The ongoing COVID-19 pandemic has infected 30.6 million people and killed 953,000 people worldwide. The United States has had one of the highest infection rates with 8.14 million people infected and 220,000 people killed as of October 2020.
As scientists rush to create a vaccine/find a cure for this highly infectious disease, people have been encouraged to practice “social distancing” to mitigate the spread of the virus in an effort to maximize the care that infected patients receive in the healthcare system. Methods of social distancing include staying 6 feet apart from people outside one's household both indoors and outdoors, increasing space between individuals, and decreasing frequency of contact between individuals.
As interest in the virus has dwindled worldwide and people have gotten bored of quarantining, more and more people have started venturing out of their homes. Today, more than six months into the pandemic, countless colleges, schools, gyms, and other public facilities are open across the country. While these places encourage safety measures such as social distancing and wearing masks, these measures are difficult to enforce on such a large scale (on college campuses, for example). This has raised the question of whether social distancing and travel restrictions are actually helping.
Researchers’ epidemiological models of COVID-19 transmission and outcomes have caused no small amount of controversy in the media due to their direct influence on public health policy. Underlying such models are assumptions about how epidemics propagate through local communities and how embedded social roots have contributed to transmission mechanisms. The exploration and assessment of these assumptions can provide key insight into the importance of different preventative measures to curtail rises in morbidity and mortality.
Big trends spanning a great nation typically have root causes, so why haven't researchers been able to find the equivalent of a smoking gun? Are they looking at the right independent variables and interactions? Are they able to think creatively about the key factors influencing the attitudes and behaviors of different age groups?
To assist in answering this main question, we’ve prepared several county-level policy datasets as well as various social mobility datasets. We invite you to explore the data source we have provided to 1) understand the underlying predictors of COVID-19 transmission and mortality and 2) further analyze the progression of the pandemic related to social changes and policy nationwide.
As a starting point, we ask you to consider the following questions when designing your experiment and interpreting your findings: