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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.

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.

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’.

AIM: Parasites are a major component of global ecosystems, yet spatial variation in parasite diversity is poorly known, largely because their occurrence data are limited and thus difficult to interpret. Using a recently compiled database of parasite occurrences, we compare different models which we use to infer parasite geographic ranges and parasite species richness across the globe. INNOVATION: To date, most studies exploring spatial patterns of parasite diversity assumed, with little validation, that the geographic range of a parasite species can be represented by the collective geographic range of its host species. Our study compares this assumption with a suite of other methods to infer parasite distribution from parasite occurrence data (e.g., based on data density, ecoregions and climatic conditions). We highlight diversity hotspots identified by the various methods and compare the effects of sampling intensities in different regions, a crucial factor determining observed parasite diversity. MAIN CONCLUSIONS: The type of model used to infer parasite distributions affects estimates of both total species richness and spatial patterns of hotspots of parasite richness. Overall, the models based on reported occurrences share similar areas of high parasite richness that tend to be biased towards areas of high sampling effort. In contrast, the model based on host distributions showed hotspots of parasite diversity that are biased towards areas of high host species richness. Accounting for sampling effort could only help to reconcile the outcome from the different models in some regions. Further, the non‐saturated species accumulation curves even for the best studied regions of the world such as Europe and North America serve as a call for further sampling effort and development of effective analytic tools that can provide robust accounts of global parasite diversity.

Made available in DSpace on 2019-10-06T16:10:49Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-03-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Healthcare-associated infections cause significant patient morbidity and mortality, and contribute to growing healthcare costs, whose effects may be felt most strongly in developing countries. Active surveillance systems, hospital staff compliance, including hand hygiene, and a rational use of antimicrobials are among the important measures to mitigate the spread of healthcare-associated infection within and between hospitals. Klebsiella pneumoniae is an important human pathogen that can spread in hospital settings, with some forms exhibiting drug resistance, including resistance to the carbapenem class of antibiotics, the drugs of last resort for such infections. Focusing on the role of patient movement within and between hospitals on the transmission and incidence of enterobacteria producing the K. pneumoniae Carbapenemase (KPC, an enzyme that inactivates several antimicrobials), we developed a metapopulation model where the connections among hospitals are made using a theoretical hospital network based on Brazilian hospital sizes and locations. The pathogen reproductive number, R 0 that measures the average number of new infections caused by a single infectious individual, was calculated in different scenarios defined by both the links between hospital environments (regular wards and intensive care units) and between different hospitals (patient transfer). Numerical simulation was used to illustrate the infection dynamics in this set of scenarios. The sensitivity of R 0 to model input parameters, such as hospital connectivity and patient-hospital staff contact rates was also established, highlighting the differential importance of factors amenable to change on pathogen transmission and control. São Paulo State University (UNESP) Institute of Biosciences Department of Biostatistics 18618-689 Botucatu Brazil Departamento de Doenças Tropicais Faculdade de Medicina de Botucatu Universidade Estadual Paulista Odum School of Ecology & Department of Infectious Diseases University of Georgia São Paulo State University (UNESP) Institute of Biosciences Department of Biostatistics 18618-689 Botucatu Brazil Departamento de Doenças Tropicais Faculdade de Medicina de Botucatu Universidade Estadual Paulista FAPESP: 15/05220-4 FAPESP: 16/23738- 3

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.

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.

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., Author summary The eradication of human infectious diseases has proven remarkably difficult. The world has only succeeded once, in the case of the smallpox virus. However, international efforts have driven the debilitating Guinea worm parasite closer to the brink of eradication than nearly any other parasite. Coordinated efforts by the Ministries of Health in endemic countries, the U.S. Centers for Disease Control, The Carter Center, and the World Health Organization have reduced the number of annual Guinea worm cases from millions in the 1980s to hundreds in the early 2010s, but recently a new threat has emerged. Guinea worm infections have been diagnosed in domestic dogs, particularly in the Republic of Chad, and numbers of infections have continued to increase. As in many countries where dracunculiasis is endemic, the campaign for eradication in Chad has focused intervention measures on interrupting transmission among humans, so infection in dogs jeopardizes eradication efforts. In this study, we used machine learning methods to identify demographic, geographic, and climatic factors associated with the presence of Guinea worm-infected dogs at the village level, and spatial clustering of dog cases regionally. A combination of demographic, geographic and climatic factors were important correlates of infection at the village level, but the importance of these factors varied between northern and southern populations of the parasite. At the larger village cluster level, the geographic position and climate of a village were most important. Some of our findings, including the importance of fishing villages and the difference in correlates between northern and southern villages can be used by researchers to guide additional data collection and by public health workers to better target eradication efforts. More generally, this work contributes to a broader understanding of the spatial patterning of multi-host infectious diseases of humans and animals.

