The big idea
Publicly available statistics about population demographics and culture can help governments prepare for the next pandemic. We have found that by using existing socio-demographic data from early COVID-19 hot spots, where there was a lot of information, officials could have predicted how COVID-19 would spread through society. The next time there is a global health crisis governments can use our techniques to figure out how a disease will likely move beyond hot spots to regions that are not yet affected.

With a computational social scientist and a librarian for science, technology and mathematics research, we study the socio-cultural drivers of public health crises, such as obesity. In two peer-reviewed papers that we published in early 2021, which build on our previous research, we analyzed these drivers at the scale of U.S. counties and at scale of nations. Both studies connected socio-cultural variables to the impact of COVID-19.

For our U.S. study, we collected data from 3,088 U.S. counties on 31 factors that could affect the spread of COVID-19. These factors included population density and ethnicity, commuting habits for work, voting patterns, social connectivity, underlying health conditions and economic information. We collected this information from the U.S. Census Bureau and a variety of other sources.

Using these factors, we built a predictive model of COVID-19 prevalence. We found that just five risk factors can predict between 47% and 60% of variation in COVID-19 prevalence in U.S. counties: population size, population density, public transport, voting patterns and percent African American population. We validated our model by showing that counties which reported fewer COVID-19 cases in April than expected in our model tended to have more cases in July. The results thus provide a new way of discerning when a U.S. county is under-reporting the actual number of infections present in the community.

https://theconversation.com/how-to-use-statistics-to-prepare-for-the-next-pandemic-157763