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Spatio-temporal patterns of the spread of infectious diseases are commonly driven by environmental and ecological factors. This is particularly true for vector-borne diseases because vector populations can be strongly affected by host distribution as well as by climatic and landscape variables. Here, we aim to identify environmental drivers for bluetongue virus (BTV), the causative agent of a major vector-borne disease of ruminants that has emerged multiple times in Europe in recent decades. In order to determine the importance of climatic, landscape and host-related factors affecting BTV diffusion across Europe, we fitted different phylogeographic models to a dataset of 113 time-stamped and geo-referenced BTV genomes, representing multiple strains and serotypes. Diffusion models using continuous space revealed that terrestrial habitat below 300 m altitude, wind direction and higher livestock densities were associated with faster BTV movement. Results of discrete phylogeographic analysis involving generalized linear models broadly supported these findings, but varied considerably with the level of spatial partitioning. Contrary to common perception, we found no evidence for average temperature having a positive effect on BTV diffusion, though both methodological and biological reasons could be responsible for this result. Our study provides important insights into the drivers of BTV transmission at the landscape scale that could inform predictive models of viral spread and have implications for designing control strategies.

Quantifying pathogen transmission in multi-host systems is difficult, as exemplified in bovine tuberculosis (bTB) systems, but is crucial for control. The agent of bTB, , persists in cattle populations worldwide, often where potential wildlife reservoirs exist. However, the relative contribution of different host species to bTB persistence is generally unknown. In Britain, the role of badgers in infection persistence in cattle is highly contentious, despite decades of research and control efforts. We applied Bayesian phylogenetic and machine-learning approaches to bacterial genome data to quantify the roles of badgers and cattle in infection dynamics in the presence of data biases. Our results suggest that transmission occurs more frequently from badgers to cattle than (10.4x in the most likely model) and that within-species transmission occurs at higher rates than between-species transmission for both. If representative, our results suggest that control operations should target both cattle and badgers.

Background Identifying the mechanisms driving disease risk is challenging for multi-host pathogens, such as Borrelia burgdorferi sensu lato (s.l.), the tick-borne bacteria causing Lyme disease. Deer are tick reproduction hosts but do not transmit B. burgdorferi s.l., whereas rodents and birds are competent transmission hosts. Here, we use a long-term deer exclosure experiment to test three mechanisms for how high deer density might shape B. burgdorferi s.l. prevalence in ticks: increased prevalence due to higher larval tick densities facilitating high transmission on rodents (M1); alternatively, reduced B. burgdorferi s.l. prevalence because more larval ticks feed on deer rather than transmission-competent rodents (dilution effect) (M2), potentially due to ecological cascades, whereby higher deer grazing pressure shortens vegetation which decreases rodent abundance thus reducing transmission (M3). Methods In a large enclosure where red deer stags were kept at high density (35.5 deer km −2 ), we used an experimental design consisting of eight plots of 0.23 ha, four of which were fenced to simulate the absence of deer and four that were accessible to deer. In each plot we measured the density of questing nymphs and nymphal infection prevalence in spring, summer and autumn, and quantified vegetation height and density, and small mammal abundance. Results Prevalence tended to be lower, though not conclusively so, in high deer density plots compared to exclosures (predicted prevalence of 1.0% vs 2.2%), suggesting that the dilution and cascade mechanisms might outweigh the increased opportunities for transmission mechanism. Presence of deer at high density led to shorter vegetation and fewer rodents, consistent with an ecological cascade. However, Lyme disease hazard (density of infected I. ricinus nymphs) was five times higher in high deer density plots due to tick density being 18 times higher. Conclusions High densities of tick reproduction hosts such as deer can drive up vector-borne disease hazard, despite the potential to simultaneously reduce pathogen prevalence. This has implications for environmental pathogen management and for deer management, although the impact of intermediate deer densities now needs testing. Graphical abstract

