부산대학교 도서관

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. Author summary: Congregate setting such as prisons and nursing homes pose challenges for infection control. In these settings, vulnerable populations live in dense, highly connected communities. This allows explosive outbreaks of infection such as COVID-19. To reduce the probability of outbreaks occurring and the size of any outbreaks that occur, several control strategies are often available. These include vaccination, depopulation, improving ventilation, and quarantine of affected individuals. In this manuscript, we construct a mathematical model of outbreak dynamics and evaluate the relative effectiveness of different types of control strategies. The model quantifies three phases of outbreak dynamics: the rate of importation of an infection, the probability that an imported infection results in uncontrolled transmission, and the anticipated size of an outbreak caused by uncontrolled transmission. We find that control interventions that decrease both the susceptibility to infection and the transmissibility of affected individuals can have a greater than linear impact on the expected burden of disease. We also find there can be a benefit if residents are apportioned into distinct cohorts. Finally, we examine the relative benefit of preemptive strategies that are enacted prior to an outbreak occurring versus reactive strategies that are deployed once an outbreak has begun. [ABSTRACT FROM AUTHOR]