Optimal reduced-mixing for an SIS infectious-disease model.

IF 2.7 4区 数学 Q3 ECOLOGY
Erin Stafford, Mark Kot
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引用次数: 0

Abstract

Which reduced-mixing strategy maximizes economic output during a disease outbreak? To answer this question, we formulate an optimal-control problem that maximizes the difference between revenue, due to healthy individuals, and medical costs, associated with infective individuals, for SIS disease dynamics. The control variable is the level of mixing in the population, which influences both revenue and the spread of the disease. Using Pontryagin's maximum principle, we find a closed-form solution for our problem. We explore an example of our problem with parameters for the transmission of Staphylococcus aureus in dairy cows, and we perform sensitivity analyses to determine how model parameters affect optimal strategies. We find that less mixing is preferable when the transmission rate is high, the per-capita recovery rate is low, or when the revenue parameter is much smaller than the cost parameter.

SIS传染病模型的最优减少混合。
在疾病暴发期间,哪种减少混合策略能使经济产出最大化?为了回答这个问题,我们制定了一个最优控制问题,该问题最大限度地提高了SIS疾病动态中健康个体的收入与感染个体的医疗成本之间的差异。控制变量是人口的混合水平,它影响收入和疾病的传播。利用庞特里亚金的极大值原理,我们找到了问题的封闭解。我们以金黄色葡萄球菌在奶牛中传播的参数为例进行了研究,并进行了敏感性分析,以确定模型参数如何影响最优策略。我们发现,当传输率较高、人均回收率较低或收益参数远小于成本参数时,减少混合是可取的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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