STARS-CoV-2| Human Factors

Human Factors

The emergence and spread of infectious diseases, like COVID-19, are on the rise. Can you identify patterns between population density and COVID-19 cases and identify factors that could help predict hotspots of disease spread?

Correcting population density data for analysis of COVID-19 cases

Summary

After proving that there is in fact a statistically significant correlation between the population density and the COVID-19 cases of a country we decided to try to improve on it by finding a better way to calculate population density using satellite imagery.

How We Addressed This Challenge

Our project was mostly focused on finding a way to get a better understanding of population density data using satellite imagery. Calculating truly how densely populated a country is could provide invaluable information on which countries could be considered hotspots for the SARS-CoV-2 and/or other viruses.

How We Developed This Project

We decided to tackle this challenge as we thought that a good result in it would have a great impact on how we understand this and other viruses. We worked step by step, first proving something that was already known  (that there is in fact a correlation between population density and COVID-19)  and afterwards trying to improve on that result using satellite imagery. The little code we had to write was written in jupyter notebooks using python. Our biggest problem in the beginning was finding a way to isolate a single country from Worldview and getting only the data we needed. After that we also faced a big roadblock in finding good threshold values to analyze our images with and that is unfortunately where our research came to an end.

Global Judging
This project was submitted for consideration during the Space Apps Global Judging process.