Mask policies & kids’ vaccines: What new data should be added to the Atlas?
By Qinyun Lin and Susan Paykin
Since the US Covid Atlas was launched in March 2020, we have extracted and integrated both dynamic and static data across multiple domains. For COVID health outcomes, we linked in multiple data sources for confirmed cases, confirmed deaths from multiple datasets including 1point3acres, John Hopkins University, USAFacts, and NYTimes. Each of these data sources has unique ways of data collection and case assignment to each county, so we provided all of them for users to explore. We also included vaccination data from CDC since they were available.
Now standing at the end of 2021, it has been almost two years since the pandemic started in the United States. Now that more pandemic-related data than ever is available to us, which ones should the Atlas prioritize? Below we point out two directions that our group has been thinking about.
Linking COVID-19 policy data: exploring what we can learn from changing policies
Various policies have been designed and implemented around the country (and the world) to slow down the spread of COVID-19, ranging from stay-at-home orders, school closure, to various types of travel bans. A burgeoning body of research is now examining the effects and impacts of all these policies. For example, a study that investigated large-scale anti-contagion policies across six countries presented evidence that such policies have reduced virus transmission substantially, with some policies having varying effects on different populations.
The existing datasets and the technical infrastructure embedded in the Atlas well prepared us to incorporate dynamic policy data and explore what we can learn in terms of what intervention works. As a starting point, we have been exploring one dataset provided by CDC: U.S. State and Territorial Public Mask Mandates From April 10, 2020, through August 15, 2020, by County by Day. As implied by the title, this dataset provides daily information regarding whether local residents were subject to “any state and territorial executive orders, administrative orders, resolutions, and proclamations for COVID-19 that require them to wear masks in public.” The spatial scale is at the county level, which aligns well with the Atlas. The duration of the dataset also spans over the majority of the first two years of the pandemic. Adding this dataset will allow us to explore historical COVID-19 trends within varying community contexts under different policy interventions.
Linking age-disaggregated vaccine data: understanding where we are in the pandemic
Vaccination rates are an important indicator of where we are in the pandemic. The temporal and spatial distribution of vaccination rates also helps us examine health inequities. The Atlas already incorporated two indicators for vaccination rates: % fully vaccinated (Figure 1) and % of at least one dose (Figure 2). But recently released data by CDC gives even richer information, allowing us to track vaccination status for different age groups within each county: younger than 12, 12–18, 18–65, and aged older than 65 years. Significantly, children ages 5–12 years were recently approved for vaccination by the CDC and vaccines are now widely available; data from this group may soon give us a better picture of the impact of COVID impact and transmission among children, including in schools. Along with other existing data in the Atlas especially those COVID-19 health outcomes and community context variables, the percent vaccinated for different age groups will present a more localized, refined story for where we are in the process of the pandemic and enable us to explore how vaccination may be associated with changes in COVID-19 cases and mortality rates, while accounting for spatial heterogeneity and dependence.
Any new datasets would be integrated with the Atlas’ existing data and spatial analysis features, such as hotspot analysis and dot density mapping. Intersections and impacts on different communities can be explored with the Atlas’ overlay features such as Native American reservations, hypersegregated cities, Black Belt counties, and US Congressional districts.
As we continue to review new datasets, we want to hear from you — what data would you like to see on the Atlas? Leave a comment below or tweet us with your ideas.
