Big Data Framework Offers Mapping Method for Future Pandemics

 A big data framework combining epidemiological and socioeconomic risk factors revealed that COVID-19 risk increased in areas with more crowding, population mobility, and morbidity, while risk decreased after the deployment of effective public health interventions.

Research conducted during and after less severe pandemics, like severe acute respiratory syndrome (SARS) and H1N1, has shown that there was a gap in how these illnesses were detected and treated among different populations. Socioeconomically disadvantaged populations were more significantly impacted by these diseases than their more affluent counterparts.

For their study, the research team identified and used seven specific socioeconomic and epidemiological factors – including healthcare access, health behavior, crowding, area morbidity, education, social distancing measures, and population mobility – using South Korean incidence data.

The team filtered the study’s variables by using a conceptual mechanistic framework that describes the potential causes of disparities in the US during a pandemic.