Modeling the spread of infectious diseases: Is there a better way?
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.
In this Economic Commentary, Cleveland Fed researchers 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.
The commonly used model “implicitly makes assumptions about the pattern of interactions among individuals that are unlikely to hold true in the real world,” say the researchers. “More importantly, we have demonstrated how incorporating some commonly observed network patterns, such as heterogeneity in the number of contacts individuals have and the clustering of contacts, can change the behavior of the model in important ways, affecting the speed of the disease spread, long-run health outcomes, and the effects of the disease on economic activity.”
By accounting for the patterns of interaction among a population, a network model offers additional nuance for the analysis of the economic effects of COVID-19 and better informs the discussion of targeted policies such as contact tracing and testing that have been successfully used to help combat infectious diseases.
Read more: Improving Epidemic Modeling with Networks
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