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.

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.

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.

Because the natural reservoir of Ebola virus remains unclear and disease outbreaks in humans have occurred only sporadically over a large region, forecasting when and where Ebola spillovers are most likely to occur constitutes a continuing and urgent public health challenge. We developed a statistical modeling approach that associates 37 human or great ape Ebola spillovers since 1982 with spatiotemporally dynamic covariates including vegetative cover, human population size, and absolute and relative rainfall over 3 decades across sub-Saharan Africa. Our model (area under the curve 0.80 on test data) shows that spillover intensity is highest during transitions between wet and dry seasons; overall, high seasonal intensity occurs over much of tropical Africa; and spillover intensity is greatest at high (>1,000/km2) and very low (

Complex life cycle parasites, including helminths, use intermediate hosts for development and definitive hosts for reproduction, with interactions between the two host types governed by food web structure. I study how a parasite's intermediate host range is controlled by the diet breadth of definitive host species and the cost of parasite generalism, a putative fitness cost that assumes host range trades off against fitness derived from a host species. In spite of such costs, a benefit to generalism may occur when the definitive host exhibits a large diet breadth, enhancing transmission of generalist parasites via consumption of a broad array of infected intermediate hosts. I develop a simple theoretical model to demonstrate how different host range infection strategies are differentially selected for across a gradient of definitive host diet breadth according to the cost of generalism. I then use a parasitic helminth–host database in conjunction with a food web database to show that diet breadth of definitive hosts promotes generalist infection strategies at the intermediate host level, indicating relatively low costs of parasite generalism among helminths.

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., Graphical abstract Image 1, Highlights • Refugia is currently recommended for strategic control of AR in GI nematodes of livestock. • Conditions required for refugia-based control may differ between parasite systems. • An improved understanding of the parameters influencing refugia-based control is required.

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.

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., Author summary Anticipating disease emergence is a challenging problem, however the public health ramifications are clear. A proposed tool to help meet this challenge are early-warning signals (EWS), summary statistics which undergo characteristic changes before dynamical transitions. While previous theoretical studies are promising, and find that epidemic transitions are preceded by detectable trends in EWS, they do not consider the effects of imperfect data. To address this, we developed a simulation study which assesses how case aggregation and reporting error impact on 10 different EWS’s performance. Case report data were simulated by combining a stochastic SIR transmission model with a model of reporting error. Temporal trends in an EWS were used as a method of distinguishing between an emerging disease (R0 approaching 1) and a stationary disease (constant R0). We investigated the robustness of EWS to reporting process parameters, namely the aggregation period, reporting probability and overdispersion of reporting error. Seven of ten EWS perform well for realistic reporting scenarios, and are strong candidates for incorporation in disease emergence monitoring systems.

SUMMARYAlthough many parasites are transmitted between hosts by a suite of arthropod vectors, the impact of vector biodiversity on parasite transmission is poorly understood. Positive relationships between host infection prevalence and vector species richness (SR) may operate through multiple mechanisms, including (i) increased vector abundance, (ii) a sampling effect in which species of high vectorial capacity are more likely to occur in species-rich communities, and (iii) functional diversity whereby communities comprised species with distinct phenologies may extend the duration of seasonal transmission. Teasing such mechanisms apart is impeded by a lack of appropriate data, yet could highlight a neglected role for functional diversity in parasite transmission. We used statistical modelling of extensive host, vector and microparasite data to test the hypothesis that functional diversity leading to longer seasonal transmission explained variable levels of disease in a wildlife population. We additionally developed a simple transmission model to guide our expectation of how an increased transmission season translates to infection prevalence. Our study demonstrates that vector SR is associated with increased levels of disease reporting, but not via increases in vector abundance or via a sampling effect. Rather, the relationship operates by extending the length of seasonal transmission, in line with theoretical predictions.

