Cost Effective Reproduction Number Based Strategies for Reducing Deaths\n from COVID-19

Le résumé fourni par la source

In epidemiology, the effective reproduction number $R_e$ is used to\ncharacterize the growth rate of an epidemic outbreak. In this paper, we\ninvestigate properties of $R_e$ for a modified SEIR model of COVID-19 in the\ncity of Houston, TX USA, in which the population is divided into low-risk and\nhigh-risk subpopulations. The response of $R_e$ to two types of control\nmeasures (testing and distancing) applied to the two different subpopulations\nis characterized. A nonlinear cost model is used for control measures, to\ninclude the effects of diminishing returns. We propose three types of heuristic\nstrategies for mitigating COVID-19 that are targeted at reducing $R_e$, and we\nexhibit the tradeoffs between strategy implementation costs and number of\ndeaths. We also consider two variants of each type of strategy: basic\nstrategies, which consider only the effects of controls on $R_e$, without\nregard to subpopulation; and high-risk prioritizing strategies, which maximize\ncontrol of the high-risk subpopulation. Results showed that of the three\nheuristic strategy types, the most cost-effective involved setting a target\nvalue for $R_e$ and applying sufficient controls to attain that target value.\nThis heuristic led to strategies that begin with strict distancing of the\nentire population, later followed by increased testing. Strategies that\nmaximize control on high-risk individuals were less cost-effective than basic\nstrategies that emphasize reduction of the rate of spreading of the disease.\nThe model shows that delaying the start of control measures past a certain\npoint greatly worsens strategy outcomes. We conclude that the effective\nreproduction can be a valuable real-time indicator in determining\ncost-effective control strategies.\n

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