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Anthropogenic habitat modification can lead to chronic stress in wildlife. This can result in immunosuppression and higher disease prevalence. Chronically stressed individuals typically have elevated baseline GCs and decreased body condition. GCs are called FGMs when excreted in feces and can be used to noninvasively evaluate stress in free-ranging wildlife. In the deer mouse (Peromyscus maniculatus)–SNV system, SNV prevalence is higher in deer mice at peridomestic settings, which are human-modified habitats. This is problematic because SNV causes a fatal disease in humans, and thus the higher SNV prevalence may lead to higher risk of infection for humans. In our study, we hypothesized that SNV prevalence would be higher in deer mice at human-modified habitats due to chronic stress. To test our hypothesis, we compared two stress measures (i.e., baseline FGMs and body condition scores) in deer mice from one peridomestic and one sylvan grid over 2 months. Captured deer mice were tagged, weighed, sexed and sampled for feces and blood and were evaluated for reproductive status and body condition before release. Blood samples were analysed for SNV antibodies, and fecal samples were evaluated for FGMs. We found higher deer mouse numbers at the sylvan grid. There were no differences in baseline FGM levels between peridomestic and sylvan populations. However, peridomestic deer mice had overall lower body condition. Given the low SNV prevalence across both grids, we were unable to examine potential correlations between SNV prevalence and chronic stress. Regardless, we conclude that deer mice at human-modified habitats may not be chronically stressed, which may suggest that higher SNV prevalence at peridomestic settings may not be the result of chronic stress. Although we did find that peridomestic deer mice had lower body condition, this may not have been related to chronic stress because there were no differences in baseline FGMs. Longer studies with more site replication are needed to validate and expand on our findings. Our preliminary study adds to the existing body of knowledge that examines relationships between stress physiology and disease prevalence in human-modified environments.

Ecologists have long sought to understand space use and mechanisms underlying patterns observed in nature. We developed an optimality landscape and mechanistic territory model to understand mechanisms driving space use and compared model predictions to empirical reality. We demonstrate our approach using grey wolves (Canis lupus). In the model, simulated animals selected territories to economically acquire resources by selecting patches with greatest value, accounting for benefits, costs and trade-offs of defending and using space on the optimality landscape. Our approach successfully predicted and explained first- and second-order space use of wolves, including the population's distribution, territories of individual packs, and influences of prey density, competitor density, human-caused mortality risk and seasonality. It accomplished this using simple behavioural rules and limited data to inform the optimality landscape. Results contribute evidence that economical territory selection is a mechanistic bridge between space use and animal distribution on the landscape. This approach and resulting gains in knowledge enable predicting effects of a wide range of environmental conditions, contributing to both basic ecological understanding of natural systems and conservation. We expect this approach will demonstrate applicability across diverse habitats and species, and that its foundation can help continue to advance understanding of spatial behaviour.

Nonlinear dynamics, where a change in the input is not proportional to a change in the output, are often found throughout nature, for example in biochemical kinetics. Because of the complex suite of interacting abiotic and biotic variables present in ecosystems, animal population dynamics are often thought to be driven in a nonlinear, state-dependent fashion. However, so far these have only been identified in model organisms and some natural systems. Here we show that nonlinear population dynamics are ubiquitous in nature. We use nonlinear forecasting to analyse 747 datasets of 228 species to find that insect population trends were highly nonlinear (74%), followed by mammals (58%), bony fish (49%) and birds (35%). This indicates that linear, equilibrium-based model assumptions may fail at predicting population dynamics across a wide range of animal taxa. We show that faster-reproducing animals are more likely to have nonlinear and high-dimensional dynamics, supporting past ecological theory. Lastly, only a third of time series were predictable beyond two years; therefore, the ability to predict animal population trends using these methods may be limited. Our results suggest that the complex dynamics necessary to cause regime shifts and other transitions may be inherent in a wide variety of animals. Analysing the population dynamics of 228 terrestrial and aquatic species, the authors show that nonlinearity is a ubiquitous feature of animal populations and is more prevalent among fast-reproducing species.

