anmorgan24 / covid-weather-capstone

Predictive modeling of COVID-19 infection rates using weather variables in Massachusetts, Connecticut, Vermont, and Maine

COVID-Weather-Capstone

Predictive Modeling of COVID-19 Infection Rates Using Weather Variables in Massachusetts, Connecticut, Vermont, and Maine

Photo by Fusion Medical Animation on Unsplash

Weather and climate are believed to play a significant role in the transmission of infectious diseases, and their relationships with COVID-19 have become a focal point of research since the onset of the pandemic. From an epidemiological perspective, weather variables can affect virus spread by influencing transmission dynamics, host susceptibility, and virus survival rates. From a behavioral perspective, weather often has big implications on social distancing, mobility levels, and frequency and location of social gatherings. The goal of this project is to further examine this relationship between weather variables and COVID-19 in order to better define the seasonlity of COVID-19 transmission, as well as to help develop early warning signs and inform outbreak control.

This project was completed as the first of two independent capstone projects for Springboard's Data Science Program. All weather data for this project was gathered from NOAA's National Centers for Environmental Information Database. All COVID19-related data was gathered from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University.