Department of Microbiology and Immunology, Montana State University, Bozeman, MT, USA.
Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA.
Philos Trans R Soc Lond B Biol Sci. 2019 Sep 30;374(1782):20190224. doi: 10.1098/rstb.2019.0224. Epub 2019 Aug 12.
Disease emergence events, epidemics and pandemics all underscore the need to predict zoonotic pathogen spillover. Because cross-species transmission is inherently hierarchical, involving processes that occur at varying levels of biological organization, such predictive efforts can be complicated by the many scales and vastness of data potentially required for forecasting. A wide range of approaches are currently used to forecast spillover risk (e.g. macroecology, pathogen discovery, surveillance of human populations, among others), each of which is bound within particular phylogenetic, spatial and temporal scales of prediction. Here, we contextualize these diverse approaches within their forecasting goals and resulting scales of prediction to illustrate critical areas of conceptual and pragmatic overlap. Specifically, we focus on an ecological perspective to envision a research pipeline that connects these different scales of data and predictions from the aims of discovery to intervention. Pathogen discovery and predictions focused at the phylogenetic scale can first provide coarse and pattern-based guidance for which reservoirs, vectors and pathogens are likely to be involved in spillover, thereby narrowing surveillance targets and where such efforts should be conducted. Next, these predictions can be followed with ecologically driven spatio-temporal studies of reservoirs and vectors to quantify spatio-temporal fluctuations in infection and to mechanistically understand how pathogens circulate and are transmitted to humans. This approach can also help identify general regions and periods for which spillover is most likely. We illustrate this point by highlighting several case studies where long-term, ecologically focused studies (e.g. Lyme disease in the northeast USA, Hendra virus in eastern Australia, Plasmodium knowlesi in Southeast Asia) have facilitated predicting spillover in space and time and facilitated the design of possible intervention strategies. Such studies can in turn help narrow human surveillance efforts and help refine and improve future large-scale, phylogenetic predictions. We conclude by discussing how greater integration and exchange between data and predictions generated across these varying scales could ultimately help generate more actionable forecasts and interventions. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.
疾病的出现、流行和大流行都突显了预测人畜共患病病原体溢出的必要性。由于跨物种传播本质上是分层的，涉及在不同的生物组织水平上发生的过程，因此，由于预测所需的数据的许多尺度和广度，这些预测工作可能会变得复杂。目前有广泛的方法用于预测溢出风险（例如，宏观生态学、病原体发现、人群监测等），每种方法都受限于特定的预测的进化、空间和时间尺度。在这里，我们将这些不同的方法置于其预测目标和预测的相应尺度内，以说明概念和实践上的重叠的关键领域。具体来说，我们专注于生态视角，设想一个研究渠道，该渠道将不同尺度的数据和预测从发现目的连接到干预目的。在进化尺度上进行的病原体发现和预测可以首先为哪些储主、媒介和病原体可能参与溢出提供粗略的基于模式的指导，从而缩小监测目标和应进行这些努力的地点。接下来，可以对储主和媒介进行生态驱动的时空研究，以量化感染的时空波动，并从机制上了解病原体如何循环并传播给人类。这种方法还可以帮助确定最有可能发生溢出的一般地区和时期。我们通过强调几个案例研究来说明这一点，这些研究都是长期的、以生态为重点的研究（例如，美国东北部的莱姆病、澳大利亚东部的亨德拉病毒、东南亚的疟原虫 knowlesi），这些研究有助于预测空间和时间上的溢出，并有助于设计可能的干预策略。此类研究反过来可以帮助缩小人类监测工作的范围，并有助于完善和改进未来的大规模、进化预测。最后，我们讨论了在这些不同尺度之间生成的数据和预测之间更大的整合和交流如何最终有助于生成更具可操作性的预测和干预措施。本文是主题为“理解病原体溢出的动态和综合方法”的特刊的一部分。