Ecological and environmental factors can influence the transmission of infectious diseases. They can accomplish this via effects on host susceptibility and exposure to infection, which are governed by host physiology and behavior, respectively. To better inform disease control, more information is needed about how extrinsic factors affect physiological and behavioral processes that determine transmission. We investigated how heterospecific competitors and seasonality may influence host susceptibility and intraspecific contact rates using a directly transmitted disease system, the North American deer mouse (Peromyscus maniculatus)—Sin Nombre hantavirus (SNV) system. In grasslands of western Montana, USA, deer mice compete with dominant voles (Microtus spp.) and shrews (Sorex spp.) and experience a seasonal temperate climate. Higher SNV transmission occurs primarily during spring/summer, when changes in physiology and behavior may serve as influential contributors. We hypothesized that (1) voles, and to a lesser extent shrews, will induce chronic stress, suppress immunity, and may change contact rates of deer mice; and (2) during spring/summer, deer mice may experience chronic stress, suppressed immunity, and higher contact rates, which may help explain the reported seasonality in SNV transmission. Over two years, we trapped small mammals at four grids in western Montana. Deer mice were sampled for feces and blood and evaluated for scar numbers, demography, and body condition scores (BCSs). We evaluated stress physiology with fecal corticosterone metabolites (FCMs), neutrophil/lymphocyte (N/L) ratios and BCSs, immunity with white blood cell (WBC) counts, and contact rates with scar numbers. We found that shrew density was negatively associated with stress response FCMs, suggestive of chronic stress. Additionally, although complex interactions existed, shrew and vole densities were negatively associated with BCSs, but differentially with scar numbers. N/L ratios were higher in spring/summer, whereas WBC counts were lower in summer, suggestive of chronic stress and suppressed immunity, respectively. Our results suggest that (1) heterospecific competitors may differentially influence disease transmission via stress physiology and contact rates, and that (2) chronic stress, suppressed immunity, and higher contact rates may help explain why higher SNV transmission has been previously reported during spring/summer in Montana. Our findings may extend to other directly transmitted disease systems.

The "Cort-Fitness" hypothesis predicts a negative relationship between baseline glucocorticoids (GCs) and fitness, although evidence for this hypothesis remains mixed. Such ambiguity could partially exist because blood GCs, typically used in field studies, can fluctuate too rapidly to measure accurately, while the relationship between GCs and trappability is often neglected. Here, by addressing these factors, we examined relationships between GC measures and survival of North American deermice (Peromyscus maniculatus; hereafter deermice) as a model system. To do this, we used more stable GC measures, including the integrated measures of baseline and stress response fecal corticosterone metabolites (FCMs), and downstream measures of neutrophil/lymphocyte ratio (N/L ratio), and body condition score (BCS), to characterize their relationships with survival and trappability. Over two years, deermice were live-trapped monthly, evaluated for BCS, and sampled for feces and blood. Stress response FCMs were evaluated only at first capture. Mark-recapture models, with GC measures as predictors of either survival or trappability, were compared to identify top models. We found that stress response FCMs negatively predicted trappability, and weaker evidence that BCS positively predicted survival. Although the latter provides some support for the "Cort-Fitness" hypothesis, there was no support when using integrated measures. Instead, our findings suggest that deermice with a lower adrenocortical response (i.e. stress response FCMs) were more likely to be captured. Therefore, GC-trappability relationships must be investigated in field studies to avoid linking the wrong GC profile to fitness, and physiological measures other than blood GCs may be useful for detecting GC-fitness patterns.

Ecosystems across the globe receive elevated inputs of nutrients, but the consequences of this for soil fungal guilds that mediate key ecosystem functions remain unclear. We find that nitrogen and phosphorus addition to 25 grasslands distributed across four continents promotes the relative abundance of fungal pathogens, suppresses mutualists, but does not affect saprotrophs. Structural equation models suggest that responses are often indirect and primarily mediated by nutrient-induced shifts in plant communities. Nutrient addition also reduces co-occurrences within and among fungal guilds, which could have important consequences for belowground interactions. Focusing only on plots that received no nutrient addition, soil properties influence pathogen abundance globally, whereas plant community characteristics influence mutualists, and climate influence saprotrophs. We show consistent, guild-level responses that enhance our ability to predict shifts in soil function related to anthropogenic eutrophication, which can have longer-term consequences for plant communities., Anthropogenic nutrient enrichment may drive shifts in soil microbial communities. Here, the authors analyse nitrogen and phosphorus addition effects on soil fungi in a distributed grassland experiment across four continents, finding promotion of pathogens, suppression of mutualists, and no shifts in saprotrophs.