Landscape change and altered host abundance are major drivers of zoonotic pathogen emergence. Conservation and biodiversity management of landscapes and vertebrate communities can have secondary effects on vector-borne pathogen transmission that are important to assess. Here we review the potential implications of these activities on the risk of Lyme borreliosis in the United Kingdom. Conservation management activities include woodland expansion, management and restoration, deer management, urban greening and the release and culling of non-native species. Available evidence suggests that increasing woodland extent, implementing biodiversity policies that encourage ecotonal habitat and urban greening can increase the risk of Lyme borreliosis by increasing suitable habitat for hosts and the tick vectors. However, this can depend on whether deer population management is carried out as part of these conservation activities. Exclusion fencing or culling deer to low densities can decrease tick abundance and Lyme borreliosis risk. As management actions often constitute large-scale perturbation experiments, these hold great potential to understand underlying drivers of tick and pathogen dynamics. We recommend integrating monitoring of ticks and the risk of tick-borne pathogens with conservation management activities. This would help fill knowledge gaps and the production of best practice guidelines to reduce risks. This article is part of the themed issue ‘Conservation, biodiversity and infectious disease: scientific evidence and policy implications’.

Transmission of SARS-CoV-2 from humans to other species threatens wildlife conservation and may create novel sources of viral diversity for future zoonotic transmission. A variety of computational heuristics have been developed to pre-emptively identify susceptible host species based on variation in the angiotensin-converting enzyme 2 (ACE2) receptor used for viral entry. However, the predictive performance of these heuristics remains unknown. Using a newly compiled database of 96 species, we show that, while variation in ACE2 can be used by machine learning models to accurately predict animal susceptibility to sarbecoviruses (accuracy = 80.2%, binomial confidence interval [CI]: 70.8–87.6%), the sites informing predictions have no known involvement in virus binding and instead recapitulate host phylogeny. Models trained on host phylogeny alone performed equally well (accuracy = 84.4%, CI: 75.5–91.0%) and at a level equivalent to retrospective assessments of accuracy for previously published models. These results suggest that the predictive power of ACE2-based models derives from strong correlations with host phylogeny rather than processes which can be mechanistically linked to infection biology. Further, biased availability of ACE2 sequences misleads projections of the number and geographic distribution of at-risk species. Models based on host phylogeny reduce this bias, but identify a very large number of susceptible species, implying that model predictions must be combined with local knowledge of exposure risk to practically guide surveillance. Identifying barriers to viral infection or onward transmission beyond receptor binding and incorporating data which are independent of host phylogeny will be necessary to manage the ongoing risk of establishment of novel animal reservoirs of SARS-CoV-2. The COVID-19 pandemic affects humans, but also many of the animals we interact with. So far, humans have transmitted the SARS-CoV-2 virus to pet dogs and cats, a wide range of zoo animals, and even wildlife. Transmission of SARS-CoV-2 from humans to animals can lead to outbreaks amongst certain species, which can endanger animal populations and create new sources of human infections. Thus, careful monitoring of animal infections may help protect both animals and humans. Identifying which animals are susceptible to SARS-CoV-2 would help scientists monitor these species for outbreaks and viral circulation. Unfortunately, testing whether SARS-CoV-2 can infect different species in the laboratory is both time-consuming and expensive. To overcome this obstacle, researchers have used computational methods and existing data about the structure and genetic sequences of ACE2 receptors – the proteins on the cell surface that SARS-CoV-2 uses to enter the cell – to predict SARS-COV-2 susceptibility in different species. However, it remained unclear how accurate this approach was at predicting susceptibility in different animals, or whether their correct predictions indicated causal links between ACE2 variability and SARS-CoV-2 susceptibility. To assess the usefulness of this approach, Mollentze et al. started by using data on the ACE2 receptors from 96 different species and building a machine learning model to predict how susceptible those species might be to SARS-CoV-2. The susceptibility of these species had either been observed in natural infections – in zoos, for example – or had been assessed in the laboratory, so Mollentze et al. were able to use this information to determine how good both their model and previous approaches based on the sequence of ACE2 receptors were. The results showed that while the model was quite accurate (it correctly predicted susceptibility to SARS-CoV-2 about 80% of the time), its predictions were based on regions of the ACE2 receptors that were not known to interact with the virus. Instead, the regions that the machine learning model relied on were ones that tend to vary more the more distantly related two species are. This indicates that existing computational approaches are likely not relying on information about how ACE2 receptors interact with SARS-CoV-2 to predict susceptibility. Instead, they are simply using information on how closely related the different animal species are, which is much easier to source than data about ACE2 receptors. Indeed, the sequences of the ACE2 receptors in many species are unknown and the species for which this information is available come only from a few geographic areas. Mollentze et al. also showed that limiting the predictions about susceptibility to these species could mislead scientists when deciding which species and geographic areas to surveil for possible viral circulation. Instead, it may be more effective and cost-efficient to use animal relatedness to predict susceptibility to SARS-CoV-2. This makes it possible to make predictions for nearly all mammals, while being just as accurate as models based on ACE2 receptor data. However, Mollentze et al. point out that this approach would still fail to narrow down the number of animals that need to be monitored enough for it to be practical. Considering additional factors like how often the animals interact with humans or how prone they are to transmit the virus among themselves may help narrow it down more. Further research is therefore needed to identify the best multifactor approaches to identifying which animal populations should be monitored.