The distribution of parasites across mammalian hosts is complex and represents a differential ability or opportunity to infect different host species. Here, we take a macroecological approach to investigate factors influencing why some parasites show a tendency to infect species widely distributed in the host phylogeny (phylogenetic generalism) while others infect only closely related hosts. Using a database on over 1400 parasite species that have been documented to infect up to 69 terrestrial mammal host species, we characterize the phylogenetic generalism of parasites using standard effect sizes for three metrics: mean pairwise phylogenetic distance (PD), maximum PD and phylogenetic aggregation. We identify a trend towards phylogenetic specialism, though statistically host relatedness is most often equivalent to that expected from a random sample of host species. Bacteria and arthropod parasites are typically the most generalist, viruses and helminths exhibit intermediate generalism, and protozoa are on average the most specialist. While viruses and helminths have similar mean pairwise PD on average, the viruses exhibit higher variation as a group. Close-contact transmission is the transmission mode most associated with specialism. Most parasites exhibiting phylogenetic aggregation (associating with discrete groups of species dispersed across the host phylogeny) are helminths and viruses.

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., Author summary The majority of host-parasite associations are poorly understood or not known at all because the number of associations is so vast. Further, interactions may shift seasonally, or as a function of changing host densities. Consequently, host-parasite networks may be poorly characterized since effects of cryptic host-parasite associations on network structure are unknown. To address this, we developed theory and applied it to empirical data to test the ability of a simple algorithm to predict interactions between hosts and parasites. The algorithm uses host and parasite trait data to train predictive probabilistic models of host-parasite interaction. We tested the accuracy of our approach using simulated networks that vary greatly in their properties, demonstrating high accuracy and robustness. We then applied this algorithm to data on a small mammal host-parasite network, estimated model accuracy, identified host and parasite traits important to prediction, and quantified expected changes to structural properties of the network as a result of link relabeling.

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.

Parasites are either dedicated to a narrow host range, or capable of exploiting a wide host range. Understanding how host ranges are determined is very important for public health, as well as wildlife, plant, livestock and agricultural diseases. Our current understanding of host–parasite associations hinges on co-evolution, which assumes evolved host preferences (host specialization) of the parasite. Despite the explanatory power of this framework, we have only a vague understanding of why many parasites routinely cross the host species’ barrier. Here we introduce a simple model demonstrating how superinfection (in a heterogeneous community) can promote host–parasite association. Strikingly, the model illustrates that strong host–parasite association occurs in the absence of host specialization, while still permitting cross-species transmission. For decades, host specialization has been foundational in explaining the maintenance of distinct parasites/strains in host species. We argue that host specializations may be exaggerated, and can occur as a byproduct (not necessarily the cause) of host–parasite associations., Highlights • Many parasites appear to exhibit host specificity. • Many parasites are also efficient in cross-species transmissions. • The above two phenomenon are largely incompatible without adaptive mutations. • Superinfection facilitates apparent host specificity and cross-species transmission.

Mathematical modelling of infectious disease is increasingly regarded as an important tool in the development of disease prevention and control measures. This article brings together key findings from various modelling studies conducted over the past 10 years that are of relevance to those on the front line of the battle against equine influenza.

While vector-borne diseases are known to be particularly influenced by environmental factors, the impact of land-cover change on vector-borne wildlife disease patterns is poorly understood, largely due to the paucity of data on disease occurrence at extensive spatial and temporal scales. Widespread and rapid anthropogenic land-cover change, especially urbanization, has transformed the US landscape during the last century. Epizootic hemorrhagic disease virus and blue tongue virus, vectored by Culicoides biting midges, are two RNA viruses in the Orbivirus genus that cause severe hemorrhagic disease (HD) in white-tailed deer (Odocoileus virginianus). We examine the spatial dynamics of HD affecting white-tailed deer in the contiguous United States in two periods covering 1980 to 2007 in connection with land-cover change over the same time. Using spatial statistical modeling, wetland cover emerges as a critical driver of HD morbidity, whereas the drivers of mortality patterns are more complex. Increasing wetland cover is positively associated with HD morbidity, which is consistent with the ecologic requirements of the Culicoides vector. Wetland cover is inherently dynamic due to its importance to biodiversity and water quality as well as its utility for other purposes when drained. Accordingly this analysis helps in understanding the consequences of changing wetlands on vector-borne disease patterns, to identify disease hotspots in a large landscape, and to forecast the spatial spread of HD and related diseases.

