Improving Epidemic Modeling with Networks
Ben R. Craig, Thomas Phelan, Jan-Peter Siedlarek, Jared Steinberg
Abstract
Many of the models used to track, forecast, and inform the response to epidemics such as COVID-19 assume that everyone has an equal chance of encountering those who are infected with a disease. But this assumption does not reflect the fact that individuals interact mostly within much narrower groups. We argue that incorporating a network perspective, which accounts for patterns of real-world interactions, into epidemiological models provides useful insights into the spread of infectious diseases.
Topics & Concepts
Coronavirus disease 2019 (COVID-19)Perspective (graphical)Computer scienceInfectious disease (medical specialty)Epidemic modelSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Data scienceEconometricsGeographyDiseaseEnvironmental healthMedicineEconomicsArtificial intelligencePopulationPathologyCOVID-19 epidemiological studiesComplex Network Analysis TechniquesOpinion Dynamics and Social Influence