Science Magazine May 15, 2020
According to researchers at the University of Chicago, mathematical modeling and historical influenza pandemics provide a warning about comparing the effects of interventions in different populations. A rapid decline in COVID-19 cases or a small springtime epidemic might be taken as evidence that interventions have been especially effective or that herd immunity has been achieved. But simple models show that epidemic dynamics become deeply unintuitive when there is seasonal variation in susceptibility or transmission, and especially when there is movement between populations. For SARS-CoV-2, like influenza virus, the shape of seasonal variation is uncertain. Linear correlations could lead to spurious causal inferences about which interventions work best and should not be overinterpreted. The effectiveness of targeted, less socially disruptive control measures depends critically on the biological parameters of the pathogen…read more.
Potential pandemic size and herd immunity. Credit: Science 15 May 2020: Vol. 368, Issue 6492, pp. 713-714