The problem of scale in the prediction and management of pathogen spillover

Abstract

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'.

Keywords: cross-species transmission; macroecology; mechanistic; pathogen discovery; surveillance; zoonosis.

Figure 1.

Interplay between scales of pathogen spillover prediction and a proposed pipeline for their integration. Even coarse phylogenetic predictions (a) can narrow the scope of what reservoirs and vectors for a given set of pathogens, and in which geographical regions, should be prioritized for surveillance (b); examples based on trait-based or cladistic analyses include filoviruses in Neotropical bats [36], zoonotic pathogens in European rodents [11] and helminths in Old World primates [37]. Spatio-temporal studies can next elucidate how zoonotic pathogens circulate in reservoir or vector populations, identify broad spatial and temporal scales at which pathogen pressure (e.g. shedding) is greatest and uncover the ecological mechanisms leading to spillover (c). Based on these regions and times where risk is greatest (circles), managers can design preemptive interventions and prioritize human surveillance. Data from spatio-temporal studies can further address information gaps and refine future macroecological analyses and predictions in an iterative fashion (d). The map (b) is adapted from Han et al. [11], and perspective plots (c) were generated using random realizations of a binomial point process with varying intensities [38]; both are used here simply as heuristic devices. (Online version in colour.)

Figure 2.

Predictive insights into pathogen spillover risk gained from long-term, spatio-temporal studies of reservoir hosts and vectors. For Lyme disease in North America (a) and Hendra virus in eastern Australia (b), ecological and mechanistic approaches have identified both spatial and temporal proxies for spillover risk in recipient hosts. Columns indicate ecological correlates of spillover risk at varying time lags, and colours represent those that occur in autumn and winter (blue, dark grey in print) or in spring and summer (yellow, light grey in print) for systems with strong seasonality (a,b). The ecological mechanisms linking land clearance with P. knowlesi spillover in Southeast Asia are less well understood (c), but analyses of spatial scale and human cases have generated hypotheses for spatio-temporal studies of reservoirs and vectors. (Online version in colour.)