University of California San Francisco, Francis I. Proctor Foundation, San Francisco, California, United States of America.
Modeling Infectious Diseases in Healthcare Network, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
PLoS Comput Biol. 2022 Jul 20;18(7):e1010308. doi: 10.1371/journal.pcbi.1010308. eCollection 2022 Jul.
The explosive outbreaks of COVID-19 seen in congregate settings such as prisons and nursing homes, has highlighted a critical need for effective outbreak prevention and mitigation strategies for these settings. Here we consider how different types of control interventions impact the expected number of symptomatic infections due to outbreaks. Introduction of disease into the resident population from the community is modeled as a stochastic point process coupled to a branching process, while spread between residents is modeled via a deterministic compartmental model that accounts for depletion of susceptible individuals. Control is modeled as a proportional decrease in the number of susceptible residents, the reproduction number, and/or the proportion of symptomatic infections. This permits a range of assumptions about the density dependence of transmission and modes of protection by vaccination, depopulation and other types of control. We find that vaccination or depopulation can have a greater than linear effect on the expected number of cases. For example, assuming a reproduction number of 3.0 with density-dependent transmission, we find that preemptively reducing the size of the susceptible population by 20% reduced overall disease burden by 47%. In some circumstances, it may be possible to reduce the risk and burden of disease outbreaks by optimizing the way a group of residents are apportioned into distinct residential units. The optimal apportionment may be different depending on whether the goal is to reduce the probability of an outbreak occurring, or the expected number of cases from outbreak dynamics. In other circumstances there may be an opportunity to implement reactive disease control measures in which the number of susceptible individuals is rapidly reduced once an outbreak has been detected to occur. Reactive control is most effective when the reproduction number is not too high, and there is minimal delay in implementing control. We highlight the California state prison system as an example for how these findings provide a quantitative framework for understanding disease transmission in congregate settings. Our approach and accompanying interactive website (https://phoebelu.shinyapps.io/DepopulationModels/) provides a quantitative framework to evaluate the potential impact of policy decisions governing infection control in outbreak settings.
在监狱和养老院等聚集场所爆发的 COVID-19 疫情凸显了这些场所需要采取有效的疫情预防和缓解策略。在这里，我们考虑了不同类型的控制干预措施如何影响因疫情爆发而导致的有症状感染的预期数量。将疾病从社区引入居民群体的过程建模为一个与分支过程耦合的随机点过程，而居民之间的传播则通过一个确定性的隔室模型来建模，该模型考虑了易感个体的消耗。控制通过比例降低易感居民数量、繁殖数和/或有症状感染比例来建模。这允许对传播的密度依赖性和疫苗接种、人口减少和其他类型的控制的保护模式进行一系列假设。我们发现，接种疫苗或人口减少对预期病例数的影响可能大于线性。例如，假设繁殖数为 3.0，且具有密度依赖性传播，我们发现，通过预先将易感人群的规模减少 20%，总体疾病负担降低了 47%。在某些情况下，通过优化一组居民分配到不同居住单元的方式，可能会降低疾病爆发的风险和负担。最佳分配可能因目标是降低爆发发生的概率还是降低爆发动力学引起的病例数而异。在其他情况下，一旦发现爆发，可能有机会实施反应性疾病控制措施，迅速减少易感个体数量。当繁殖数不太高且实施控制的延迟最小化时，反应性控制最为有效。我们以加利福尼亚州监狱系统为例，说明了这些发现如何为理解聚集场所的疾病传播提供了定量框架。我们的方法和附带的交互式网站（https://phoebelu.shinyapps.io/DepopulationModels/）提供了一个定量框架，用于评估控制传染病在爆发场所的政策决策的潜在影响。
