Restrictive policies

Mathematical modelling is broadly used to justify restrictive measures implemented to prevent infection transmission and ultimately death. Some studies recently published show insufficient data, forgetting to include the sudden rise in the number of infections expected after the lifting of measures.

“Once transmission rates return to normal, the epidemic will proceed largely as it would have without mitigations, unless a significant fraction of the population is immune (either because they have recovered from the infection or because an effective vaccine has been developed), or the infectious agent has been completely eliminated, without risk of reintroduction.”

– Wesley Pegden, associate professor, Department of mathematical Sciences, Carnegie Mellon University

Maria Chikina, Wesley Pegden, A call to honesty in pandemic modeling, Medium Magazine, March 29, 2020

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Different strategies are employed worldwide to face the reality of hospital and intensive care unit (ICU) restricted capacity. China, for instance, did not wait long to implement restrictive measures, like city lockdowns and school closures. On the other hand, England first believed that being too restrictive too early would lead to a large second epidemic once measures were lifted. They finally applied restrictive measures after a sudden rise of infected cases.

Behind these different defensible measures lie mathematical computer simulation models applied to the epidemiology of infectious disease. These mathematical models were used to model the Ebola and Zika viruses in the past.

There is still debate among scientists about which mathematical model most accurately represents characteristics of SARS-CoV-2 and the affected population, and about the scope of such models within government authority restriction plans.

Martin Enserink, Kai Kupferschmidt, Mathematics of life and death: How disease models shape national shutdowns and other pandemic policies, Science magazine, March 25, 2020

Original article :