Patterns of host-parasite coinvasion promote enemy release and specialist parasite spillover.

Species invasion and redistribution, driven by climate change and other anthropogenic influences, alter global biodiversity patterns and disrupt ecosystems. As host species move, they can bring their associated parasites with them, potentially infecting resident species, or leave their parasites behind, enhancing their competitive ability in their new ranges. General rules to predict why invading hosts will retain some parasites but not others are relatively unexplored, and the potential predictors are numerous, ranging from parasite life history to host community composition. In this study, we focus on the parasite retention process during host invasion. We used the Global Mammal Parasite Database to identify terrestrial mammal hosts sampled for parasites in both native and non-native ranges. We then selected predictors likely to play a role in parasite retention, such as parasite type, parasite specialism, species composition of the invaded community, and the invading host's phylogenetic or trait-based similarity to the new community. We modelled parasite retention using boosted regression trees, with a suite of 25 predictors describing parasite and host community traits. We further tested the generality of our predictions by cross-validating models on data for other hosts and invasion locations. Our results show that parasite retention is nonrandom and predictable across hosts and invasions. It is broadly shaped by parasite type and parasite specialism, with more specialist parasites that infect many closely related hosts more likely to be retained. This trend is pronounced across parasite types; helminths, however, show a more uniform likelihood of retention regardless of specificity. Overall, we see that most parasites are not retained (11% retained), meaning many invasive species may benefit from enemy release. However, species redistribution does have the potential to spread parasites, and this also has great relevance to understanding conservation implications of species invasions. We see that specialist parasites are most likely to coinvade with their hosts, which suggests that species closely related to the invasive hosts are most likely to be affected by parasite spillover.

Vector communities under global change may exacerbate and redistribute infectious disease risk.

Vector-borne parasites may be transmitted by multiple vector species, resulting in an increased risk of transmission, potentially at larger spatial scales compared to any single vector species. Additionally, the different abilities of patchily distributed vector species to acquire and transmit parasites will lead to varying degrees of transmission risk. Investigation of how vector community composition and parasite transmission change over space due to variation in environmental conditions may help to explain current patterns in diseases but also informs our understanding of how patterns will change under climate and land-use change. We developed a novel statistical approach using a multi-year, spatially extensive case study involving a vector-borne virus affecting white-tailed deer transmitted by Culicoides midges. We characterized the structure of vector communities, established the ecological gradient controlling change in structure, and related the ecology and structure to the amount of disease reporting observed in host populations. We found that vector species largely occur and replace each other as groups, rather than individual species. Moreover, community structure is primarily controlled by temperature ranges, with certain communities being consistently associated with high levels of disease reporting. These communities are essentially composed of species previously undocumented as potential vectors, whereas communities containing putative vector species were largely associated with low levels, or even absence, of disease reporting. We contend that the application of metacommunity ecology to vector-borne infectious disease ecology can greatly aid the identification of transmission hotspots and an understanding of the ecological drivers of parasite transmission risk both now and in the future.

Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models.

Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point-early warning signals (EWS) due to critical slowing down (CSD)-can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.

The ghost of hosts past: impacts of host extinction on parasite specificity.

A growing body of research is focused on the extinction of parasite species in response to host endangerment and declines. Beyond the loss of parasite species richness, host extinction can impact apparent parasite host specificity, as measured by host richness or the phylogenetic distances among hosts. Such impacts on the distribution of parasites across the host phylogeny can have knock-on effects that may reshape the adaptation of both hosts and parasites, ultimately shifting the evolutionary landscape underlying the potential for emergence and the evolution of virulence across hosts. Here, we examine how the reshaping of host phylogenies through extinction may impact the host specificity of parasites, and offer examples from historical extinctions, present-day endangerment, and future projections of biodiversity loss. We suggest that an improved understanding of the impact of host extinction on contemporary host-parasite interactions may shed light on core aspects of disease ecology, including comparative studies of host specificity, virulence evolution in multi-host parasite systems, and future trajectories for host and parasite biodiversity. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

TEMPORAL AND SPATIAL PATTERNS IN CANINE DISTEMPER VIRUS CASES IN WILDLIFE DIAGNOSED AT THE SOUTHEASTERN COOPERATIVE WILDLIFE DISEASE STUDY, 1975-2019.