The emergence and spread of mutant pathogens that evade the effects of prophylactic interventions, including vaccines, threatens our ability to control infectious diseases globally. Imperfect vaccines (e.g. those used against influenza), while not providing life-long immunity, confer protection by reducing a range of pathogen life-history characteristics; conversely, mutant pathogens can gain an advantage by restoring the same range of traits in vaccinated hosts. Using an SEIR model motivated by equine influenza, we investigate the evolutionary consequences of alternative types of imperfect vaccination, by comparing the spread rate of three types of mutant pathogens, in response to three types of vaccines. All mutant types spread faster in response to a transmission-blocking vaccine, relative to vaccines that reduce the proportion of exposed vaccinated individuals becoming infectious, and to vaccines that reduce the length of the infectious period; this difference increases with increasing vaccine efficacy. We interpret our results using the first published Price equation formulation for an SEIR model, and find that our main result is explained by the effects of vaccines on the equilibrium host distribution across epidemiological classes. In particular, the proportion of vaccinated infectious individuals among all exposed and infectious hosts, which is relatively higher in the transmission-blocking vaccine scenario, is important in explaining the faster spread of mutant strains in response to that vaccine. Our work illustrates the connection between epidemiological and evolutionary dynamics, and the need to incorporate both in order to explain and interpret findings of complicated infectious disease dynamics.

SUMMARYHost–parasite associations are complex interactions dependent on aspects of hosts (e.g. traits, phylogeny or coevolutionary history), parasites (e.g. traits and parasite interactions) and geography (e.g. latitude). Predicting the permissive host set or the subset of the host community that a parasite can infect is a central goal of parasite ecology. Here we develop models that accurately predict the permissive host set of 562 helminth parasites in five different parasite taxonomic groups. We developed predictive models using host traits, host taxonomy, geographic covariates, and parasite community composition, finding that models trained on parasite community variables were more accurate than any other covariate group, even though parasite community covariates only captured a quarter of the variance in parasite community composition. This suggests that it is possible to predict the permissive host set for a given parasite, and that parasite community structure is an important predictor, potentially because parasite communities are interacting non-random assemblages.

Identifying drivers of infectious disease patterns and impacts at the broadest scales of organisation is one of the most crucial challenges for modern science, yet answers to many fundamental questions remain elusive. These include what factors commonly facilitate transmission of pathogens to novel host species, what drives variation in immune investment among host species, and more generally what drives global patterns of parasite diversity and distribution? Here we consider how the perspectives and tools of macroecology, a field that investigates patterns and processes at broad spatial, temporal and taxonomic scales, are expanding scientific understanding of global infectious disease ecology. In particular, emerging approaches are providing new insights about scaling properties across all living taxa, and new strategies for mapping pathogen biodiversity and infection risk. Ultimately, macroecology is establishing a framework to more accurately predict global patterns of infectious disease distribution and emergence.

We propose ‘the moving target hypothesis’ to describe the aetiology of a contemporary coral disease that differs from that of its historical disease state. Hitting the target with coral disease aetiology is a complex pursuit that requires understanding of host and environment, and may lack a single pathogen solution. White pox disease (WPX) affects the Caribbean coral Acropora palmata . Acroporid serratiosis is a form of WPX for which the bacterial pathogen ( Serratia marcescens ) has been established. We used long-term (1994–2014) photographic monitoring to evaluate historical and contemporary epizootiology and aetiology of WPX affecting A. palmata at eight reefs in the Florida Keys. Ranges of WPX prevalence over time (0–71.4%) were comparable for the duration of the 20-year study. Whole colony mortality and disease severity were high in historical (1994–2004), and low in contemporary (2008–2014), outbreaks of WPX. Acroporid serratiosis was diagnosed for some historical (1999, 2003) and contemporary (2012, 2013) outbreaks, but this form of WPX was not confirmed for all WPX cases. Our results serve as a context for considering aetiology as a moving target for WPX and other coral diseases for which pathogens are established and/or candidate pathogens are identified. Coral aetiology investigations completed to date suggest that changes in pathogen, host and/or environment alter the disease state and complicate diagnosis.

