Percolation models of pathogen spillover

A One Health framework for exploring zoonotic interactions demonstrated through a case study

The eco-epidemiology of zoonoses is often oversimplified to host-pathogen interactions while findings derived from global datasets are rarely directly transferable to smaller-scale contexts. Through a systematic literature search, we compiled a dataset of naturally occurring zoonotic interactions in Austria, spanning 1975-2022. We introduce the concept of zoonotic web to describe the complex relationships between zoonotic agents, their hosts, vectors, food, and environmental sources. The zoonotic web was explored through network analysis. After controlling for research effort, we demonstrate that, within the projected unipartite source-source network of zoonotic agent sharing, the most influential zoonotic sources are human, cattle, chicken, and some meat products. Analysis of the One Health 3-cliques (triangular sets of nodes representing human, animal, and environment) confirms the increased probability of zoonotic spillover at human-cattle and human-food interfaces. We characterise six communities of zoonotic agent sharing, which assembly patterns are likely driven by highly connected infectious agents in the zoonotic web, proximity to human, and anthropogenic activities. Additionally, we report a frequency of emerging zoonotic diseases in Austria of one every six years. Here, we present a flexible network-based approach that offers insights into zoonotic transmission chains, facilitating the development of locally-relevant One Health strategies against zoonoses.

Modeling spillover dynamics: understanding emerging pathogens of public health concern

The emergence of infectious diseases with pandemic potential is a major public health threat worldwide. The World Health Organization reports that about 60% of emerging infectious diseases are zoonoses, originating from spillover events. Although the mechanisms behind spillover events remain unclear, mathematical modeling offers a way to understand the intricate interactions among pathogens, wildlife, humans, and their shared environment. Aiming at gaining insights into the dynamics of spillover events and the outcome of an eventual disease outbreak in a population, we propose a continuous time stochastic modeling framework. This framework links the dynamics of animal reservoirs and human hosts to simulate cross-species disease transmission. We conduct a thorough analysis of the model followed by numerical experiments that explore various spillover scenarios. The results suggest that although most epidemic outbreaks caused by novel zoonotic pathogens do not persist in the human population, the rising number of spillover events can avoid long-lasting extinction and lead to unexpected large outbreaks. Hence, global efforts to reduce the impacts of emerging diseases should not only address post-emergence outbreak control but also need to prevent pandemics before they are established.

Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

The ecological conditions experienced by wildlife reservoirs affect infection dynamics and thus the distribution of pathogen excreted into the environment. This spatial and temporal distribution of shed pathogen has been hypothesised to shape risks of zoonotic spillover. However, few systems have data on both long-term ecological conditions and pathogen excretion to advance mechanistic understanding and test environmental drivers of spillover risk. We here analyse three years of Hendra virus data from nine Australian flying fox roosts with covariates derived from long-term studies of bat ecology. We show that the magnitude of winter pulses of viral excretion, previously considered idiosyncratic, are most pronounced after recent food shortages and in bat populations displaced to novel habitats. We further show that cumulative pathogen excretion over time is shaped by bat ecology and positively predicts spillover frequency. Our work emphasises the role of reservoir host ecology in shaping pathogen excretion and provides a new approach to estimate spillover risk.

A protocol for a longitudinal, observational cohort study of infection and exposure to zoonotic and vector-borne diseases across a land-use gradient in Sabah, Malaysian Borneo: a socio-ecological systems approach

Introduction. Landscape changes disrupt environmental, social and biological systems, altering pathogen spillover and transmission risks. This study aims to quantify the impact of specific land management practices on spillover and transmission rates of zoonotic and vector-borne diseases within Malaysian Borneo. This protocol describes a cohort study with integrated ecological sampling to assess how deforestation and agricultural practices impact pathogen flow from wildlife and vector populations to human infection and detection by health facilities. This will focus on malaria, dengue and emerging arboviruses (Chikungunya and Zika), vector-borne diseases with varying contributions of simian reservoirs within this setting. Methods. A prospective longitudinal observational cohort study will be established in communities residing or working within the vicinity of the Stability of Altered Forest Ecosystems (SAFE) Project, a landscape gradient within Malaysian Borneo encompassing different plantation and forest types. The primary outcome of this study will be transmission intensity of selected zoonotic and vector-borne diseases, as quantified by changes in pathogen-specific antibody levels. Exposure will be measured using paired population-based serological surveys conducted at the beginning and end of the two-year cohort study. Secondary outcomes will include the distribution and infection rates of Aedes and Anopheles mosquito vectors, human risk behaviours and clinical cases reported to health facilities. Longitudinal data on human behaviour, contact with wildlife and GPS tracking of mobility patterns will be collected throughout the study period. This will be integrated with entomological surveillance to monitor densities and pathogen infection rates of Aedes and Anopheles mosquitoes relative to land cover. Within surrounding health clinics, continuous health facility surveillance will be used to monitor reported infections and febrile illnesses. Models will be developed to assess spillover and transmission rates relative to specific land management practices and evaluate abilities of surveillance systems to capture these risks.

