A model for leveraging animal movement to understand spatio-temporal disease dynamics.

The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model 'Movement-driven modelling of spatio-temporal infection risk' (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.

"One Health" or Three? Publication Silos Among the One Health Disciplines.

The One Health initiative is a global effort fostering interdisciplinary collaborations to address challenges in human, animal, and environmental health. While One Health has received considerable press, its benefits remain unclear because its effects have not been quantitatively described. We systematically surveyed the published literature and used social network analysis to measure interdisciplinarity in One Health studies constructing dynamic pathogen transmission models. The number of publications fulfilling our search criteria increased by 14.6% per year, which is faster than growth rates for life sciences as a whole and for most biology subdisciplines. Surveyed publications clustered into three communities: one used by ecologists, one used by veterinarians, and a third diverse-authorship community used by population biologists, mathematicians, epidemiologists, and experts in human health. Overlap between these communities increased through time in terms of author number, diversity of co-author affiliations, and diversity of citations. However, communities continue to differ in the systems studied, questions asked, and methods employed. While the infectious disease research community has made significant progress toward integrating its participating disciplines, some segregation--especially along the veterinary/ecological research interface--remains.

Heterologous Vaccination and Checkpoint Blockade Synergize To Induce Antileukemia Immunity.

Checkpoint blockade-based immunotherapies are effective in cancers with high numbers of nonsynonymous mutations. In contrast, current paradigms suggest that such approaches will be ineffective in cancers with few nonsynonymous mutations. To examine this issue, we made use of a murine model of BCR-ABL(+) B-lineage acute lymphoblastic leukemia. Using a principal component analysis, we found that robust MHC class II expression, coupled with appropriate costimulation, correlated with lower leukemic burden. We next assessed whether checkpoint blockade or therapeutic vaccination could improve survival in mice with pre-established leukemia. Consistent with the low mutation load in our leukemia model, we found that checkpoint blockade alone had only modest effects on survival. In contrast, robust heterologous vaccination with a peptide derived from the BCR-ABL fusion (BAp), a key driver mutation, generated a small population of mice that survived long-term. Checkpoint blockade strongly synergized with heterologous vaccination to enhance overall survival in mice with leukemia. Enhanced survival did not correlate with numbers of BAp:I-A(b)-specific T cells, but rather with increased expression of IL-10, IL-17, and granzyme B and decreased expression of programmed death 1 on these cells. Our findings demonstrate that vaccination to key driver mutations cooperates with checkpoint blockade and allows for immune control of cancers with low nonsynonymous mutation loads.

Age-specific infectious period shapes dynamics of pneumonia in bighorn sheep.

Superspreading, the phenomenon where a small proportion of individuals contribute disproportionately to new infections, has profound effects on disease dynamics. Superspreading can arise through variation in contacts, infectiousness or infectious periods. The latter has received little attention, yet it drives the dynamics of many diseases of critical public health, livestock health and conservation concern. Here, we present rare evidence of variation in infectious periods underlying a superspreading phenomenon in a free-ranging wildlife system. We detected persistent infections of Mycoplasma ovipneumoniae, the primary causative agent of pneumonia in bighorn sheep (Ovis canadensis), in a small number of older individuals that were homozygous at an immunologically relevant genetic locus. Interactions among age-structure, genetic composition and infectious periods may drive feedbacks in disease dynamics that determine the magnitude of population response to infection. Accordingly, variation in initial conditions may explain divergent population responses to infection that range from recovery to catastrophic decline and extirpation.

Contact and contagion: Probability of transmission given contact varies with demographic state in bighorn sheep.

Understanding both contact and probability of transmission given contact are key to managing wildlife disease. However, wildlife disease research tends to focus on contact heterogeneity, in part because the probability of transmission given contact is notoriously difficult to measure. Here, we present a first step towards empirically investigating the probability of transmission given contact in free-ranging wildlife. We used measured contact networks to test whether bighorn sheep demographic states vary systematically in infectiousness or susceptibility to Mycoplasma ovipneumoniae, an agent responsible for bighorn sheep pneumonia. We built covariates using contact network metrics, demographic information and infection status, and used logistic regression to relate those covariates to lamb survival. The covariate set contained degree, a classic network metric describing node centrality, but also included covariates breaking the network metrics into subsets that differentiated between contacts with yearlings, ewes with lambs, and ewes without lambs, and animals with and without active infections. Yearlings, ewes with lambs, and ewes without lambs showed similar group membership patterns, but direct interactions involving touch occurred at a rate two orders of magnitude higher between lambs and reproductive ewes than between any classes of adults or yearlings, and one order of magnitude higher than direct interactions between multiple lambs. Although yearlings and non-reproductive bighorn ewes regularly carried M. ovipneumoniae, our models suggest that a contact with an infected reproductive ewe had approximately five times the odds of producing a lamb mortality event of an identical contact with an infected dry ewe or yearling. Consequently, management actions targeting infected animals might lead to unnecessary removal of young animals that carry pathogens but rarely transmit. This analysis demonstrates a simple logistic regression approach for testing a priori hypotheses about variation in the odds of transmission given contact for free-ranging hosts, and may be broadly applicable for investigations in wildlife disease ecology.

