of learning about where and how coronavirus has entered the United States will help further our understanding of the disease. Air travelers can come from countries or areas with a high rate of infection and may very well be at risk of being exposed to the virus. Therefore, as they reach the United States, the virus could easily spread. On our analysis, we utilized machine learning to determine if the number of flights into the Washington DC Metro Area had an effect on the number of cases and deaths reported in the city and surrounding area.