We generated reference ranges for seasonal leukocyte differential counts of the free-ranging deermouse (

Nonlinear dynamics, where a change in the input is not proportional to a change in the output, are often found throughout nature, for example in biochemical kinetics. Because of the complex suite of interacting abiotic and biotic variables present in ecosystems, animal population dynamics are often thought to be driven in a nonlinear, state-dependent fashion. However, so far these have only been identified in model organisms and some natural systems. Here we show that nonlinear population dynamics are ubiquitous in nature. We use nonlinear forecasting to analyse 747 datasets of 228 species to find that insect population trends were highly nonlinear (74%), followed by mammals (58%), bony fish (49%) and birds (35%). This indicates that linear, equilibrium-based model assumptions may fail at predicting population dynamics across a wide range of animal taxa. We show that faster-reproducing animals are more likely to have nonlinear and high-dimensional dynamics, supporting past ecological theory. Lastly, only a third of time series were predictable beyond two years; therefore, the ability to predict animal population trends using these methods may be limited. Our results suggest that the complex dynamics necessary to cause regime shifts and other transitions may be inherent in a wide variety of animals.

In this era of unprecedented biodiversity loss and increased zoonotic disease emergence, it is imperative to understand the effects of biodiversity on zoonotic pathogen dynamics in wildlife. Whether increasing biodiversity should lead to a decrease or increase in infection prevalence, termed the dilution and amplification effects, respectively, has been hotly debated in disease ecology. Sin Nombre hantavirus, which has an ∼35% mortality rate when it spills over into humans, occurs at a lower prevalence in the reservoir host, the North American deermouse, in areas with higher small mammal diversity-a dilution effect. However, the mechanism driving this relationship is not understood. Using a mechanistic mathematical model of infection dynamics and a unique long-term, high-resolution, multisite dataset, it appears that the observed dilution effect is a result of increasing small-mammal diversity leading to decreased deermouse population density and, subsequently, prevalence (a result of density-dependent transmission). However, once density is taken into account, there is an increase in the transmission rate at sites with higher diversity-a component amplification effect. Therefore, dilution and amplification are occurring at the same time in the same host-pathogen system; there is a component amplification effect (increase in transmission rate), but overall a net dilution because the effect of diversity on reservoir host population density is stronger. These results suggest we should focus on how biodiversity affects individual mechanisms that drive prevalence and their relative strengths if we want to make generalizable predictions across host-pathogen systems.

Predicting outbreaks of zoonotic infections in reservoir hosts that live in highly fluctuating environments, such as Sin Nombre virus (SNV) in deer mice, is particularly challenging because host populations vary widely in response to environmental conditions and the relationship between field infection rates and abundance often appears to contradict conventional theory. Using a stage-structured host-pathogen model parameterized and cross-validated from a unique 15-year data set, we show how stochastic population fluctuations can lead to predictable dynamics of SNV in deer mice. Significant variation in host abundance and the basic reproductive number of the virus results in intermittent crossing of the critical host population density necessary for SNV endemicity and frequent local extinctions. When environmental conditions favor growth of the host population above the threshold, host–pathogen interactions lead to delayed density dependence in reservoir prevalence. The resultant ecological delay may provi...

High metabolism and body temperatures of flying bats might enable them to host many viruses., Bats are sources of high viral diversity and high-profile zoonotic viruses worldwide. Although apparently not pathogenic in their reservoir hosts, some viruses from bats severely affect other mammals, including humans. Examples include severe acute respiratory syndrome coronaviruses, Ebola and Marburg viruses, and Nipah and Hendra viruses. Factors underlying high viral diversity in bats are the subject of speculation. We hypothesize that flight, a factor common to all bats but to no other mammals, provides an intensive selective force for coexistence with viral parasites through a daily cycle that elevates metabolism and body temperature analogous to the febrile response in other mammals. On an evolutionary scale, this host–virus interaction might have resulted in the large diversity of zoonotic viruses in bats, possibly through bat viruses adapting to be more tolerant of the fever response and less virulent to their natural hosts.