Costs and benefits of group living with disease: a case study of pneumonia in bighorn lambs (Ovis canadensis).

Group living facilitates pathogen transmission among social hosts, yet temporally stable host social organizations can actually limit transmission of some pathogens. When there are few between-subpopulation contacts for the duration of a disease event, transmission becomes localized to subpopulations. The number of per capita infectious contacts approaches the subpopulation size as pathogen infectiousness increases. Here, we illustrate that this is the case during epidemics of highly infectious pneumonia in bighorn lambs (Ovis canadensis). We classified individually marked bighorn ewes into disjoint seasonal subpopulations, and decomposed the variance in lamb survival to weaning into components associated with individual ewes, subpopulations, populations and years. During epidemics, lamb survival varied substantially more between ewe-subpopulations than across populations or years, suggesting localized pathogen transmission. This pattern of lamb survival was not observed during years when disease was absent. Additionally, group sizes in ewe-subpopulations were independent of population size, but the number of ewe-subpopulations increased with population size. Consequently, although one might reasonably assume that force of infection for this highly communicable disease scales with population size, in fact, host social behaviour modulates transmission such that disease is frequency-dependent within populations, and some groups remain protected during epidemic events.

Spatio-temporal dynamics of pneumonia in bighorn sheep.

1. Bighorn sheep mortality related to pneumonia is a primary factor limiting population recovery across western North America, but management has been constrained by an incomplete understanding of the disease. We analysed patterns of pneumonia-caused mortality over 14 years in 16 interconnected bighorn sheep populations to gain insights into underlying disease processes. 2. We observed four age-structured classes of annual pneumonia mortality patterns: all-age, lamb-only, secondary all-age and adult-only. Although there was considerable variability within classes, overall they differed in persistence within and impact on populations. Years with pneumonia-induced mortality occurring simultaneously across age classes (i.e. all-age) appeared to be a consequence of pathogen invasion into a naïve population and resulted in immediate population declines. Subsequently, low recruitment due to frequent high mortality outbreaks in lambs, probably due to association with chronically infected ewes, posed a significant obstacle to population recovery. Secondary all-age events occurred in previously exposed populations when outbreaks in lambs were followed by lower rates of pneumonia-induced mortality in adults. Infrequent pneumonia events restricted to adults were usually of short duration with low mortality. 3. Acute pneumonia-induced mortality in adults was concentrated in fall and early winter around the breeding season when rams are more mobile and the sexes commingle. In contrast, mortality restricted to lambs peaked in summer when ewes and lambs were concentrated in nursery groups. 4. We detected weak synchrony in adult pneumonia between adjacent populations, but found no evidence for landscape-scale extrinsic variables as drivers of disease. 5. We demonstrate that there was a >60% probability of a disease event each year following pneumonia invasion into bighorn sheep populations. Healthy years also occurred periodically, and understanding the factors driving these apparent fade-out events may be the key to managing this disease. Our data and modelling indicate that pneumonia can have greater impacts on bighorn sheep populations than previously reported, and we present hypotheses about processes involved for testing in future investigations and management.

Deriving spatially explicit direct and indirect interaction networks from animal movement data.

Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems ["GPS"]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous-time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions-individuals occurring at the same location, but at different times-while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease-relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30-min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM-Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine-scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer-resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.

ABORTION AND NEONATAL MORTALITY DUE TO TOXOPLASMA GONDII IN BIGHORN SHEEP (OVIS CANADENSIS).

