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- W3015057037 abstract "The epidemic curve and the final extent of the COVID-19 pandemic are usually predicted from the rate of early exponential raising using the SIR model. These predictions implicitly assume a full social mixing, which is not plausible generally. Here I am showing a counterexample to the these predictions, based on random propagation of an epidemic in Barabasi--Albert scale-free network models. The start of the epidemic suggests $R_0=2.6$, but unlike $Omegaapprox 70%{}$ predicted by the SIR model, they reach a final extent of only $Omegaapprox 4%{}$ without external mitigation and $Omegaapprox 0.5$--$1.5%{}$ with mitigation. Daily infection rate at the top is also 1--1.5 orders of magnitude less than in SIR models. Quarantining only the 1.5%{} most active superspreaders has similar effect on extent and top infection rate as blind quarantining a random 50%{} of the full community." @default.
- W3015057037 created "2020-04-10" @default.
- W3015057037 creator A5017030044 @default.
- W3015057037 date "2020-03-31" @default.
- W3015057037 modified "2023-09-27" @default.
- W3015057037 title "Propagation and mitigation of epidemics in a scale-free network" @default.
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- W3015057037 hasPublicationYear "2020" @default.
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