The application of network analysis to cattle shipments broadens our understanding of shipment patterns beyond pairwise interactions to the network as a whole. Such a quantitative description of cattle shipments in the U.S. can identify trade communities, describe temporal shipment patterns, and inform the design of disease surveillance and control strategies. Here, we analyze a longitudinal dataset of beef and dairy cattle shipments from 2009 to 2011 in the United States to characterize communities within the broader cattle shipment network, which are groups of counties that ship mostly to each other. Because shipments occur over time, we aggregate the data at various temporal scales to examine the consistency of network and community structure over time. Our results identified nine large (>50 counties) communities based on shipments of beef cattle in 2009 aggregated into an annual network and nine large communities based on shipments of dairy cattle. The size and connectance of the shipment network was highly dynamic; monthly networks were smaller than yearly networks and revealed seasonal shipment patterns consistent across years. Comparison of the shipment network over time showed largely consistent shipping patterns, such that communities identified on annual networks of beef and diary shipments from 2009 still represented 41-95% of shipments in monthly networks from 2009 and 41-66% of shipments from networks in 2010 and 2011. The temporal aspects of cattle shipments suggest that future applications of the U.S. cattle shipment network should consider seasonal shipment patterns. However, the consistent within-community shipping patterns indicate that yearly communities could provide a reasonable way to group regions for management.

Understanding the environmental drivers of zoonotic reservoir and human interactions is crucial to understanding disease risk, but these drivers are poorly predicted. We propose a mechanistic understanding of human-reservoir interactions, using hantavirus pulmonary syndrome as a case study. Crucial processes underpinning the disease's incidence remain poorly studied, including the connectivity among natural and peridomestic deer mouse host activity, virus transmission, and human exposure. We found that disease cases were greatest in arid states and declined exponentially with increasing precipitation. Within arid environments, relatively rare climatic conditions (e.g., El Nino) are associated with increased rainfall and reservoir abundance, producing more frequent virus transmission and host dispersal. We suggest that deer mice increase their occupancy of peridomestic structures during spring-summer, amplifying intraspecific transmission and human infection risk. Disease incidence in arid states may increase with predicted climatic changes. Mechanistic approaches incorporating reservoir behavior, reservoir-human interactions, and pathogen spillover could enhance our understanding of global hantavirus ecology, with applications to other directly transmitted zoonoses.

Infectious disease ecology has recently raised its public profile beyond the scientific community due to the major threats that wildlife infections pose to biological conservation, animal welfare, human health and food security. As we start unravelling the full extent of emerging infectious diseases, there is an urgent need to facilitate multidisciplinary research in this area. Even though research in ecology has always had a strong theoretical component, cultural and technical hurdles often hamper direct collaboration between theoreticians and empiricists. Building upon our collective experience of multidisciplinary research and teaching in this area, we propose practical guidelines to help with effective integration among mathematical modelling, fieldwork and laboratory work. Modelling tools can be used at all steps of a field-based research programme, from the formulation of working hypotheses to field study design and data analysis. We illustrate our model-guided fieldwork framework with two case studies we have been conducting on wildlife infectious diseases: plague transmission in prairie dogs and lyssavirus dynamics in American and African bats. These demonstrate that mechanistic models, if properly integrated in research programmes, can provide a framework for holistic approaches to complex biological systems.

