Linking community and disease ecology: the impact of biodiversity on pathogen transmission

The increasing number of zoonotic diseases spilling over from a range of wild animal species represents a particular concern for public health, especially in light of the current dramatic trend of biodiversity loss. To understand the ecology of these multi-host pathogens and their response to environmental degradation and species extinctions, it is necessary to develop a theoretical framework that takes into account realistic community assemblages. Here, we present a multi-host species epidemiological model that includes empirically determined patterns of diversity and composition derived from community ecology studies. We use this framework to study the interaction between wildlife diversity and directly transmitted pathogen dynamics. First, we demonstrate that variability in community composition does not affect significantly the intensity of pathogen transmission. We also show that the consequences of community diversity can differentially impact the prevalence of pathogens and the number of infectious individuals. Finally, we show that ecological interactions among host species have a weaker influence on pathogen circulation than inter-species transmission rates. We conclude that integration of a community perspective to study wildlife pathogens is crucial, especially in the context of understanding and predicting infectious disease emergence events.     

Forest species diversity reduces disease risk in a generalist plant pathogen invasion

Empirical evidence suggests that biodiversity loss can increase disease transmission, yet our understanding of the 'diversity-disease hypothesis' for generalist pathogens in natural ecosystems is limited. We used a landscape epidemiological approach to examine two scenarios regarding diversity effects on the emerging plant pathogen Phytophthora ramorum across a broad, heterogeneous ecoregion: (1) an amplification effect exists where disease risk is greater in areas with higher plant diversity due to the pathogen's wide host range, or (2) a dilution effect where risk is reduced with increasing diversity due to lower competency of alternative hosts. We found evidence for pathogen dilution, whereby disease risk was lower in sites with higher species diversity, after accounting for potentially confounding effects of host density and landscape heterogeneity. Our results suggest that although nearly all plants in the ecosystem are hosts, alternative hosts may dilute disease transmission by competent hosts, thereby buffering forest health from infectious disease.     

Frontiers in research on biodiversity and disease

Global losses of biodiversity have galvanised efforts to understand how changes to communities affect ecological processes, including transmission of infectious pathogens. Here, we review recent research on diversity–disease relationships and identify future priorities. Growing evidence from experimental, observational and modelling studies indicates that biodiversity changes alter infection for a range of pathogens and through diverse mechanisms. Drawing upon lessons from the community ecology of free‐living organisms, we illustrate how recent advances from biodiversity research generally can provide necessary theoretical foundations, inform experimental designs, and guide future research at the interface between infectious disease risk and changing ecological communities. Dilution effects are expected when ecological communities are nested and interactions between the pathogen and the most competent host group(s) persist or increase as biodiversity declines. To move beyond polarising debates about the generality of diversity effects and develop a predictive framework, we emphasise the need to identify how the effects of diversity vary with temporal and spatial scale, to explore how realistic patterns of community assembly affect transmission, and to use experimental studies to consider mechanisms beyond simple changes in host richness, including shifts in trophic structure, functional diversity and symbiont composition.     

Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission

Background  

Computational biology is often associated with genetic or genomic studies only. However, thanks to the increase of computational resources, computational models are appreciated as useful tools in many other scientific fields. Such modeling systems are particularly relevant for the study of complex systems, like the epidemiology of emerging infectious diseases. So far, mathematical models remain the main tool for the epidemiological and ecological analysis of infectious diseases, with SIR models could be seen as an implicit standard in epidemiology. Unfortunately, these models are based on differential equations and, therefore, can become very rapidly unmanageable due to the too many parameters which need to be taken into consideration. For instance, in the case of zoonotic and vector-borne diseases in wildlife many different potential host species could be involved in the life-cycle of disease transmission, and SIR models might not be the most suitable tool to truly capture the overall disease circulation within that environment. This limitation underlines the necessity to develop a standard spatial model that can cope with the transmission of disease in realistic ecosystems.     

Harvesting can increase severity of wildlife disease epidemics

Theoretical studies of wildlife population dynamics have proved insightful for sustainable management, where the principal aim is to maximize short-term yield, without risking population extinction. Surprisingly, infectious diseases have not been accounted for in harvest models, which is a major oversight because the consequences of parasites for host population dynamics are well-established. Here, we present a simple general model for a host species subject to density dependent reproduction and seasonal demography. We assume this host species is subject to infection by a strongly immunizing, directly transmitted pathogen. In this context, we show that the interaction between density dependent effects and harvesting can substantially increase both disease prevalence and the absolute number of infectious individuals. This effect clearly increases the risk of cross-species disease transmission into domestic and livestock populations. In addition, if the disease is associated with a risk of mortality, then the synergistic interaction between hunting and disease-induced death can increase the probability of host population extinction.     

