Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures
Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures
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DOI：
10.1287/mnsc.2022.4318
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发表时间：
2022-05-01
影响因子：
5.4
通讯作者：
Feng, Yiding
中科院分区：
文献类型：
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作者：
Birge, John R.;Candogan, Ozan;Feng, Yiding
关于人口流动的数据有助于制定有针对性的政策对策，以遏制流行病的蔓延。然而，目前尚不清楚如何准确利用这些数据，以及它们对控制流行病有多大价值。为了探索这些问题，我们研究了一个明确考虑人口流动的空间流行病模型，并提出了一个优化框架，以获得在不同层次上限制城市不同社区经济活动的有针对性的政策。我们专注于COVID-19，并使用捕获个人的手机数据校准我们的模型？？纽约市（NYC）内的运动。我们使用这些数据来说明，当应用于减少一个重点地区的感染时，目标可以比统一的（全市范围的）政策允许更高的就业水平。在我们的纽约市示例中（重点关注2020年4月的疾病控制），我们的主要模型表明，适当的目标可以减少所有社区的感染，同时恢复23.1% - 42.4%的基线不可远程工作就业水平。相比之下，实现相同政策目标的统一限制政策允许的非远程就业减少了3.92 - 6.25倍。我们的优化框架表明，有可能以限制失业的经济成本为目标，同时遏制流行病的蔓延。
Data on population movements can be helpful in designing targeted policy responses to curb epidemic spread. However, it is not clear how to exactly leverage such data and how valuable they might be for the control of epidemics. To explore these questions, we study a spatial epidemic model that explicitly accounts for population movements and propose an optimization framework for obtaining targeted policies that restrict economic activity in different neighborhoods of a city at different levels. We focus on COVID-19 and calibrate our model using the mobile phone data that capture individuals??? movements within New York City (NYC). We use these data to illustrate that targeting can allow for substantially higher employment levels than uniform (city-wide) policies when applied to reduce infections across a region of focus. In our NYC example (which focuses on the control of the disease in April 2020), our main model illustrates that appropriate targeting achieves a reduction in infections in all neighborhoods while resuming 23.1%???42.4% of the baseline nonteleworkable employment level. By contrast, uniform restriction policies that achieve the same policy goal permit 3.92???6.25 times less nonteleworkable employment. Our optimization framework demonstrates the potential of targeting to limit the economic costs of unemployment while curbing the spread of an epidemic.