Pathogens causing acute disease and death or lasting immunity require specific spatial or temporal processes to persist in populations. Host traits, such as maternally-derived antibody (MDA) and seasonal birthing affect infection maintenance within populations. Our study objective is to understand how viral and host traits lead to population level infection persistence when the infection can be fatal. We collected data on African fruit bats and a rabies-related virus, Lagos bat virus (LBV), including through captive studies. We incorporate these data into a mechanistic model of LBV transmission to determine how host traits, including MDA and seasonal birthing, and viral traits, such as incubation periods, interact to allow fatal viruses to persist within bat populations. Captive bat studies supported MDA presence estimated from field data. Captive bat infection-derived antibody decayed more slowly than MDA, and while faster than estimates from the field, supports field data that suggest antibody persistence may be lifelong. Unobserved parameters were estimated by particle filtering and suggest only a small proportion of bats die of disease. Pathogen persistence in the population is sensitive to this proportion, along with MDA duration and incubation period. Our analyses suggest MDA produced bats and prolonged virus incubation periods allow viral maintenance in adverse conditions, such as a lethal pathogen or strongly seasonal resource availability for the pathogen in the form of seasonally pulsed birthing.

1. Since Sin Nombre virus was discovered in the U.S. in 1993, longitudinal studies of the rodent reservoir host, the deer mouse (Peromyscus maniculatus) have demonstrated a qualitative correlation among mouse population dynamics and risk of hantavirus pulmonary syndrome (HPS) in humans, indicating the importance of understanding deer mouse population dynamics for evaluating risk of HPS. 2. Using capture-mark-recapture statistical methods on a 15-year data set from Montana, we estimated deer mouse survival, maturation and recruitment rates and tested the relative importance of seasonality, population density and local climate in explaining temporal variation in deer mouse demography. 3. From these estimates, we designed a population model to simulate deer mouse population dynamics given climatic variables and compared the model to observed patterns. 4. Month, precipitation 5 months previously, temperature 5 months previously and to a lesser extent precipitation and temperature in the current month, were important in determining deer mouse survival. Month, the sum of precipitation over the last 4 months, and the sum of the temperature over the last 4 months were important in determining recruitment rates. Survival was more important in determining the growth rate of the population than recruitment. 5. While climatic drivers appear to have a complex influence on dynamics, our forecasts were good. Our quantitative model may allow public health officials to better predict increased human risk from basic climatic data.

Bats are natural reservoirs of several important emerging viruses. Cross‐species transmission appears to be quite common among bats, which may contribute to their unique reservoir potential. Therefore, understanding the importance of bats as reservoirs requires examining them in a community context rather than concentrating on individual species. Here, we use a network approach to identify ecological and biological correlates of cross‐species virus transmission in bats and rodents, another important host group. We show that given our current knowledge the bat viral sharing network is more connected than the rodent network, suggesting viruses may pass more easily between bat species. We identify host traits associated with important reservoir species: gregarious bats are more likely to share more viruses and bats which migrate regionally are important for spreading viruses through the network. We identify multiple communities of viral sharing within bats and rodents and highlight potential species traits that can help guide studies of novel pathogen emergence.

Background Increased risks for hemorrhagic fever with renal syndrome (HFRS) caused by Hantaan virus have been observed since 2005, in Xi’an, China. Despite increased vigilance and preparedness, HFRS outbreaks in 2010, 2011, and 2012 were larger than ever, with a total of 3,938 confirmed HFRS cases and 88 deaths in 2010 and 2011. Methods and Findings Data on HFRS cases and weather were collected monthly from 2005 to 2012, along with active rodent monitoring. Wavelet analyses were performed to assess the temporal relationship between HFRS incidence, rodent density and climatic factors over the study period. Results showed that HFRS cases correlated to rodent density, rainfall, and temperature with 2, 3 and 4-month lags, respectively. Using a Bayesian time-series Poisson adjusted model, we fitted the HFRS outbreaks among humans for risk assessment in Xi’an. The best models included seasonality, autocorrelation, rodent density 2 months previously, and rainfall 2 to 3 months previously. Our models well reflected the epidemic characteristics by one step ahead prediction, out-of-sample. Conclusions In addition to a strong seasonal pattern, HFRS incidence was correlated with rodent density and rainfall, indicating that they potentially drive the HFRS outbreaks. Future work should aim to determine the mechanism underlying the seasonal pattern and autocorrelation. However, this model can be useful in risk management to provide early warning of potential outbreaks of this disease., Author Summary Hemorrhagic fever with renal syndrome (HFRS, caused by hantavirus) is a zoonotic infectious disease reservoired in rodent populations worldwide, but with 90% of the total cases occurring in China. Xi’an is one of the most endemic areas in China, with a total of 7,748 confirmed HFRS cases from 2005 to 2012. HFRS came to the attention of the public when two larger outbreaks occurred in Xi’an in 2010 and 2011, with 1,366 and 1,067 cases being reported, respectively. By using 8 years of surveillance data (2005–2012) on HFRS dynamics, including data on the main rodent host reservoir, human cases, and weather conditions, we show how the epidemic dynamics of HFRS were associated with seasonality, rodent abundance, rainfall, and temperature. We find that the two larger HFRS outbreaks coincided with the abrupt increase of rodent abundance and/or rainfall. We present a statistical model revealing strong effects of seasonality and autocorrelation and additional effects of rodent density and rainfall on HFRS incidence that gives robust prediction; this approach could be a very practical tool in Xi’an.

