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Bats are the natural reservoirs of a number of high-impact viral zoonoses. We present a quantitative analysis to address the hypothesis that bats are unique in their propensity to host zoonotic viruses based on a comparison with rodents, another important host order. We found that bats indeed host more zoonotic viruses per species than rodents, and we identified life-history and ecological factors that promote zoonotic viral richness. More zoonotic viruses are hosted by species whose distributions overlap with a greater number of other species in the same taxonomic order (sympatry). Specifically in bats, there was evidence for increased zoonotic viral richness in species with smaller litters (one young), greater longevity and more litters per year. Furthermore, our results point to a new hypothesis to explain in part why bats host more zoonotic viruses per species: the stronger effect of sympatry in bats and more viruses shared between bat species suggests that interspecific transmission is more prevalent among bats than among rodents. Although bats host more zoonotic viruses per species, the total number of zoonotic viruses identified in bats (61) was lower than in rodents (68), a result of there being approximately twice the number of rodent species as bat species. Therefore, rodents should still be a serious concern as reservoirs of emerging viruses. These findings shed light on disease emergence and perpetuation mechanisms and may help lead to a predictive framework for identifying future emerging infectious virus reservoirs.

Rift Valley fever virus (RVFV) causes morbidity and mortality in humans and domestic ungulates in sub-Saharan Africa, Egypt, and the Arabian Peninsula. Mosquito vectors transmit RVFV between vertebrates by bite, and also vertically to produce infectious progeny. Arrival of RVFV into the United States by infected mosquitoes or humans could result in significant impacts on food security, human health, and wildlife health. Elucidation of the vectors involved in the post-introduction RVFV ecology is paramount to rapid implementation of vector control. We performed vector competence experiments in which field-collected mosquitoes were orally exposed to an epidemic strain of RVFV via infectious blood meals. We targeted floodwater Aedes species known to feed on cattle, and/or deer species ( Aedes melanimon Dyar, Aedes increpitus Dyar, Aedes vexans [Meigen]). Two permanent-water-breeding species were targeted as well: Culiseta inornata (Williston) of unknown competence considering United States populations, and Culex tarsalis Coquillett as a control species for which transmission efficiency is known. We tested the potential for midgut infection, midgut escape (dissemination), ovarian infection (vertical transmission), and transmission by bite (infectious saliva). Tissues were assayed by plaque assay and RT-qPCR, to quantify infectious virus and confirm virus identity. Tissue infection data were analyzed using a within-host model under a Bayesian framework to determine the probabilities of infection outcomes (midgut-limited infection, disseminated infection, etc.) while estimating barriers to infection between tissues. Permanent-water-breeding mosquitoes ( Cx . tarsalis and Cs . inornata ) exhibited more efficient horizontal transmission, as well as potential for vertical transmission, which is contrary to the current assumptions of RVFV ecology. Barrier estimates trended higher for Aedes spp ., suggesting systemic factors in the differences between these species and Cx . tarsalis and Cs . inornata . These data indicate higher potential for vertical transmission than previously appreciated, and support the consensus of RVFV transmission including a broad range of potential vectors.

A considerable amount of disease is transmitted from animals to humans and many of these zoonoses are neglected tropical diseases. As outbreaks of SARS, avian influenza and Ebola have demonstrated, however, zoonotic diseases are serious threats to global public health and are not just problems confined to remote regions. There are two fundamental, and poorly studied, stages of zoonotic disease emergence: 'spillover', i.e. transmission of pathogens from animals to humans, and 'stuttering transmission', i.e. when limited human-to-human infections occur, leading to self-limiting chains of transmission. We developed a transparent, theoretical framework, based on a generalization of Poisson processes with memory of past human infections, that unifies these stages. Once we have quantified pathogen dynamics in the reservoir, with some knowledge of the mechanism of contact, the approach provides a tool to estimate the likelihood of spillover events. Comparisons with independent agent-based models demonstrates the ability of the framework to correctly estimate the relative contributions of human-to-human vs animal transmission. As an illustrative example, we applied our model to Lassa fever, a rodent-borne, viral haemorrhagic disease common in West Africa, for which data on human outbreaks were available. The approach developed here is general and applicable to a range of zoonoses. This kind of methodology is of crucial importance for the scientific, medical and public health communities working at the interface between animal and human diseases to assess the risk associated with the disease and to plan intervention and appropriate control measures. The Lassa case study revealed important knowledge gaps, and opportunities, arising from limited knowledge of the temporal patterns in reporting, abundance of and infection prevalence in, the host reservoir.

