A contact tracing SIR model for randomly mixed populations.

IF 1.8 4区 数学 Q3 ECOLOGY Journal of Biological Dynamics Pub Date : 2022-12-01 DOI:10.1080/17513758.2022.2153938
Sam Bednarski, Laura L E Cowen, Junling Ma, Tanya Philippsen, P van den Driessche, Manting Wang
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Abstract

Contact tracing is an important intervention measure to control infectious diseases. We present a new approach that borrows the edge dynamics idea from network models to track contacts included in a compartmental SIR model for an epidemic spreading in a randomly mixed population. Unlike network models, our approach does not require statistical information of the contact network, data that are usually not readily available. The model resulting from this new approach allows us to study the effect of contact tracing and isolation of diagnosed patients on the control reproduction number and number of infected individuals. We estimate the effects of tracing coverage and capacity on the effectiveness of contact tracing. Our approach can be extended to more realistic models that incorporate latent and asymptomatic compartments.

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随机混合群体接触追踪SIR模型。
接触者追踪是控制传染病的重要干预措施。我们提出了一种新的方法,该方法借用网络模型的边缘动力学思想来跟踪流行病在随机混合人群中传播的分区SIR模型中包含的接触者。与网络模型不同,我们的方法不需要接触网络的统计信息,这些数据通常不容易获得。这种新方法建立的模型使我们能够研究接触者追踪和确诊患者隔离对控制繁殖数和感染个体数的影响。我们估计了追踪覆盖率和追踪能力对接触者追踪有效性的影响。我们的方法可以扩展到更现实的模型,包括潜伏和无症状室。
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来源期刊
Journal of Biological Dynamics
Journal of Biological Dynamics ECOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
4.90
自引率
3.60%
发文量
28
审稿时长
33 weeks
期刊介绍: Journal of Biological Dynamics, an open access journal, publishes state of the art papers dealing with the analysis of dynamic models that arise from biological processes. The Journal focuses on dynamic phenomena at scales ranging from the level of individual organisms to that of populations, communities, and ecosystems in the fields of ecology and evolutionary biology, population dynamics, epidemiology, immunology, neuroscience, environmental science, and animal behavior. Papers in other areas are acceptable at the editors’ discretion. In addition to papers that analyze original mathematical models and develop new theories and analytic methods, the Journal welcomes papers that connect mathematical modeling and analysis to experimental and observational data. The Journal also publishes short notes, expository and review articles, book reviews and a section on open problems.
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