Canine distemper is a high-impact disease of many mammal species and has caused substantial carnivore population declines. Analysis was conducted on passive surveillance data of canine distemper (CDV)-positive wild mammal cases submitted to the Southeastern Cooperative Wildlife Disease Study, Athens, Georgia, US, between January 1975 and December 2019. Overall, 964 cases from 17 states were CDV positive, including 646 raccoons (Procyon lotor), 254 gray foxes (Urocyon cinereoargenteus), 33 striped skunks (Mephitis mephitis), 18 coyotes (Canis latrans), four red foxes (Vulpes vulpes), three gray wolves (Canis lupus), three American black bears (Ursus americanus), two American mink (Mustela vison), and one long-tailed weasel (Mustela frenata). Raccoon and gray fox case data from the state of Georgia (n=441) were selected for further analysis. Autoregressive integrated moving average models were developed predicting raccoon and gray fox case numbers. The best-performing model for gray foxes used numbers of gray fox CDV cases from the previous 2 mo and of raccoon cases in the present month to predict the numbers of gray fox cases in the present month. The best-performing model for raccoon prediction used numbers of raccoon CDV cases from the previous month and of gray fox cases in the present month and previous 2 mo to predict numbers of raccoon cases in the present month. Temporal trends existed in CDV cases for both species, with cases more likely to occur during the breeding season. Spatial clustering of cases was more likely to occur in areas of medium to high human population density; fewer cases occurred in both the most densely populated and sparsely populated areas. This pattern was most prominent for raccoons, which may correspond to high transmission rates in suburban areas, where raccoon population densities are probably highest, possibly because of a combination of suitable habitat and supplemental resources.

Alternative transmission pathways for guinea worm in dogs: implications for outbreak risk and control.

Guinea worm (Dracunculus medinensis) has exerted a high human health burden in parts of Africa. Complete eradication of Guinea worm disease (dracunculiasis) may be delayed by the circulation of the parasite in domestic dogs. As with humans, dogs acquire the parasite by directly ingesting infected copepods, and recent evidence suggests that consuming frogs that ingested infected copepods as tadpoles may be a viable transmission route (paratenic route). To understand the relative contributions of direct and paratenic transmission routes, we developed a mathematical model that describes transmission of Guinea worm between dogs, copepods and frogs. We explored how the parasite basic reproductive number (R0) depends on parameters amenable to actionable interventions under three scenarios: frogs/tadpoles do not consume copepods; tadpoles consume copepods but frogs do not contribute to transmission; and frogs are paratenic hosts. We found a non-monotonic relationship between the number of dogs and R0. Generally, frogs can contribute to disease control by removing infected copepods from the waterbody even when paratenic transmission can occur. However, paratenic transmission could play an important role in maintaining the parasite when direct transmission is reduced by interventions focused on reducing copepod ingestion by dogs. Together, these suggest that the most effective intervention strategies may be those which focus on the reduction of copepods, as this reduces outbreak potential irrespective of the importance of the paratenic route.

Host and parasite traits predict cross-species parasite acquisition by introduced mammals.

Species invasions and range shifts can lead to novel host-parasite communities, but we lack general rules on which new associations are likely to form. While many studies examine parasite sharing among host species, the directionality of transmission is typically overlooked, impeding our ability to derive principles of parasite acquisition. Consequently, we analysed parasite records from the non-native ranges of 11 carnivore and ungulate species. Using boosted regression trees, we modelled parasite acquisition within each zoogeographic realm of a focal host's non-native range, using a suite of predictors characterizing the parasites themselves and the host community in which they live. We found that higher parasite prevalence among established hosts increases the likelihood of acquisition, particularly for generalist parasites. Non-native host species are also more likely to acquire parasites from established host species to which they are closely related; however, the acquisition of several parasite groups is biased to phylogenetically specialist parasites, indicating potential costs of parasite generalism. Statistical models incorporating these features provide an accurate prediction of parasite acquisition, indicating that measurable host and parasite traits can be used to estimate the likelihood of new host-parasite associations forming. This work provides general rules to help anticipate novel host-parasite associations created by climate change and other anthropogenic influences.

