Graph for Better Health: COVID Response
COVID Response focuses on using the wide array of publicly availabledata on COVID transmission and spread to help understand virus transmission and spread, with the hope of being able to predict and mitigate future infection spikes.
PROBLEM STATEMENT
COVID infections have occured in several waves of rapidly increasing and then declining infections, with hospitalizations and deaths lagging infections by predictable lags. Many explanations have been proposed over the last two years to explain these waves, including new variants, weather, vaccinations, public health measures, and changes in individual decision making and risk tolerance. All appear to play a factor, but the rise and fall of cases over time isn’t fully explained by any of these alone and we don’t have good insights into when the next wave will come, how long it will last, or how severe it will be. Several insights have come from analyzing the progression of these waves in different countries, or in different regions within a country (counties or zip codes within the US for example).
THE CHALLENGE
Develop a solution to analyze prior COVID waves and model the progression of new infections. This approach could inform policy makers to more proactively institute restrictions ahead of impending waves, and to remove restrictions that are unrelated to the real reasons for caseload increases or have simply outlived their usefulness. It could also be used by healthcare organizations to plan for capacity surges by rescheduling elective procedures, and by any organization potentially throwing a large gathering to have more insight into planning.
RESOURCES
- Use the many publicly available COVID resources to explore connections between these various factors and analyze how these waves have progressed, both geographically and over time.
- Read up on other approaches to this problem here
Dataset Example Resources:
- Johns Hopkins COVID data – Up to date, aggregated data on worldwide infections.
- CORD-19 – Computer readable scientific papers on COVID-19
