Department of Mathematics and Statistics, McMaster University, Hamilton, Canada.
Department of Mathematics and Statistics, University of Ottawa, Ottawa, Canada.
Bull Math Biol. 2022 May 13;84(6):66. doi: 10.1007/s11538-022-01018-2.
Testing individuals for pathogens can affect the spread of epidemics. Understanding how individual-level processes of sampling and reporting test results can affect community- or population-level spread is a dynamical modeling question. The effect of testing processes on epidemic dynamics depends on factors underlying implementation, particularly testing intensity and on whom testing is focused. Here, we use a simple model to explore how the individual-level effects of testing might directly impact population-level spread. Our model development was motivated by the COVID-19 epidemic, but has generic epidemiological and testing structures. To the classic SIR framework we have added a per capita testing intensity, and compartment-specific testing weights, which can be adjusted to reflect different testing emphases-surveillance, diagnosis, or control. We derive an analytic expression for the relative reduction in the basic reproductive number due to testing, test-reporting and related isolation behaviours. Intensive testing and fast test reporting are expected to be beneficial at the community level because they can provide a rapid assessment of the situation, identify hot spots, and may enable rapid contact-tracing. Direct effects of fast testing at the individual level are less clear, and may depend on how individuals' behaviour is affected by testing information. Our simple model shows that under some circumstances both increased testing intensity and faster test reporting can reduce the effectiveness of control, and allows us to explore the conditions under which this occurs. Conversely, we find that focusing testing on infected individuals always acts to increase effectiveness of control.
对个体进行病原体检测会影响传染病的传播。了解个体层面的采样和报告检测结果的过程如何影响社区或人群层面的传播，是一个动力学建模问题。检测过程对疫情动态的影响取决于实施的基础因素，特别是检测强度以及检测的重点对象。在这里，我们使用一个简单的模型来探索检测的个体层面效应如何直接影响人群层面的传播。我们的模型开发受到了 COVID-19 疫情的启发，但具有通用的流行病学和检测结构。我们在经典的 SIR 框架中加入了人均检测强度和特定隔间的检测权重，可以根据不同的检测重点（监测、诊断或控制）进行调整。我们推导出了由于检测、检测报告和相关隔离行为导致基本繁殖数相对减少的解析表达式。密集的检测和快速的检测报告有望在社区层面带来益处，因为它们可以快速评估情况、识别热点，并可能实现快速的接触者追踪。快速检测对个体层面的直接影响不太明确，这可能取决于个体的行为如何受到检测信息的影响。我们的简单模型表明，在某些情况下，增加检测强度和加快检测报告都可能降低控制的效果，并且使我们能够探索出现这种情况的条件。相反，我们发现将检测重点放在感染个体上总是可以提高控制效果。