Large epidemics such as the recent Ebola crisis in West Africa occur when local efforts to contain outbreaks fail to overcome the probabilistic onward transmission to new locations. As a result, there may be large differences in total epidemic size from similar initial conditions. This work seeks to determine the extent to which the effects of behavior changes and metapopulation coupling on epidemic size can be characterized. While mathematical models have been developed to study local containment by social distancing, intervention and other behavior changes, their connection to larger-scale transmission is relatively underdeveloped. We make use of the assumption that behavior changes limit local transmission before susceptible depletion to develop a time-varying birth-death process capturing the dynamic decrease of the transmission rate associated with behavior changes. We derive an expression for the mean outbreak size of this model and show that the distribution of outbreak sizes is approximately geometric. This allows a probabilistic extension whereby infected individuals may initiate new outbreaks. From this model we characterize the overall epidemic size as a function of the behavior change rate and the probability that an infected individual starts a new outbreak. We find good agreement between the analytical results and stochastic simulations leading to novel findings including critical learning rates that demarcate large and small epidemic sizes.

Understanding the ecology and evolution of tick-borne parasites is the foundation for preventing and managing tick-borne diseases. Tick-borne diseases such as Lyme borreliosis, are an emerging health threat in America, Europe, and Asia. Certain strains of Borrelia burgdorferi (the etiological agent of Lyme borreliosis) sampled in nature appear to be rapidly cleared by murine hosts. These strains, unlike their inhost-persistent counterparts, are unlikely to manifest severe disease. Their emergence and abundance in North America is unclear. Understanding why strains adopt a persistent or rapid-clearing phenotype is a crucial question in Lyme biology. Using dynamic, data-driven infectivity profiles in a competitive, two-strain mathematical model, we show that these phenotypes are differentially favored under distinct ecological conditions (i.e. vector phenology). We argue these two phenotypes represent distinct parasite life-history strategies, impacting regional Lyme disease severity across North America.

Motivated by an array of infectious diseases that threaten wildlife populations, a simple metapopulation model (subpopulations connected by animal movement) is developed, which allows for both movement-based and environmental transmission. The model demonstrates that for a range of plausible parameterizations of environmental transmission, increased movement rate of animals between discrete habitats can lead to a decrease in the overall proportion of sites that are occupied. This can limit the ability of the rescue effect to ensure locally extinct populations become recolonized and can drive metapopulations down in size so that extinction by mechanisms other than disease may become more likely. It further highlights that, in the context of environmental transmission, the environmental persistence time of pathogens and the probability of acquiring infection by environmental transmission can affect host metapopulations both qualitatively and quantitatively. Additional spillover sources of infection from alternate reservoir hosts are also included in the model and a synthesis of all three types of transmission, acting alone or in combination, is performed revealing that movement-based transmission is the only necessary condition for a decline in the proportion of occupied sites with increasing movement rate, but that the presence of other types of transmission can reverse this qualitative result. By including the previously neglected role of environmental transmission, this work contributes to the general discussion of when dispersal by wild animals is beneficial or detrimental to populations experiencing infectious disease.

Recent evidence suggests that sexual contact may give rise to transmission of Ebola virus long after infection has been cleared from blood. We develop a simple mathematical model that incorporates contact transmission and sexual transmission parametrized from data relating to the 2013–2015 West African Ebola epidemic. The model explores scenarios where contact transmission is reduced following infection events, capturing behaviour change, and quantifies how these actions reducing transmission may be compromised by sexual transmission in terms of increasing likelihood, size and duration of outbreaks. We characterize the extent to which sexual transmission operates in terms of the probability of initial infection resolving to sexual infectiousness and the sexual transmission rate, and relate these parameters to the overall case burden. We find that sexual transmission can have large effects on epidemic dynamics (increasing attack ratios from 25% in scenarios without sexual transmission but with contact-transmission-reducing behaviour, up to 80% in equivalent scenarios with sexual transmission).

The epidemic threshold of the susceptible-infected-recovered (SIR) model is a boundary separating parameters that can permit epidemics from those that cannot. This threshold corresponds to points where the stability of the system’s equilibrium reaches zero. 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 for some individual variables of the model. Although these results are exact only in the limit of long-term observation of a large system, we find that they still provide useful insight into the behaviour of estimates from simulations 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 MERS-CoV as well as vaccine-preventable diseases such as measles.