Integrating data mining and transmission theory in the ecology of infectious diseases

Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent‐borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining‐modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans.     

Host physiological phenotype explains pathogen reservoir potential

Control of emerging infectious diseases often hinges on identifying a pathogen reservoir, the source of disease transmission. The potential to function as a pathogen reservoir can be influenced by host lifespan, geographic provenance and phylogeny. Yet, no study has identified factors that causally determine the reservoir potential of diverse host species. We propose the host physiological phenotype hypothesis, which predicts that hosts with short‐lived, poorly defended, nutrient rich and high metabolism tissue have greater values for three epidemiological parameters that determine reservoir potential: host susceptibility to infection, competence to infect vectors and ability to support vector populations. We experimentally tested these predictions using a generalist vectored virus and six wild grass species. Host physiological phenotype explained why hosts differed in all three epidemiological parameters while host lifespan, provenance and phylogeny could not explain host competence. Thus, a single, general axis describing variation in host physiological phenotype may explain reservoir potential.     

Mechanisms underlying host persistence following amphibian disease emergence determine appropriate management strategies

Emerging infectious diseases have caused many species declines, changes in communities and even extinctions. There are also many species that persist following devastating declines due to disease. The broad mechanisms that enable host persistence following declines include evolution of resistance or tolerance, changes in immunity and behaviour, compensatory recruitment, pathogen attenuation, environmental refugia, density‐dependent transmission and changes in community composition. Here we examine the case of chytridiomycosis, the most important wildlife disease of the past century. We review the full breadth of mechanisms allowing host persistence, and synthesise research on host, pathogen, environmental and community factors driving persistence following chytridiomycosis‐related declines and overview the current evidence and the information required to support each mechanism. We found that for most species the mechanisms facilitating persistence have not been identified. We illustrate how the mechanisms that drive long‐term host population dynamics determine the most effective conservation management strategies. Therefore, understanding mechanisms of host persistence is important because many species continue to be threatened by disease, some of which will require intervention. The conceptual framework we describe is broadly applicable to other novel disease systems.     

Inferring the Source of Transmission with Phylogenetic Data

Identifying the source of transmission using pathogen genetic data is complicated by numerous biological, immunological, and behavioral factors. A large source of error arises when there is incomplete or sparse sampling of cases. Unsampled cases may act as either a common source of infection or as an intermediary in a transmission chain for hosts infected with genetically similar pathogens. It is difficult to quantify the probability of common source or intermediate transmission events, which has made it difficult to develop statistical tests to either confirm or deny putative transmission pairs with genetic data. We present a method to incorporate additional information about an infectious disease epidemic, such as incidence and prevalence of infection over time, to inform estimates of the probability that one sampled host is the direct source of infection of another host in a pathogen gene genealogy. These methods enable forensic applications, such as source-case attribution, for infectious disease epidemics with incomplete sampling, which is usually the case for high-morbidity community-acquired pathogens like HIV, Influenza and Dengue virus. These methods also enable epidemiological applications such as the identification of factors that increase the risk of transmission. We demonstrate these methods in the context of the HIV epidemic in Detroit, Michigan, and we evaluate the suitability of current sequence databases for forensic and epidemiological investigations. We find that currently available sequences collected for drug resistance testing of HIV are unlikely to be useful in most forensic investigations, but are useful for identifying transmission risk factors.     

Ecosystem dynamics, biological diversity and emerging infectious diseases

In this article, we summarize the major scientific developments of the last decade on the transmission of infectious agents in multi-host systems. Almost sixty percent of the pathogens that have emerged in humans during the last 30-40 years are of animal origin and about sixty percent of them show an important variety of host species besides humans (3 or more possible host species). In this review, we focus on zoonotic infections with vector-borne transmission and dissect the contrasting effects that a multiplicity of host reservoirs and vectors can have on their disease dynamics. We discuss the effects exerted by host and vector species richness and composition on pathogen prevalence (i.e., reduction, including the dilution effect, or amplification). We emphasize that, in multiple host systems and for vector-borne zoonotic pathogens, host reservoir species and vector species can exert contrasting effect locally. The outcome on disease dynamics (reduced pathogen prevalence in vectors when the host reservoir species is rich and increased pathogen prevalence when the vector species richness increases) may be highly heterogeneous in both space and time. We then ask briefly how a shift towards a more systemic perspective in the study of emerging infectious diseases, which are driven by a multiplicity of hosts, may stimulate further research developments. Finally, we propose some research avenues that take better into account the multi-host species reality in the transmission of the most important emerging infectious diseases, and, particularly, suggest, as a possible orientation, the careful assessment of the life-history characteristics of hosts and vectors in a community ecology-based perspective.