Identifying correlates of Guinea worm (Dracunculus medinensis) infection in domestic dog populations.

Few human infectious diseases have been driven as close to eradication as dracunculiasis, caused by the Guinea worm parasite (Dracunculus medinensis). The number of human cases of Guinea worm decreased from an estimated 3.5 million in 1986 to mere hundreds by the 2010s. In Chad, domestic dogs were diagnosed with Guinea worm for the first time in 2012, and the numbers of infected dogs have increased annually. The presence of the parasite in a non-human host now challenges efforts to eradicate D. medinensis, making it critical to understand the factors that correlate with infection in dogs. In this study, we evaluated anthropogenic and environmental factors most predictive of detection of D. medinensis infection in domestic dog populations in Chad. Using boosted regression tree models to identify covariates of importance for predicting D. medinensis infection at the village and spatial hotspot levels, while controlling for surveillance intensity, we found that the presence of infection in a village was predicted by a combination of demographic (e.g. fishing village identity, dog population size), geographic (e.g. local variation in elevation), and climatic (e.g. precipitation and temperature) factors, which differed between northern and southern villages. In contrast, the presence of a village in a spatial infection hotspot, was primarily predicted by geography and climate. Our findings suggest that factors intrinsic to individual villages are highly predictive of the detection of Guinea worm parasite presence, whereas village membership in a spatial infection hotspot is largely determined by location and climate. This study provides new insight into the landscape-scale epidemiology of a debilitating parasite and can be used to more effectively target ongoing research and possibly eradication and control efforts.

Characterising interactions between co-infecting parasites using age-intensity profiles.

Interactions between co-infecting parasite species can impact transmission. Whether co-infection is beneficial or detrimental to a target parasite, and whether the mechanism involves changes in host susceptibility or parasite clearance, can be difficult to assess. We demonstrate the potential for host age-parasite intensity curves to allow assessment of these factors. A model is developed to generate predictions and test these predictions using helminth parasites of white-tailed deer (Odocoileus virginianus). We identify three beneficial interactions involving five helminth species, including susceptibility and clearance-based mechanisms. Our results suggest that analysis of age-intensity data represents a new tool for assessing the nature and strength of co-infecting parasite interactions.

Phylogenetic aggregation increases zoonotic potential of mammalian viruses.

While many viruses of wild mammals are capable of infecting humans, our understanding of zoonotic potential is incomplete. Viruses vary in their degree of generalism, characterized by the phylogenetic relationships of their hosts. Among the dimensions of this phylogenetic landscape, phylogenetic aggregation, which is largely overlooked in studies of parasite host range, emerges in this study as a key predictor of zoonotic status of viruses. Plausibly, viruses that exhibit aggregation, typified by discrete clusters of related host species, may (i) have been able to close the phylogenetic distance to humans, (ii) have subsequently acquired an epidemiologically relevant host and (iii) exhibit relatively high fitness in realized host communities, which are frequently phylogenetically aggregated. These mechanisms associated with phylogenetic aggregation may help explain why correlated fundamental traits, such as the ability of viruses to replicate in the cytoplasm, are associated with zoonoses.

Vector-borne parasite invasion in communities across space and time.