Summary 1 To examine the hypothesis that stimulation of immune function plays a role in periodic arousal from hibernation, bacterial growth during hibernation was estimated using a simple mathematical model of the general dynamics of bacterial abundance at body temperatures experienced during hibernation. 2 In the model, periodic arousals were important for animals infected with Salmonella at body temperatures above 7 °C, but not below. In contrast, periodic arousals appeared to be important at all temperatures examined when infected with several species of coliform bacteria and Pseudomonas, species that grow well at low temperatures. 3 The modelled outputs were compared with torpor patterns seen in captive European Ground Squirrels, Spermophilus citellus, under natural light and temperature conditions. We used maximum likelihood to estimate model parameters and show that the six bacterial species examined are consistent with the immune stimulation hypothesis. 4 Our analyses suggest that bacterial infection could be a selective force on torpor behaviour and warrants further experimental investigation.

SIV-infected Indian rhesus macaques (Macaca mulatta) are an important animal model for humans infected with HIV. Understanding macaque (M. mulatta class I (Mamu)) MHC class I-peptide binding facilitates the comparison of SIV- and HIV-specific cellular immune responses. In this study, we characterized the endogenous peptide-binding properties of three Mamu-A (A*02, A*08, A*11) and three Mamu-B (B*01, B*03, B*12) class I molecules. Motif comparisons revealed that five of the six macaque class I molecules (A*02, A*08, A*11, B*01, and B*03) have peptide-binding motifs similar to those of human class I molecules. Of the 65 macaque endogenous peptide ligands that we sequenced by tandem mass spectroscopy, 5 were previously eluted from HLA class I molecules. Nonamers predominated among the individual ligands, and both the motifs and the individual ligands indicated P2, P9, and various ancillary anchors. Interestingly, peptide binding of the Mamu-A and Mamu-B molecules exhibited cross-species peptide-presentation overlap primarily with HLA-B molecules. Indeed, all of the macaque class I molecules appeared HLA-B-like in peptide presentation. Remarkably, the overlap in macaque- and HLA-peptide presentation occurred despite divergent class I peptide-binding grooves. Macaque and human class I differing by up to 42 aa (13–23%) within the α-1 and α-2 domains, including substantial divergence within specificity pockets A-F, bound the same endogenous peptide. Therefore, endogenous peptide characterization indicates that macaque class I molecules may be the functional equivalents of HLA-B molecules.

How pathogens affect their hosts is a key question in infectious disease ecology, and it can have important influences on the spread and persistence of the pathogen. Sin Nombre virus (SNV) is the etiological agent of hantavirus pulmonary syndrome (HPS) in humans. A better understanding of SNV in its reservoir host, the deer mouse, could lead to improved predictions of the circulation and persistence of the virus in the mouse reservoir, and could help identify the factors that lead to increased human risk of HPS. Using mark–recapture statistical modeling on longitudinal data collected over 15 years, we found a 13.4% decrease in the survival of male deer mice with antibodies to SNV compared to uninfected mice (both male and female). There was also an additive effect of breeding condition, with a 21.3% decrease in survival for infected mice in breeding condition compared to uninfected, non-breeding mice. The data identified that transmission was consistent with density-dependent transmission, implying that there may be a critical host density below which SNV cannot persist. The notion of a critical host density coupled with the previously overlooked disease-induced mortality reported here contribute to a better understanding of why SNV often goes extinct locally and only seems to persist at the metapopulation scale, and why human spillover is episodic and hard to predict.

