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.

Drivers of African Filovirus (Ebola and Marburg) Outbreaks.

Outbreaks of African filoviruses often have high mortality, including more than 11,000 deaths among 28,562 cases during the West Africa Ebola outbreak of 2014-2016. Numerous studies have investigated the factors that contributed to individual filovirus outbreaks, but there has been little quantitative synthesis of this work. In addition, the ways in which the typical causes of filovirus outbreaks differ from other zoonoses remain poorly described. In this study, we quantify factors associated with 45 outbreaks of African filoviruses (ebolaviruses and Marburg virus) using a rubric of 48 candidate causal drivers. For filovirus outbreaks, we reviewed >700 peer-reviewed and gray literature sources and developed a list of the factors reported to contribute to each outbreak (i.e., a "driver profile" for each outbreak). We compare and contrast the profiles of filovirus outbreaks to 200 background outbreaks, randomly selected from a global database of 4463 outbreaks of bacterial and viral zoonotic diseases. We also test whether the quantitative patterns that we observed were robust to the influences of six covariates, country-level factors such as gross domestic product, population density, and latitude that have been shown to bias global outbreak data. We find that, regardless of whether covariates are included or excluded from models, the driver profile of filovirus outbreaks differs from that of background outbreaks. Socioeconomic factors such as trade and travel, wild game consumption, failures of medical procedures, and deficiencies in human health infrastructure were more frequently reported in filovirus outbreaks than in the comparison group. Based on our results, we also present a review of drivers reported in at least 10% of filovirus outbreaks, with examples of each provided.

Protective population behavior change in outbreaks of emerging infectious disease.

BACKGROUND: During outbreaks of emerging and re-emerging infections, the lack of effective drugs and vaccines increases reliance on non-pharmacologic public health interventions and behavior change to limit human-to-human transmission. Interventions that increase the speed with which infected individuals remove themselves from the susceptible population are paramount, particularly isolation and hospitalization. Ebola virus disease (EVD), Severe Acute Respiratory Syndrome (SARS), and Middle East Respiratory Syndrome (MERS) are zoonotic viruses that have caused significant recent outbreaks with sustained human-to-human transmission. METHODS: This investigation quantified changing mean removal rates (MRR) and days from symptom onset to hospitalization (DSOH) of infected individuals from the population in seven different outbreaks of EVD, SARS, and MERS, to test for statistically significant differences in these metrics between outbreaks. RESULTS: We found that epidemic week and viral serial interval were correlated with the speed with which populations developed and maintained health behaviors in each outbreak. CONCLUSIONS: These findings highlight intrinsic population-level changes in isolation rates in multiple epidemics of three zoonotic infections with established human-to-human transmission and significant morbidity and mortality. These data are particularly useful for disease modelers seeking to forecast the spread of emerging pathogens.

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.

Accuracy of climate-based forecasts of pathogen spread.

Species distribution models (SDMs) are a tool for predicting the eventual geographical range of an emerging pathogen. Most SDMs, however, rely on an assumption of equilibrium with the environment, which an emerging pathogen, by definition, has not reached. To determine if some SDM approaches work better than others for modelling the spread of emerging, non-equilibrium pathogens, we studied time-sensitive predictive performance of SDMs for Batrachochytrium dendrobatidis, a devastating infectious fungus of amphibians, using multiple methods trained on time-incremented subsets of the available data. We split our data into timeline-based training and testing sets, and evaluated models on each set using standard performance criteria, including AUC, kappa, false negative rate and the Boyce index. Of eight models examined, we found that boosted regression trees and random forests performed best, closely followed by MaxEnt. As expected, predictive performance generally improved with the length of time series used for model training. These results provide information on how quickly the potential extent of an emerging disease may be determined, and identify which modelling frameworks are likely to provide useful information during the early phases of pathogen expansion.