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控制快还是控制巧:何时应该控制入侵病原体?

Control fast or control smart: When should invading pathogens be controlled?

机构信息

Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, United Kingdom.

Department of Zoology, University of Oxford, Oxford OX1 3PS, United Kingdom.

出版信息

PLoS Comput Biol. 2018 Feb 16;14(2):e1006014. doi: 10.1371/journal.pcbi.1006014. eCollection 2018 Feb.

Abstract

The intuitive response to an invading pathogen is to start disease management as rapidly as possible, since this would be expected to minimise the future impacts of disease. However, since more spread data become available as an outbreak unfolds, processes underpinning pathogen transmission can almost always be characterised more precisely later in epidemics. This allows the future progression of any outbreak to be forecast more accurately, and so enables control interventions to be targeted more precisely. There is also the chance that the outbreak might die out without any intervention whatsoever, making prophylactic control unnecessary. Optimal decision-making involves continuously balancing these potential benefits of waiting against the possible costs of further spread. We introduce a generic, extensible data-driven algorithm based on parameter estimation and outbreak simulation for making decisions in real-time concerning when and how to control an invading pathogen. The Control Smart Algorithm (CSA) resolves the trade-off between the competing advantages of controlling as soon as possible and controlling later when more information has become available. We show-using a generic mathematical model representing the transmission of a pathogen of agricultural animals or plants through a population of farms or fields-how the CSA allows the timing and level of deployment of vaccination or chemical control to be optimised. In particular, the algorithm outperforms simpler strategies such as intervening when the outbreak size reaches a pre-specified threshold, or controlling when the outbreak has persisted for a threshold length of time. This remains the case even if the simpler methods are fully optimised in advance. Our work highlights the potential benefits of giving careful consideration to the question of when to start disease management during emerging outbreaks, and provides a concrete framework to allow policy-makers to make this decision.

摘要

对于入侵病原体,人们的直观反应是尽快开始疾病管理,因为这有望将疾病的未来影响降至最低。然而,随着疫情的发展,更多的传播数据会不断出现,病原体传播的过程几乎总是可以在疫情后期更准确地描述。这使得对任何疫情的未来发展都能更准确地预测,从而使控制干预措施能够更准确地靶向。也有可能疫情会自行消失,而无需任何干预,从而使预防性控制变得不必要。最佳决策需要不断平衡等待的潜在好处与进一步传播的可能成本。我们引入了一种通用的、可扩展的数据驱动算法,该算法基于参数估计和疫情模拟,用于实时决策何时以及如何控制入侵病原体。控制智能算法(CSA)解决了尽快控制和在获得更多信息后再控制之间的竞争优势的权衡问题。我们使用一个通用的数学模型来展示——该模型代表了农业动物或植物病原体通过农场或田地的种群传播——CSA 如何允许优化疫苗接种或化学控制的时机和水平。特别是,该算法的表现优于更简单的策略,例如在疫情规模达到预定阈值时进行干预,或者在疫情持续时间达到阈值时进行控制。即使提前对更简单的方法进行了完全优化,情况仍然如此。我们的工作强调了在新兴疫情期间仔细考虑何时开始疾病管理的问题的潜在好处,并提供了一个具体的框架,使决策者能够做出这一决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb52/5833286/e521b55aaf8e/pcbi.1006014.g001.jpg

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