Low lamb recruitment can be an obstacle to bighorn sheep (Ovis canadensis) conservation and restoration. Causes of abortion and neonate loss in bighorn sheep, which may affect recruitment, are poorly understood. Toxoplasma gondii is a major cause of abortion and stillbirth in domestic small ruminants worldwide, but no reports exist documenting abortion or neonatal death in bighorn sheep attributable to toxoplasmosis. Between March 2019 and May 2021, eight fetal and neonatal bighorn lamb cadavers from four western US states (Idaho, Montana, Nebraska, and Washington) were submitted to the Washington Animal Disease Diagnostic Laboratory for postmortem examination, histologic examination, and ancillary testing to determine the cause of abortion or neonatal death. Necrotizing encephalitis characteristic of toxoplasmosis was identified histologically in six of eight cases, and T. gondii infection was confirmed by PCR in five cases with characteristic lesions. Other lesions attributable to toxoplasmosis were pneumonia (3/5 cases) and myocarditis (2/5 cases). Protozoal cysts were identified histologically within brain, lung, heart, skeletal muscle, adipose tissue, or a combination of samples in all five sheep with PCR-confirmed T. gondii infections. Seroprevalence of T. gondii ranged from 40-81% of adult females sampled in the Washington population in October and November 2018-2021, confirming high rates of exposure before detection of Toxoplasma abortions in this study. Of 1,149 bighorn sheep postmortem samples submitted to Washington Animal Disease Diagnostic Laboratory between January 2000 and May 2021, 21 of which were from fetuses or neonates, a single case of chronic toxoplasmosis was diagnosed in one adult ewe. Recent identification of Toxoplasma abortions in bighorn sheep suggests that toxoplasmosis is an underappreciated cause of reproductive loss. Abortions and neonatal mortalities should be investigated through postmortem and histologic examination, particularly in herds that are chronically small, demographically stagnant, or exhibit reproductive rates lower than expected.

Bighorn sheep show similar in-host responses to the same pathogen strain in two contrasting environments.

Ecological context-the biotic and abiotic environment, along with its influence on population mixing dynamics and individual susceptibility-is thought to have major bearing on epidemic outcomes. However, direct comparisons of wildlife disease events in contrasting ecological contexts are often confounded by concurrent differences in host genetics, exposure histories, or pathogen strains. Here, we compare disease dynamics of a Mycoplasma ovipneumoniae spillover event that affected bighorn sheep populations in two contrasting ecological contexts. One event occurred on the herd's home range near the Rio Grande Gorge in New Mexico, while the other occurred in a captive facility at Hardware Ranch in Utah. While data collection regimens varied, general patterns of antibody signal strength and symptom emergence were conserved between the two sites. Symptoms appeared in the captive setting an average of 12.9 days postexposure, average time to seroconversion was 24.9 days, and clinical signs peaked at approximately 36 days postinfection. These patterns were consistent with serological testing and subsequent declines in symptom intensity in the free-ranging herd. At the captive site, older animals exhibited more severe declines in body condition and loin thickness, higher symptom burdens, and slower antibody response to the pathogen than younger animals. Younger animals were more likely than older animals to clear infection by the time of sampling at both sites. The patterns presented here suggest that environment may not be a major determinant of epidemiological outcomes in the bighorn sheep-M. ovipneumoniae system, elevating the possibility that host- or pathogen-factors may be responsible for observed variation.

Natural history of a bighorn sheep pneumonia epizootic: Source of infection, course of disease, and pathogen clearance.

A respiratory disease epizootic at the National Bison Range (NBR) in Montana in 2016-2017 caused an 85% decline in the bighorn sheep population, documented by observations of its unmarked but individually identifiable members, the subjects of an ongoing long-term study. The index case was likely one of a small group of young bighorn sheep on a short-term exploratory foray in early summer of 2016. Disease subsequently spread through the population, with peak mortality in September and October and continuing signs of respiratory disease and sporadic mortality of all age classes through early July 2017. Body condition scores and clinical signs suggested that the disease affected ewe groups before rams, although by the end of the epizootic, ram mortality (90% of 71) exceeded ewe mortality (79% of 84). Microbiological sampling 10 years to 3 months prior to the epizootic had documented no evidence of infection or exposure to Mycoplasma ovipneumoniae at NBR, but during the epizootic, a single genetic strain of M. ovipneumoniae was detected in affected animals. Retrospective screening of domestic sheep flocks near the NBR identified the same genetic strain in one flock, presumptively the source of the epizootic infection. Evidence of fatal lamb pneumonia was observed during the first two lambing seasons following the epizootic but was absent during the third season following the death of the last identified M. ovipneumoniae carrier ewe. Monitoring of life-history traits prior to the epizootic provided no evidence that environmentally and/or demographically induced nutritional or other stress contributed to the epizootic. Furthermore, the epizootic occurred despite proactive management actions undertaken to reduce risk of disease and increase resilience in this population. This closely observed bighorn sheep epizootic uniquely illustrates the natural history of the disease including the (presumptive) source of spillover, course, severity, and eventual pathogen clearance.

Cross-species pathogen spillover across ecosystem boundaries: mechanisms and theory.