From Protein to Pandemic: The Transdisciplinary Approach Needed to Prevent Spillover and the Next Pandemic

Pandemics are a consequence of a series of processes that span scales from viral biology at 10-9 m to global transmission at 106 m. The pathogen passes from one host species to another through a sequence of events that starts with an infected reservoir host and entails interspecific contact, innate immune responses, receptor protein structure within the potential host, and the global spread of the novel pathogen through the naive host population. Each event presents a potential barrier to the onward passage of the virus and should be characterized with an integrated transdisciplinary approach. Epidemic control is based on the prevention of exposure, infection, and disease. However, the ultimate pandemic prevention is prevention of the spillover event itself. Here, we focus on the potential for preventing the spillover of henipaviruses, a group of viruses derived from bats that frequently cross species barriers, incur high human mortality, and are transmitted among humans via stuttering chains. We outline the transdisciplinary approach needed to prevent the spillover process and, therefore, future pandemics.

Towards an ecosystem model of infectious disease

Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics.

Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England

A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States.

Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England

A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States.

Land use-induced spillover: a call to action to safeguard environmental, animal, and human health

The rapid global spread and human health impacts of SARS-CoV-2, the virus that causes COVID-19, show humanity's vulnerability to zoonotic disease pandemics. Although anthropogenic land use change is known to be the major driver of zoonotic pathogen spillover from wildlife to human populations, the scientific underpinnings of land use-induced zoonotic spillover have rarely been investigated from the landscape perspective. We call for interdisciplinary collaborations to advance knowledge on land use implications for zoonotic disease emergence with a view toward informing the decisions needed to protect human health. In particular, we urge a mechanistic focus on the zoonotic pathogen infect-shed-spill-spread cascade to enable protection of landscape immunity-the ecological conditions that reduce the risk of pathogen spillover from reservoir hosts-as a conservation and biosecurity priority. Results are urgently needed to formulate an integrated, holistic set of science-based policy and management measures that effectively and cost-efficiently minimise zoonotic disease risk. We consider opportunities to better institute the necessary scientific collaboration, address primary technical challenges, and advance policy and management issues that warrant particular attention to effectively address health security from local to global scales.

Mathematically modeling spillovers of an emerging infectious zoonosis with an intermediate host

Modeling the behavior of zoonotic pandemic threats is a key component of their control. Many emerging zoonoses, such as SARS, Nipah, and Hendra, mutated from their wild type while circulating in an intermediate host population, usually a domestic species, to become more transmissible among humans, and this transmission route will only become more likely as agriculture and trade intensifies around the world. Passage through an intermediate host enables many otherwise rare diseases to become better adapted to humans, and so understanding this process with accurate mathematical models is necessary to prevent epidemics of emerging zoonoses, guide policy interventions in public health, and predict the behavior of an epidemic. In this paper, we account for a zoonotic disease mutating in an intermediate host by introducing a new mathematical model for disease transmission among three species. We present a model of these disease dynamics, including the equilibria of the system and the basic reproductive number of the pathogen, finding that in the presence of biologically realistic interspecies transmission parameters, a zoonotic disease with the capacity to mutate in an intermediate host population can establish itself in humans even if its R₀ in humans is less than 1. This result and model can be used to predict the behavior of any zoonosis with an intermediate host and assist efforts to protect public health.

Are we overestimating risk of enteric pathogen spillover from wild birds to humans?