Drug resistance is a long-standing economic, veterinary and human health concern in human and animal populations. Efficacy of prophylactic drug treatments targeting a particular pathogen is often short-lived, as drug-resistant pathogens evolve and reach high frequency in a treated population. Methods to combat drug resistance are usually costly, including use of multiple drugs that are applied jointly or sequentially, or development of novel classes of drugs. Alternatively, there is growing interest in exploiting untreated host populations, refugia , for the management of drug resistance. Refugia do not experience selection for resistance, and serve as a reservoir for native, drug-susceptible pathogens. The force of infection from refugia may dilute the frequency of resistant pathogens in the treated population, potentially at an acceptable cost in terms of overall disease burden. We examine this concept using a simple mathematical model that captures the core mechanisms of transmission and selection common to many host–pathogen systems. We identify the roles of selection and gene flow in determining the utility of refugia.

We investigated temporal and spatial trends in reporting of hemorrhagic disease (HD) in the midwestern and northeastern US using a 33-yr (1980-2012) questionnaire-based data set. This data set was supported by an additional 19 yr (1994-2012) of bluetongue virus (BTV) and epizootic hemorrhagic disease virus (EHDV) isolation results from clinically affected white-tailed deer (Odocoileus virginianus) in these regions. Both the number of counties that were reported positive for HD and the northern latitudinal range of reported HD increased with time. A similar increase was observed with both the number of states annually reporting HD and the number of counties where HD was reported. Large-scale outbreaks occurred in 1988, 1996, 2007, and 2012, and the scale of these individual outbreaks also increased with time. The predominant virus isolated from these regions was EHDV-2, but the prevalence of EHDV-6, which was first detected in 2006, appears to be increasing. Temporally, the extent of regional HD reporting was correlated with regional drought conditions. The significance of increases in reported HD and the incursions and establishment of new BTV and EHDV in the US currently are unknown.

Pandemic influenza remains a serious public health threat and the processes involved in the evolutionary emergence of pandemic influenza strains remain incompletely understood. Here, we develop a stochastic model for the evolutionary emergence of pandemic influenza, and use it to address three main questions. (i) What is the minimum annual number of avian influenza virus infections required in humans to explain the historical rate of pandemic emergence? (ii) Are such avian influenza infections in humans more likely to give rise to pandemic strains if they are driven by repeated cross-species introductions, or by low-level transmission of avian influenza viruses between humans? (iii) What are the most effective interventions for reducing the probability that an influenza strain with pandemic potential will evolve? Our results suggest that if evolutionary emergence of past pandemics has occurred primarily through viral reassortment in humans, then thousands of avian influenza virus infections in humans must have occurred each year for the past 250 years. Analyses also show that if there is epidemiologically significant variation among avian influenza virus genotypes, then avian virus outbreaks stemming from repeated cross-species transmission events result in a greater likelihood of a pandemic strain evolving than those caused by low-level transmission between humans. Finally, public health interventions aimed at reducing the duration of avian virus infections in humans give the greatest reduction in the probability that a pandemic strain will evolve.

We assess the effects of strain heterology (strains that are immunologically similar but not identical) on equine influenza in a vaccinated population. Using data relating to individual animals, for both homologous and heterologous vaccinees, we estimate distributions for the latent and infectious periods, quantify the risk of becoming infected in terms of the quantity of cross-reactive antibodies to a key surface protein of the virus (haemagglutinin) and estimate the probability of excreting virus (i.e. becoming infectious) given that infection has occurred. The data suggest that the infectious period, the risk of becoming infected (for a given vaccine-induced level of cross-reactive antibodies) and the probability of excreting virus are increased for heterologously vaccinated animals when compared with homologously vaccinated animals. The data are used to parameterize a modified susceptible, exposed, infectious and recovered/resistant (SEIR) model, which shows that these relatively small differences combine to have a large effect at the population level, where populations of heterologous vaccinees face a significantly increased risk of an epidemic occurring.