Background Vector control remains the primary defense against dengue fever. Its success relies on the assumption that vector density is related to disease transmission. Two operational issues include the amount by which mosquito density should be reduced to minimize transmission and the spatio-temporal allotment of resources needed to reduce mosquito density in a cost-effective manner. Recently, a novel technology, MI-Dengue, was implemented city-wide in several Brazilian cities to provide real-time mosquito surveillance data for spatial prioritization of vector control resources. We sought to understand the role of city-wide mosquito density data in predicting disease incidence in order to provide guidance for prioritization of vector control work. Methods We used hierarchical Bayesian regression modeling to examine the role of city-wide vector surveillance data in predicting human cases of dengue fever in space and time. We used four years of weekly surveillance data from Vitoria city, Brazil, to identify the best model structure. We tested effects of vector density, lagged case data and spatial connectivity. We investigated the generality of the best model using an additional year of data from Vitoria and two years of data from other Brazilian cities: Governador Valadares and Sete Lagoas. Results We found that city-wide, neighborhood-level averages of household vector density were a poor predictor of dengue-fever cases in the absence of accounting for interactions with human cases. Effects of city-wide spatial patterns were stronger than within-neighborhood or nearest-neighborhood effects. Readily available proxies of spatial relationships between human cases, such as economic status, population density or between-neighborhood roadway distance, did not explain spatial patterns in cases better than unweighted global effects. Conclusions For spatial prioritization of vector controls, city-wide spatial effects should be given more weight than within-neighborhood or nearest-neighborhood connections, in order to minimize city-wide cases of dengue fever. More research is needed to determine which data could best inform city-wide connectivity. Once these data become available, MI-dengue may be even more effective if vector control is spatially prioritized by considering city-wide connectivity between cases together with information on the location of mosquito density and infected mosquitos. Electronic supplementary material The online version of this article (doi:10.1186/s13071-015-0659-y) contains supplementary material, which is available to authorized users.

MHC class I molecules present host- and pathogen-derived peptides for immune surveillance. Much attention is given to the search for viral and tumor nonself peptide epitopes, yet the question remains, “What is self?” Analyses of Edman motifs and of small sets of individual peptides suggest that the class I self repertoire consists of thousands of different peptides. However, there exists no systematic characterization of this self-peptide backdrop, causing the definition of class I-presented self to remain largely hypothetical. To better understand the breadth and nature of self proteins sampled by class I HLA, we sequenced >200 endogenously loaded HLA-B*1801 peptides from a human B cell line. Peptide-source proteins, ranging from actin-related protein 6 to zinc finger protein 147, possessed an assortment of biological and molecular functions. Major categories included binding proteins, catalytic proteins, and proteins involved in cell metabolism, growth, and maintenance. Genetically, peptides encoded by all chromosomes were presented. Statistical comparison of proteins presented by class I vs the human proteome provides empiric evidence that the range of proteins sampled by class I is relatively unbiased, with the exception of RNA-binding proteins that are over-represented in the class I peptide repertoire. These data show that, in this cell line, class I-presented self peptides represent a comprehensive and balanced summary of the proteomic content of the cell. Importantly, virus- and tumor-induced changes in virtually any cellular compartment or to any chromosome can be expected to be presented by class I molecules for immune recognition.

Class I MHC molecules bind intracellular peptides for presentation to cytotoxic T lymphocytes. Identification of peptides presented by class I molecules during infection is therefore a priority for detecting and targeting intracellular pathogens. To understand which host-encoded peptides distinguish HIV-infected cells, we have developed a mass spectrometric approach to characterize HLA-B*0702 peptides unique to or up-regulated on infected T cells. In this study, we identify 15 host proteins that are differentially presented on infected human T cells. Peptides with increased expression on HIV-infected cells were derived from multiple categories of cellular proteins including RNA binding proteins and cell cycle regulatory proteins. Therefore, comprehensive analysis of the B*0702 peptide repertoire demonstrates that marked differences in host protein presentation occur after HIV infection.

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.