Enteric illnesses remain the second largest source of communicable diseases worldwide, and wild birds are suspected sources for human infection. This has led to efforts to reduce pathogen spillover through deterrence of wildlife and removal of wildlife habitat, particularly within farming systems, which can compromise conservation efforts and the ecosystem services wild birds provide. Further, Salmonella spp. are a significant cause of avian mortality, leading to additional conservation concerns. Despite numerous studies of enteric bacteria in wild birds and policies to discourage birds from food systems, we lack a comprehensive understanding of wild bird involvement in transmission of enteric bacteria to humans. Here, we propose a framework for understanding spillover of enteric pathogens from wild birds to humans, which includes pathogen acquisition, reservoir competence and bacterial shedding, contact with people and food, and pathogen survival in the environment. We place the literature into this framework to identify important knowledge gaps. Second, we conduct a meta‐analysis of prevalence data for three human enteric pathogens, Campylobacter spp., E. coli, and Salmonella spp., in 431 North American breeding bird species. Our literature review revealed that only 3% of studies addressed the complete system of pathogen transmission. In our meta‐analysis, we found a Campylobacter spp. prevalence of 27% across wild birds, while prevalence estimates of pathogenic E. coli (20%) and Salmonella spp. (6.4%) were lower. There was significant bias in which bird species have been tested, with most studies focusing on a small number of taxa that are common near people (e.g. European starlings Sturnus vulgaris and rock pigeons Columba livia) or commonly in contact with human waste (e.g. gulls). No pathogen prevalence data were available for 65% of North American breeding bird species, including many commonly in contact with humans (e.g. black‐billed magpie Pica hudsonia and great blue heron Ardea herodias), and our metadata suggest that some under‐studied species, taxonomic groups, and guilds may represent equivalent or greater risk to human infection than heavily studied species. We conclude that current data do not provide sufficient information to determine the likelihood of enteric pathogen spillover from wild birds to humans and thus preclude management solutions. The primary focus in the literature on pathogen prevalence likely overestimates the probability of enteric pathogen spillover from wild birds to humans because a pathogen must survive long enough at an infectious dose and be a strain that is able to colonize humans to cause infection. We propose that future research should focus on the large number of under‐studied species commonly in contact with people and food production and demonstrate shedding of bacterial strains pathogenic to humans into the environment where people may contact them. Finally, studies assessing the duration and intensity of bacterial shedding and survival of bacteria in the environment in bird faeces will help provide crucial missing information necessary to calculate spillover probability. Addressing these essential knowledge gaps will support policy to reduce enteric pathogen spillover to humans and enhance bird conservation efforts that are currently undermined by unsupported fears of pathogen spillover from wild birds.

Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface

Managing pathogen spillover at the wildlife-livestock interface is a key step towards improving global animal health, food security and wildlife conservation. However, predicting the effectiveness of management actions across host-pathogen systems with different life histories is an on-going challenge since data on intervention effectiveness are expensive to collect and results are system-specific. We developed a simulation model to explore how the efficacies of different management strategies vary according to host movement patterns and epidemic growth rates. The model suggested that fast-growing, fast-moving epidemics like avian influenza were best-managed with actions like biosecurity or containment, which limited and localized overall spillover risk. For fast-growing, slower-moving diseases like foot-and-mouth disease, depopulation or prophylactic vaccination were competitive management options. Many actions performed competitively when epidemics grew slowly and host movements were limited, and how management efficacy related to epidemic growth rate or host movement propensity depended on what objective was used to evaluate management performance. This framework offers one means of classifying and prioritizing responses to novel pathogen spillover threats, and evaluating current management actions for pathogens emerging at the wildlife-livestock interface. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

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

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'.

Mosquito and primate ecology predict human risk of yellow fever virus spillover in Brazil

Many (re)emerging infectious diseases in humans arise from pathogen spillover from wildlife or livestock, and accurately predicting pathogen spillover is an important public health goal. In the Americas, yellow fever in humans primarily occurs following spillover from non-human primates via mosquitoes. Predicting yellow fever spillover can improve public health responses through vector control and mass vaccination. Here, we develop and test a mechanistic model of pathogen spillover to predict human risk for yellow fever in Brazil. This environmental risk model, based on the ecology of mosquito vectors and non-human primate hosts, distinguished municipality-months with yellow fever spillover from 2001 to 2016 with high accuracy (AUC = 0.72). Incorporating hypothesized cyclical dynamics of infected primates improved accuracy (AUC = 0.79). Using boosted regression trees to identify gaps in the mechanistic model, we found that important predictors include current and one-month lagged environmental risk, vaccine coverage, population density, temperature and precipitation. More broadly, we show that for a widespread human viral pathogen, the ecological interactions between environment, vectors, reservoir hosts and humans can predict spillover with surprising accuracy, suggesting the potential to improve preventive action to reduce yellow fever spillover and avert onward epidemics in humans.

This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.