While vector-borne parasite transmission often operates via generalist-feeding vectors facilitating cross-species transmission in host communities, theory describing the relationship between host species diversity and parasite invasion in these systems is underdeveloped. Host community composition and abundance vary across space and time, generating opportunities for parasite invasion. To explore how host community variation can modify parasite invasion potential, we develop a model for vector-borne parasite transmission dynamics that includes a host community of arbitrary richness and species' abundance. To compare invasion potential across communities, we calculate the community basic reproductive ratio of the parasite. We compare communities comprising a set of host species to their subsets, which allows for flexible scenario building including the introduction of novel host species and species loss. We allow vector abundance to scale with, or be independent of, community size, capturing regulation by feeding opportunities and non-host effects such as limited oviposition sites. Motivated by equivocal data relating host species competency to abundance, we characterize plausible host communities via phenomenological relationships between host species abundance and competency. We identify an underappreciated mechanism whereby changes to communities simultaneously alter average competency and the vector to host ratio and demonstrate that the interaction can profoundly influence invasion potential.

Refugia and anthelmintic resistance: Concepts and challenges.

Anthelmintic resistance is a threat to global food security. In order to alleviate the selection pressure for resistance and maintain drug efficacy, management strategies increasingly aim to preserve a proportion of the parasite population in 'refugia', unexposed to treatment. While persuasive in its logic, and widely advocated as best practice, evidence for the ability of refugia-based approaches to slow the development of drug resistance in parasitic helminths is currently limited. Moreover, the conditions needed for refugia to work, or how transferable those are between parasite-host systems, are not known. This review, born of an international workshop, seeks to deconstruct the concept of refugia and examine its assumptions and applicability in different situations. We conclude that factors potentially important to refugia, such as the fitness cost of drug resistance, the degree of mixing between parasite sub-populations selected through treatment or not, and the impact of parasite life-history, genetics and environment on the population dynamics of resistance, vary widely between systems. The success of attempts to generate refugia to limit anthelmintic drug resistance are therefore likely to be highly dependent on the system in hand. Additional research is needed on the concept of refugia and the underlying principles for its application across systems, as well as empirical studies within systems that prove and optimise its usefulness.

The statistics of epidemic transitions.

Emerging and re-emerging pathogens exhibit very complex dynamics, are hard to model and difficult to predict. Their dynamics might appear intractable. However, new statistical approaches-rooted in dynamical systems and the theory of stochastic processes-have yielded insight into the dynamics of emerging and re-emerging pathogens. We argue that these approaches may lead to new methods for predicting epidemics. This perspective views pathogen emergence and re-emergence as a "critical transition," and uses the concept of noisy dynamic bifurcation to understand the relationship between the system observables and the distance to this transition. Because the system dynamics exhibit characteristic fluctuations in response to perturbations for a system in the vicinity of a critical point, we propose this information may be harnessed to develop early warning signals. Specifically, the motion of perturbations slows as the system approaches the transition.

Estimating the distance to an epidemic threshold.

The epidemic threshold of the susceptible-infected-recovered model is a boundary separating parameters that permit epidemics from those that do not. This threshold corresponds to parameters where the system's equilibrium becomes unstable. Consequently, we use the average rate at which deviations from the equilibrium shrink to define a distance to this threshold. However, the vital dynamics of the host population may occur slowly even when transmission is far from threshold levels. Here, we show analytically how such slow dynamics can prevent estimation of the distance to the threshold from fluctuations in the susceptible population. Although these results are exact only in the limit of long-term observation of a large system, simulations show that they still provide useful insight into systems with a range of population sizes, environmental noise and observation schemes. Having established some guidelines about when estimates are accurate, we then illustrate how multiple distance estimates can be used to estimate the rate of approach to the threshold. The estimation approach is general and may be applicable to zoonotic pathogens such as Middle East respiratory syndrome-related coronavirus (MERS-CoV) as well as vaccine-preventable diseases like measles.

Anticipating epidemic transitions with imperfect data.