Influenza A viruses present a major challenge for animal and human health. They circulate widely in wild waterfowl and frequently spillover into poultry, emphasizing the need for risk-based surveillance in wild birds and an understanding of the relative importance of different transmission mechanisms. We addressed this objective with a replicated ( N = 6) experimental infection study in which we serially exposed eight cohorts of four naïve contact mallards to an experimentally infected mallard and a shared water pool. Viral concentration in the water was a better predictor of transmission than several direct measures of viral shedding in the focal duck. Our data provide quantification of transmission probability and its variation throughout the infectious period of an infected duck. Our findings highlight the need to consider environmental surveillance in risk-based surveillance planning and provide realistic parameters for identifying optimal control strategies using epidemiological inference. Wild birds are the natural reservoir hosts of influenza A viruses. Highly pathogenic strains of influenza A viruses pose risks to wild birds, poultry, and human health. Thus, understanding how these viruses are transmitted between birds is critical. We conducted an experiment where we experimentally infected mallards which are ducks that are commonly exposed to influenza viruses. We exposed several contact ducks to the experimentally infected duck to estimate the probability that a contact duck would become infected from either exposure to the virus shed directly from the infected duck or shared water contaminated with the virus from the infected duck. We found that environmental transmission from contaminated water best predicted the probability of transmission to naïve contact ducks, relatively low levels of virus in the water were sufficient to cause infection, and the probability of a naïve duck becoming infected varied over time.

Rabies is an acute viral infection that is typically fatal. Most rabies modeling has focused on disease dynamics and control within terrestrial mammals (e.g., raccoons and foxes). As such, rabies in bats has been largely neglected until recently. Because bats have been implicated as natural reservoirs for several emerging zoonotic viruses, including SARS-like corona viruses, henipaviruses, and lyssaviruses, understanding how pathogens are maintained within a population becomes vital. Unfortunately, little is known about maintenance mechanisms for any pathogen in bat populations. We present a mathematical model parameterized with unique data from an extensive study of rabies in a Colorado population of big brown bats (Eptesicus fuscus) to elucidate general maintenance mechanisms. We propose that life history patterns of many species of temperate-zone bats, coupled with sufficiently long incubation periods, allows for rabies virus maintenance. Seasonal variability in bat mortality rates, specifically low mortality during hibernation, allows long-term bat population viability. Within viable bat populations, sufficiently long incubation periods allow enough infected individuals to enter hibernation and survive until the following year, and hence avoid an epizootic fadeout of rabies virus. We hypothesize that the slowing effects of hibernation on metabolic and viral activity maintains infected individuals and their pathogens until susceptibles from the annual birth pulse become infected and continue the cycle. This research provides a context to explore similar host ecology and viral dynamics that may explain seasonal patterns and maintenance of other bat-borne diseases.

The grassland biome is an important sink for atmospheric methane (CH4), a major greenhouse gas. There is considerable uncertainty in the grassland CH4 sink capacity due to diverse environmental gradients in which grasslands occur, and many environmental conditions can affect abiotic (e.g., CH4 diffusivity into soils) and biotic (e.g., methanotrophy) factors that determine spatial and temporal CH4 dynamics. We investigated the relative importance of a soil's gas diffusivity versus net methanotroph activity in 22 field plots in seven sites distributed across the US Great Plains by making approximately biweekly measures during the growing seasons over 3 years. We quantified net methanotroph activity and diffusivity by using an approach combining a gas tracer, chamber headspace measurements, and a mathematical model. At each plot, we also measured environmental characteristics, including water‐filled pore space (WFPS), soil temperature, and inorganic nitrogen contents, and examined the relative importance of these for controlling diffusivity and net methanotroph activity. At most of the plots across the seven sites, CH4 uptake rates were consistently greatest when WFPS was intermediate at the plot level. Our results show that variation in net methanotroph activity was more important than diffusivity in explaining temporal variations in net CH4 uptake, but the two factors were equally important for driving spatial variation across the seven sites. WFPS was a significant predictor for diffusivity only in plots with sandy soils. WFPS was the most important control on net methanotroph activity, with net methanotroph activity showing a parabolic response to WFPS (concave down), and the shape of this response differed significantly among sites. Moreover, we found that the WFPS level at peak net methanotroph activity was strongly correlated with the mean annual precipitation of the site. These results suggest that the local precipitation regime determines unique sensitivity of CH4 uptake rates to soil moisture. Our findings indicate that grassland CH4 uptake may be predicted using local soil water conditions. More variable soil moisture, potentially induced through predicted future extremes of rainfall and drought, could reduce grassland CH4 sink capacity in the future.