Genetic exchange by a process of genome-segment 'reassortment' represents an important mechanism for evolutionary change in all viruses with segmented genomes, yet in many cases a detailed understanding of its frequency and biological consequences is lacking. We provide a comprehensive assessment of reassortment in bluetongue virus (BTV), a globally important insect-borne pathogen of livestock, during recent outbreaks in Europe. Full-genome sequences were generated and analysed for over 150 isolates belonging to the different BTV serotypes that have emerged in the region over the last 5 decades. Based on this novel dataset we confirm that reassortment is a frequent process that plays an important and on-going role in evolution of the virus. We found evidence for reassortment in all ten segments without a significant bias towards any particular segment. However, we observed biases in the relative frequency at which particular segments were associated with each other during reassortment. This points to selective constraints possibly caused by functional relationships between individual proteins or genome segments and genome-wide epistatic interactions. Sites under positive selection were more likely to undergo amino acid changes in newly reassorted viruses, providing additional evidence for adaptive dynamics as a consequence of reassortment. We show that the live attenuated vaccines recently used in Europe have repeatedly reassorted with field strains, contributing to their genotypic, and potentially phenotypic, variability. The high degree of plasticity seen in the BTV genome in terms of segment origin suggests that current classification schemes that are based primarily on serotype, which is determined by only a single genome segment, are inadequate. Our work highlights the need for a better understanding of the mechanisms and epidemiological consequences of reassortment in BTV, as well as other segmented RNA viruses.

Hepatitis delta virus (HDV) is an unusual RNA agent that replicates using host machinery but exploits hepatitis B virus (HBV) to mobilize its spread within and between hosts. In doing so, HDV enhances the virulence of HBV. How this seemingly improbable hyperparasitic lifestyle emerged is unknown, but it underpins the likelihood that HDV and related deltaviruses may alter other host–virus interactions. Here, we show that deltaviruses diversify by transmitting between mammalian species. Among 96,695 RNA sequence datasets, deltaviruses infected bats, rodents, and an artiodactyl from the Americas but were absent from geographically overrepresented Old World representatives of each mammalian order, suggesting a relatively recent diversification within the Americas. Consistent with diversification by host shifting, both bat and rodent-infecting deltaviruses were paraphyletic, and coevolutionary modeling rejected cospeciation with mammalian hosts. In addition, a 2-y field study showed common vampire bats in Peru were infected by two divergent deltaviruses, indicating multiple introductions to a single host species. One vampire batassociated deltavirus was detected in the saliva of up to 35% of individuals, formed phylogeographically compartmentalized clades, and infected a sympatric bat, illustrating horizontal transmission within and between species on ecological timescales. Consistent absence of HBV-like viruses in two deltavirus-infected bat species indicated acquisitions of novel viral associations during the divergence of bat and human-infecting deltaviruses. Our analyses support an American zoonotic origin of HDV and reveal prospects for future cross-species emergence of deltaviruses. Given their peculiar life history, deltavirus host shifts will have different constraints and disease outcomes compared to ordinary animal pathogens.

