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COVID-19 peaks reflect time-dependent social activity, not herd immunity

Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign Scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and the University of Illinois Urbana-Champaign (UIUC) have developed a new mathematical model for predicting how epidemics such as COVID-19 spread. This model not only accounts for individuals’ varying biological susceptibility to infection but also their levels of social activity, which naturally change over time. Using their model, the team showed that a temporary state of collective immunity–which they termed “transient collective immunity”–emerged during the early, fast-paced stages of the epidemic. However, subsequent “waves,” or surges in the number of cases, are predicted to appear because of changing social behaviors due to pandemic fatigue or variations in imposed mitigations. Their results appeared online on April 8, 2021 in advance of publication in the Proceedings of the National Academ

University of Illinois: Suppression of COVID-19 peaks reflect time-dependent social activity, not herd immunity

Share Scientists developed a model showing that a fragile, temporary state of immunity emerged during the early epidemic but got destroyed as people changed their social behaviors over time, leading to future waves of infection Scientists modeling the spread of COVID-19 showed that a temporary state of immunity arises due to individual differences in social behaviors. This ‘transient collective immunity’ referring to when the susceptible or more social groups collectively have been infected&mdashgets destroyed as people modify their social behaviors over time. For example, someone who isolated in the early days of the epidemic may at some point renew their social networks, meeting with small groups or large crowds.

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