A stochastic model of equine influenza (EI) is constructed to assess the risk of an outbreak in a Thoroughbred population at a typical flat race training yard. The model is parameterised using data from equine challenge experiments conducted by the Animal Health Trust (relating to the latent and infectious period of animals) and also published data on previous epidemics (to estimate the transmission rate for equine influenza). Using 89 ponies, an empirical relationship between pre-challenge antibody and the probability of becoming infectious is established using logistic regression. Changes in antibody level over time are quantified using published and unpublished studies comprising 618 ponies and horses. A plausible Thoroughbred population is examined over the course of a year and the model is used to assess the risk of an outbreak of EI in the yard under the current minimum vaccination policy in the UK. The model is adapted to consider an alternative vaccination programme where the frequency of vaccination in older horses (2-year-olds and upwards) is increased. Model results show that this practical alternative would offer a significant increase in protection. Spread of infection between yards is also considered to ascertain the risk of secondary outbreaks.

Although of practical importance, the relationship between the duration of an epidemic and host spatial structure is poorly understood. Here we use a stochastic metapopulation model for the transmission of infection in a spatially structured host population. There are three qualitatively different regimes for the extinction time, which depend on patch population size, the within-patch basic reproductive number and the strength of coupling between patches. In the first regime, the extinction time for the metapopulation (i.e. from all patches) is approximately equal to the extinction time for a single patch. In the second regime, the metapopulation extinction time is maximal but also highly variable. In the third regime, the extinction time for the metapopulation (TE) is given by TE ¼ a + bn 1 ⁄ 2 where a is the local extinction time (i.e. from last patch), b is the transit time (i.e. the time taken for infection to spread from one patch to another) and n is the total number of patches.

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.

The authors develop a multi-type branching process model of the 2014 Liberian Ebola outbreak that incorporates the impacts of changes in behavior on potential transmission scenarios, thereby informing the path to containment of the epidemic., In 2014, a major epidemic of human Ebola virus disease emerged in West Africa, where human-to-human transmission has now been sustained for greater than 12 months. In the summer of 2014, there was great uncertainty about the answers to several key policy questions concerning the path to containment. What is the relative importance of nosocomial transmission compared with community-acquired infection? How much must hospital capacity increase to provide care for the anticipated patient burden? To which interventions will Ebola transmission be most responsive? What must be done to achieve containment? In recent years, epidemic models have been used to guide public health interventions. But, model-based policy relies on high quality causal understanding of transmission, including the availability of appropriate dynamic transmission models and reliable reporting about the sequence of case incidence for model fitting, which were lacking for this epidemic. To investigate the range of potential transmission scenarios, we developed a multi-type branching process model that incorporates key heterogeneities and time-varying parameters to reflect changing human behavior and deliberate interventions in Liberia. Ensembles of this model were evaluated at a set of parameters that were both epidemiologically plausible and capable of reproducing the observed trajectory. Results of this model suggested that epidemic outcome would depend on both hospital capacity and individual behavior. Simulations suggested that if hospital capacity was not increased, then transmission might outpace the rate of isolation and the ability to provide care for the ill, infectious, and dying. Similarly, the model suggested that containment would require individuals to adopt behaviors that increase the rates of case identification and isolation and secure burial of the deceased. As of mid-October, it was unclear that this epidemic would be contained even by 99% hospitalization at the planned hospital capacity. A new version of the model, updated to reflect information collected during October and November 2014, predicts a significantly more constrained set of possible futures. This model suggests that epidemic outcome still depends very heavily on individual behavior. Particularly, if future patient hospitalization rates return to background levels (estimated to be around 70%), then transmission is predicted to remain just below the critical point around R eff = 1. At the higher hospitalization rate of 85%, this model predicts near complete elimination in March to June, 2015., Author Summary There is considerable uncertainty regarding the steps needed to contain the ongoing Ebola crisis in West Africa, the timeline required to achieve control, and the projected burden of mortality. To address these issues, we develop a branching process model for Ebola transmission that focuses on offspring distributions (i.e., the numbers of new infections caused by each case). We use the model to assess the likely progression of Ebola in Liberia. The model assesses the feedback between new cases and hospital demand under a range of plausible intervention scenarios, particularly ramping-up of treatment facilities over time and increasing the number of individuals seeking hospital treatment through outreach and education. Transmission scenarios—to health care workers in hospitals, to caregivers in the community, to hospital visitors, and to individuals preparing bodies for funerals—are described by distinct offspring distributions based on available data. Results suggest that the outcome of the epidemic depends on both hospital capacity and individual behavior. Additionally, the model highlights the conditions under which transmission might have outpaced hospital capacity, and projects possible epidemic trajectories into 2015.