Our ability to effectively prevent the transmission of the dengue virus through targeted control of its vector, Aedes aegypti , depends critically on our understanding of the link between mosquito abundance and human disease risk. Mosquito and clinical surveillance data are widely collected, but linking them requires a modeling framework that accounts for the complex non-linear mechanisms involved in transmission. Most critical are the bottleneck in transmission imposed by mosquito lifespan relative to the virus’ extrinsic incubation period, and the dynamics of human immunity. We developed a differential equation model of dengue transmission and embedded it in a Bayesian hierarchical framework that allowed us to estimate latent time series of mosquito demographic rates from mosquito trap counts and dengue case reports from the city of Vitória, Brazil. We used the fitted model to explore how the timing of a pulse of adult mosquito control influences its effect on the human disease burden in the following year. We found that control was generally more effective when implemented in periods of relatively low mosquito mortality (when mosquito abundance was also generally low). In particular, control implemented in early September (week 34 of the year) produced the largest reduction in predicted human case reports over the following year. This highlights the potential long-term utility of broad, off-peak-season mosquito control in addition to existing, locally targeted within-season efforts. Further, uncertainty in the effectiveness of control interventions was driven largely by posterior variation in the average mosquito mortality rate (closely tied to total mosquito abundance) with lower mosquito mortality generating systems more vulnerable to control. Broadly, these correlations suggest that mosquito control is most effective in situations in which transmission is already limited by mosquito abundance.

BACKGROUND: Zoonotic infections, which transmit from animals to humans, form the majority of new human pathogens. Following zoonotic transmission, the pathogen may already have, or may acquire, the ability to transmit from human to human. With infections such as Lassa fever (LF), an often fatal, rodent-borne, hemorrhagic fever common in areas of West Africa, rodent-to-rodent, rodent-to-human, human-to-human and even human-to-rodent transmission patterns are possible. Indeed, large hospital-related outbreaks have been reported. Estimating the proportion of transmission due to human-to-human routes and related patterns (e.g. existence of super-spreaders), in these scenarios is challenging, but essential for planned interventions. METHODOLOGY/PRINCIPAL FINDINGS: Here, we make use of an innovative modeling approach to analyze data from published outbreaks and the number of LF hospitalized patients to Kenema Government Hospital in Sierra Leone to estimate the likely contribution of human-to-human transmission. The analyses show that almost [Formula: see text] of the cases at KGH are secondary cases arising from human-to-human transmission. However, we found much of this transmission is associated with a disproportionally large impact of a few individuals ('super-spreaders'), as we found only [Formula: see text] of human cases result in an effective reproduction number (i.e. the average number of secondary cases per infectious case) [Formula: see text], with a maximum value up to [Formula: see text]. CONCLUSIONS/SIGNIFICANCE: This work explains the discrepancy between the sizes of reported LF outbreaks and a clinical perception that human-to-human transmission is low. Future assessment of risks of LF and infection control guidelines should take into account the potentially large impact of super-spreaders in human-to-human transmission. Our work highlights several neglected topics in LF research, the occurrence and nature of super-spreading events and aspects of social behavior in transmission and detection.

Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.

Few studies have examined the genetic population structure of vector-borne microparasites in wildlife, making it unclear how much these systems can reveal about the movement of their associated hosts. This study examined the complex host–vector–microbe interactions in a system of bats, wingless ectoparasitic bat flies (Nycteribiidae), vector-borne microparasitic bacteria (Bartonella) and bacterial endosymbionts of flies (Enterobacterales) across an island chain in the Gulf of Guinea, West Africa. Limited population structure was found in bat flies and Enterobacterales symbionts compared to that of their hosts. Significant isolation by distance was observed in the dissimilarity of Bartonella communities detected in flies from sampled populations of Eidolon helvum bats. These patterns indicate that, while genetic dispersal of bats between islands is limited, some non-reproductive movements may lead to the dispersal of ectoparasites and associated microbes. This study deepens our knowledge of the phylogeography of African fruit bats, their ectoparasites and associated bacteria. The results presented could inform models of pathogen transmission in these bat populations and increase our theoretical understanding of community ecology in host–microbe systems.