The cross-species transmission (CST) of pathogens can have dramatic consequences, as highlighted by recent disease emergence events affecting human, animal and plant health. Understanding the ecological and evolutionary factors that increase the likelihood of disease agents infecting and establishing in a novel host is therefore an important research area. Previous work across different pathogens, including rabies virus (RABV), found that increased evolutionary distance between hosts reduces the frequency of cross-species transmission and of permanent host shifts. However, whether this effect of host relatedness still holds for transmission among recently diverged hosts is not well understood. We aimed to ask if high host relatedness can still increase the probability of a host shift between more recently diverged hosts, and the importance of this effect relative to ecological predictors. We first addressed this question by quantifying the CST frequency of RABV between North American bat species within the genus Myotis, using a multi-decade data set containing 128 nucleoprotein (N) RABV sequences from ten host species. We compared RABV CST frequency within Myotis to the rates of CST between nine genera of North American bat species. We then examined whether host relatedness or host range overlap better explains the frequency of CST seen between Myotis species. We found that at the within genus scale, host range overlap, rather than host relatedness best explains the frequency of CST events. Moreover, we found evidence of CST occurring among a higher proportion of species, and CST more frequently resulting in sustained transmission in the novel host in the Myotis dataset compared to the multi-genus dataset. Our results suggest that among recently diverged species, the ability to infect a novel host is no longer restricted by physiological barriers but instead is limited by physical contact. Our results improve predictions of where future CST events for RABV might occur and clarify the relationship between host divergence and pathogen emergence.

The contemporary surge in metagenomic sequencing has transformed knowledge of viral diversity in wildlife. However, evaluating which newly discovered viruses pose sufficient risk of infecting humans to merit detailed laboratory characterization and surveillance remains largely speculative. Machine learning algorithms have been developed to address this imbalance by ranking the relative likelihood of human infection based on viral genome sequences, but are not yet routinely applied to viruses at the time of their discovery. Here, we characterized viral genomes detected through metagenomic sequencing of feces and saliva from common vampire bats (Desmodus rotundus) and used these data as a case study in evaluating zoonotic potential using molecular sequencing data. Of 58 detected viral families, including 17 which infect mammals, the only known zoonosis detected was rabies virus; however, additional genomes were detected from the families Hepeviridae, Coronaviridae, Reoviridae, Astroviridae and Picornaviridae, all of which contain human-infecting species. In phylogenetic analyses, novel vampire bat viruses most frequently grouped with other bat viruses that are not currently known to infect humans. In agreement, machine learning models built from only phylogenetic information ranked all novel viruses similarly, yielding little insight into zoonotic potential. In contrast, genome composition-based machine learning models estimated different levels of zoonotic potential, even for closely related viruses, categorizing one out of four detected hepeviruses and two out of three picornaviruses as having high priority for further research. We highlight the value of evaluating zoonotic potential beyond ad hoc consideration of phylogeny and provide surveillance recommendations for novel viruses in a wildlife host which has frequent contact with humans and domestic animals.

Lyme disease is usually associated with forested habitats but has recently emerged on treeless islands in the Western Isles of Scotland. The environmental and human components of Lyme disease risk in open habitats remain unknown. We quantified the environmental hazard and risk factors for human tick bite exposure among treeless islands with low and high Lyme disease incidence in the Western Isles. We found a higher prevalence of Borrelia burgdorferi sensu lato-infected ticks on high-incidence than on low-incidence islands (6.4% vs. 0.7%); we also found that residents of high-incidence islands reported increased tick bite exposure. Most tick bites (72.7%) occurred <1 km from the home, including many in home gardens. Residents of high Lyme disease incidence islands reported increasing problems with ticks; many suggested changing deer distribution as a potential driver. We highlight the benefits of an integrated approach in understanding the factors that contribute to Lyme disease emergence.