Pathogen spillover between different host species is the trigger for many infectious disease outbreaks and emergence events, and ecosystem boundary areas have been suggested as spatial hotspots of spillover. This hypothesis is largely based on suspected higher rates of zoonotic disease spillover and emergence in fragmented landscapes and other areas where humans live in close vicinity to wildlife. For example, Ebola virus outbreaks have been linked to contacts between humans and infected wildlife at the rural-forest border, and spillover of yellow fever via mosquito vectors happens at the interface between forest and human settlements. Because spillover involves complex interactions between multiple species and is difficult to observe directly, empirical studies are scarce, particularly those that quantify underlying mechanisms. In this review, we identify and explore potential ecological mechanisms affecting spillover of pathogens (and parasites in general) at ecosystem boundaries. We borrow the concept of 'permeability' from animal movement ecology as a measure of the likelihood that hosts and parasites are present in an ecosystem boundary region. We then discuss how different mechanisms operating at the levels of organisms and ecosystems might affect permeability and spillover. This review is a step towards developing a general theory of cross-species parasite spillover across ecosystem boundaries with the eventual aim of improving predictions of spillover risk in heterogeneous landscapes. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Sampling to elucidate the dynamics of infections in reservoir hosts.

The risk of zoonotic spillover from reservoir hosts, such as wildlife or domestic livestock, to people is shaped by the spatial and temporal distribution of infection in reservoir populations. Quantifying these distributions is a key challenge in epidemiology and disease ecology that requires researchers to make trade-offs between the extent and intensity of spatial versus temporal sampling. We discuss sampling methods that strengthen the reliability and validity of inferences about the dynamics of zoonotic pathogens in wildlife hosts. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Percolation models of pathogen spillover.

Predicting pathogen spillover requires counting spillover events and aligning such counts with process-related covariates for each spillover event. How can we connect our analysis of spillover counts to simple, mechanistic models of pathogens jumping from reservoir hosts to recipient hosts? We illustrate how the pathways to pathogen spillover can be represented as a directed graph connecting reservoir hosts and recipient hosts and the number of spillover events modelled as a percolation of infectious units along that graph. Percolation models of pathogen spillover formalize popular intuition and management concepts for pathogen spillover, such as the inextricably multilevel nature of cross-species transmission, the impact of covariance between processes such as pathogen shedding and human susceptibility on spillover risk, and the assumptions under which the effect of a management intervention targeting one process, such as persistence of vectors, will translate to an equal effect on the overall spillover risk. Percolation models also link statistical analysis of spillover event datasets with a mechanistic model of spillover. Linear models, one might construct for process-specific parameters, such as the log-rate of shedding from one of several alternative reservoirs, yield a nonlinear model of the log-rate of spillover. The resulting nonlinearity is approximately piecewise linear with major impacts on statistical inferences of the importance of process-specific covariates such as vector density. We recommend that statistical analysis of spillover datasets use piecewise linear models, such as generalized additive models, regression clustering or ensembles of linear models, to capture the piecewise linearity expected from percolation models. We discuss the implications of our findings for predictions of spillover risk beyond the range of observed covariates, a major challenge of forecasting spillover risk in the Anthropocene. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface.

Managing pathogen spillover at the wildlife-livestock interface is a key step towards improving global animal health, food security and wildlife conservation. However, predicting the effectiveness of management actions across host-pathogen systems with different life histories is an on-going challenge since data on intervention effectiveness are expensive to collect and results are system-specific. We developed a simulation model to explore how the efficacies of different management strategies vary according to host movement patterns and epidemic growth rates. The model suggested that fast-growing, fast-moving epidemics like avian influenza were best-managed with actions like biosecurity or containment, which limited and localized overall spillover risk. For fast-growing, slower-moving diseases like foot-and-mouth disease, depopulation or prophylactic vaccination were competitive management options. Many actions performed competitively when epidemics grew slowly and host movements were limited, and how management efficacy related to epidemic growth rate or host movement propensity depended on what objective was used to evaluate management performance. This framework offers one means of classifying and prioritizing responses to novel pathogen spillover threats, and evaluating current management actions for pathogens emerging at the wildlife-livestock interface. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Authors and editors assort on gender and geography in high-rank ecological publications.

Peer-reviewed publication volume and caliber are widely-recognized proxies for academic merit, and a strong publication record is essential for academic success and advancement. However, recent work suggests that publication productivity for particular author groups may also be determined in part by implicit biases lurking in the publication pipeline. Here, we explore patterns of gender, geography, and institutional rank among authors, editorial board members, and handling editors in high-impact ecological publications during 2015 and 2016. A higher proportion of lead authors had female first names (33.9%) than editorial board members (28.9%), and the proportion of female first names among handling editors was even lower (21.1%). Female editors disproportionately edited publications with female lead authors (40.3% of publications with female lead authors were handled by female editors, though female editors handled only 34.4% of all studied publications). Additionally, ecological authors and editors were overwhelmingly from countries in the G8, and high-ranking academic institutions accounted for a large portion of both the published work, and its editorship. Editors and lead authors with female names were typically affiliated with higher-ranking institutions than their male peers. This description of author and editor features provides a baseline for benchmarking future trends in the ecological publishing culture.