The research summary is a brief summary of interesting academic work.
The great idea
Publicly available statistics on population demographics and culture can help governments prepare for the next pandemic. We found that by using existing sociodemographic data from the first COVID-19 hotspots, where there was a lot of information, officials could have predicted how COVID-19 would spread across society. The next time there is a global health crisis, governments can use our techniques to find out how a disease is likely to move beyond the hotspots to regions that are not yet affected.
With a computational social scientist ia librarian for research in science, technology and mathematics, we study the sociocultural drivers of public health crises, such as obesity. In two peer-reviewed articles we published in early 2021, which are based on our previous research, we analyzed these engines. at the scale of U.S. counties i at the scale of nations. Both studies related sociocultural variables to the impact of COVID-19.
For our study in the U.S., we collected data from 3,088 U.S. counties on 31 factors that could affect the spread of COVID-19. These factors included population and ethnicity density, commuting habits, voting patterns, social connectivity, underlying health conditions, and economic information. We have collected this information from the United States Census Bureau and several other sources.
Using these factors, we constructed a predictive model of COVID-19 prevalence. We found that only five risk factors can predict between 47% and 60% of COVID-19 prevalence variation in U.S. counties: population size, population density, public transportation, voting patterns, and percentage of African American population. We validated our model by showing that counties that reported fewer COVID-19 cases in April than expected in our model tend to have more cases in July. The results thus provide a new way of discerning when an American county does not report the actual number of infections present in the community.
In the second paper, we tried to explain why certain countries, such as the US, have death tolls in the hundreds of thousands, while other nations had very few deaths. Using international data from great pollmeasuring cultural values in 88 countries, we found that demographic factors such as population size and obesity levels were important. But, more surprisingly, we found that culture was also important, as open and tolerant societies, as well as those with little confidence in institutions, tended to suffer the worst.
This analysis made some surprising predictions about the spread of COVID-19 around the world. For example, while in early 2020 many believed that African countries would be severely affected by COVID-19, our model predicted not. So far this has been true.
COVID-19 has been severely affected in the United States, which has scored high on many of the sociocultural risk factors, including low confidence in institutions, high tolerance of minorities, and high levels of obesity. Almost 583,000 people in the US had died from COVID-19 as of May 12, 2021. This is the absolute highest number of deaths of any nation to date, and approximately 17.5% of global virus deaths, in a country where only 4% of the population lives. world population.
Why is it important?
Governments struggle to predict and plan the location and extent of disease outbreaks. With so many moving parts, from local mandates like economic shutdowns and facial mask recommendations, to international travel bans or restrictions, it seems almost impossible to project the number of cases in different counties or regions. In the middle week, how many cases could you expect to have? Should the United States expect more cases than Ghana? Why can one city or region be hit harder than another?
We show that additional planning based on cultural and demographic factors can help predict how outbreaks might progress. It can also reveal which people may be most vulnerable. Properly applied, this data-driven approach can save hundreds of thousands of lives when the next pandemic hits.
What is not yet known
Our goal is to use the predictive power of demographic and cultural data to anticipate the spread of future pandemics. But none of our studies specify a cause-and-effect relationship.
For example, when looking at the U.S., one of the five predictors is the proportion of the African American population: higher proportions predicted higher infection and mortality rates. Our analysis, however, did not determine whether this factor could subsume many other truly causal factors. The social sciences and public health literature raises reasons why African American populations have suffered more from COVID-19, including larger households, underlying health conditions, and a tendency to work in sectors at higher risk of exposure.