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通过即时释放控制虫媒传染病爆发。

Vector-borne disease outbreak control via instant releases.

机构信息

Laboratoire J.-L. Lions, Sorbonne Université CNRS, Université de Paris, Inria, 75005, Paris, France.

Centre d'Estudis Avançats de Blanes (CEAB-CSIC), Carrer d'Accés a la cala Sant Francesc 14, 17300, Blanes, Spain.

出版信息

J Math Biol. 2024 Nov 12;89(6):63. doi: 10.1007/s00285-024-02159-9.

Abstract

This paper is devoted to the study of optimal release strategies to control vector-borne diseases, such as dengue, Zika, chikungunya and malaria. Two techniques are considered: the sterile insect one (SIT), which consists in releasing sterilized males among wild vectors in order to perturb their reproduction, and the Wolbachia one (presently used mainly for mosquitoes), which consists in releasing vectors, that are infected with a bacterium limiting their vectorial capacity, in order to replace the wild population by one with reduced vectorial capacity. In each case, the time dynamics of the vector population is modeled by a system of ordinary differential equations in which the releases are represented by linear combinations of Dirac measures with positive coefficients determining their intensity. We introduce optimal control problems that we solve numerically using ad-hoc algorithms, based on writing first-order optimality conditions characterizing the best combination of Dirac measures. We then discuss the results obtained, focusing in particular on the complexity and efficiency of optimal controls and comparing the strategies obtained. Mathematical modeling can help testing a great number of scenarios that are potentially interesting in future interventions (even those that are orthogonal to the present strategies) but that would be hard, costly or even impossible to test in the field in present conditions.

摘要

本文致力于研究最优释放策略以控制蚊媒传染病,如登革热、寨卡、基孔肯雅热和疟疾。两种技术被考虑:一是不育昆虫技术(SIT),它包括在野外传播媒介中释放不育雄性,以干扰其繁殖;二是沃尔巴克氏体技术(目前主要用于蚊子),它包括释放受感染的细菌以限制其传播能力的传播媒介,以用传播能力降低的种群取代野生种群。在每种情况下,通过一个带有正系数的线性组合的狄拉克测度来表示释放量,该系数确定其强度,用一个常微分方程组来模拟媒介种群的时间动态。我们引入了最优控制问题,并使用基于一阶最优性条件的特定算法来求解,这些条件可以刻画狄拉克测度的最佳组合。然后,我们讨论了所得到的结果,特别关注最优控制的复杂性和效率,并比较了所得到的策略。数学建模可以帮助测试大量在未来干预中可能有趣的场景(甚至与当前策略正交的场景),但在当前条件下,在现场进行测试可能很难、成本很高甚至不可能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0835/11557670/daa412c8b061/285_2024_2159_Fig1_HTML.jpg

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