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

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.面对入侵病原体的直观应对策略是尽快启动疫病防控，以期最大限度降低疫病未来造成的影响。然而随着疫情发展，更多传播数据会逐步涌现，病原体传播的底层机制通常可在疫情后期得到更为精准的刻画。这使得我们能够更准确地预测疫情的未来走向，从而更精准地靶向实施防控干预措施。此外，疫情还有可能在无任何干预的情况下自行消散，此时预防性防控便无必要。最优决策需要在等待带来的潜在收益与疫情进一步扩散的潜在代价之间持续权衡。我们提出了一种基于参数估计与疫情模拟的通用可扩展数据驱动算法，用于实时决策入侵病原体的防控时机与方式。智能防控算法（Control Smart Algorithm, CSA）解决了尽快开展防控与待获取更多信息后再实施防控这两种互斥优势间的权衡难题。我们借助一个通用数学模型展开验证——该模型刻画了农业动植物病原体在农场或田块种群中的传播过程，展示了CSA如何优化疫苗接种或化学防控的部署时机与强度。特别地，该算法的表现优于两类更简单的策略：一类是当疫情规模达到预设阈值时实施干预，另一类是当疫情持续时长达到阈值时启动防控。即便预先对这两类简单策略进行了全面优化，CSA的性能依然更胜一筹。本研究凸显了在新发疫情中审慎考量疫病防控启动时机的潜在价值，并为政策制定者提供了可落地的决策框架。