Epidemic transitions are an important feature of infectious disease systems. As the transmissibility of a pathogen increases, the dynamics of disease spread shifts from limited stuttering chains of transmission to potentially large scale outbreaks. One proposed method to anticipate this transition are early-warning signals (EWS), summary statistics which undergo characteristic changes as the transition is approached. Although theoretically predicted, their mathematical basis does not take into account the nature of epidemiological data, which are typically aggregated into periodic case reports and subject to reporting error. The viability of EWS for epidemic transitions therefore remains uncertain. Here we demonstrate that most EWS can predict emergence even when calculated from imperfect data. We quantify performance using the area under the curve (AUC) statistic, a measure of how well an EWS distinguishes between numerical simulations of an emerging disease and one which is stationary. Values of the AUC statistic are compared across a range of different reporting scenarios. We find that different EWS respond to imperfect data differently. The mean, variance and first differenced variance all perform well unless reporting error is highly overdispersed. The autocorrelation, autocovariance and decay time perform well provided that the aggregation period of the data is larger than the serial interval and reporting error is not highly overdispersed. The coefficient of variation, skewness and kurtosis are found to be unreliable indicators of emergence. Overall, we find that seven of ten EWS considered perform well for most realistic reporting scenarios. We conclude that imperfect epidemiological data is not a barrier to using EWS for many potentially emerging diseases.

Estimating parasite host range.

Estimating the number of host species that a parasite can infect (i.e. host range) provides key insights into the evolution of host specialism and is a central concept in disease ecology. Host range is rarely estimated in real systems, however, because variation in species relative abundance and the detection of rare species makes it challenging to confidently estimate host range. We applied a non-parametric richness indicator to estimate host range in simulated and empirical data, allowing us to assess the influence of sampling heterogeneity and data completeness. After validating our method on simulated data, we estimated parasite host range for a sparsely sampled global parasite occurrence database (Global Mammal Parasite Database) and a repeatedly sampled set of parasites of small mammals from New Mexico (Sevilleta Long Term Ecological Research Program). Estimation accuracy varied strongly with parasite taxonomy, number of parasite occurrence records, and the shape of host species-abundance distribution (i.e. the dominance and rareness of species in the host community). Our findings suggest that between 20% and 40% of parasite host ranges are currently unknown, highlighting a major gap in our understanding of parasite specificity, host-parasite network structure, and parasite burdens.

Predicting cryptic links in host-parasite networks.

Networks are a way to represent interactions among one (e.g., social networks) or more (e.g., plant-pollinator networks) classes of nodes. The ability to predict likely, but unobserved, interactions has generated a great deal of interest, and is sometimes referred to as the link prediction problem. However, most studies of link prediction have focused on social networks, and have assumed a completely censused network. In biological networks, it is unlikely that all interactions are censused, and ignoring incomplete detection of interactions may lead to biased or incorrect conclusions. Previous attempts to predict network interactions have relied on known properties of network structure, making the approach sensitive to observation errors. This is an obvious shortcoming, as networks are dynamic, and sometimes not well sampled, leading to incomplete detection of links. Here, we develop an algorithm to predict missing links based on conditional probability estimation and associated, node-level features. We validate this algorithm on simulated data, and then apply it to a desert small mammal host-parasite network. Our approach achieves high accuracy on simulated and observed data, providing a simple method to accurately predict missing links in networks without relying on prior knowledge about network structure.

Global Mammal Parasite Database version 2.0.

Illuminating the ecological and evolutionary dynamics of parasites is one of the most pressing issues facing modern science, and is critical for basic science, the global economy, and human health. Extremely important to this effort are data on the disease-causing organisms of wild animal hosts (including viruses, bacteria, protozoa, helminths, arthropods, and fungi). Here we present an updated version of the Global Mammal Parasite Database, a database of the parasites of wild ungulates (artiodactyls and perissodactyls), carnivores, and primates, and make it available for download as complete flat files. The updated database has more than 24,000 entries in the main data file alone, representing data from over 2700 literature sources. We include data on sampling method and sample sizes when reported, as well as both "reported" and "corrected" (i.e., standardized) binomials for each host and parasite species. Also included are current higher taxonomies and data on transmission modes used by the majority of species of parasites in the database. In the associated metadata we describe the methods used to identify sources and extract data from the primary literature, how entries were checked for errors, methods used to georeference entries, and how host and parasite taxonomies were standardized across the database. We also provide definitions of the data fields in each of the four files that users can download.