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At least 289 records · Page 16Linked to original sources
USGS2026

Validation and application of a standardized quantitative PCR assay for the assessment of antimicrobial resistance genes in surface water

Antimicrobial resistance can be an indicator of anthropogenic contamination in surface waters and is a potential public health threat. Methodological standardization for characterization of antimicrobial resistance in the environment is lacking. Quantitative PCR (qPCR) is used for rapid assessment of antibiotic resistance genes (ARGs) from environmental sources, including surface water. Here we describe the validation and application of a qPCR assay for 47 bacterial gene targets intended for surface water samples. The qPCR assay displayed excellent sensitivity (97.66%) and specificity (98.71%) for detecting ARGs when compared to whole genome sequencing of bacterial isolates. The qPCR assay was able to detect up to 6/8 (75.0%) of ARGs spiked into sterile water at varying concentrations and four sample ultrafiltration volumes. Nineteen different ARGs were detected across six samples sites at three national parks in Alaska using ultrafiltered surface water samples. The number of unique ARGs detected was higher at sites within parks with greater visitation. The relative abundance of ARGs/16S from Exit Creek in Kenai Fjords National Park, downstream from a visitor center was greater than all other sampled sites. We have demonstrated a robust qPCR assay for monitoring ARGs in surface waters, including those that are minimally human impacted.

Alaska↗
USGS2025

Tapwater exposures, residential risk, and mitigation in a PFAS-impacted-groundwater community

Tapwater (TW) safety and sustainability are priorities in the United States. Per/polyfluoroalkyl substance(s) (PFAS) contamination is a growing public-health concern due to prolific use, widespread TW exposures, and mounting human-health concerns. Historically-rural, actively-urbanizing communities that rely on surficial-aquifer private wells incur elevated risks of unrecognized TW exposures, including PFAS, due to limited private-well monitoring and contaminant-source proliferation in urbanizing landscapes. Here, a broad-analytical-scope TW-assessment was conducted in a hydrologically-vulnerable, Mississippi River alluvial-island community, where PFAS contamination of the shallow-alluvial drinking-water aquifer has been documented, but more comprehensive contaminant characterization to inform decision-making is currently lacking. In 2021, we analyzed 510 organics, 34 inorganics, and 3 microbial groups in 11 residential and community locations to assess (1) TW risks beyond recognized PFAS issues, (2) day-to-day and year-to-year risk variability, and (3) suitability of the underlying sandstone aquifer as an alternative source to mitigate TW-PFAS exposures. Seventy-six organics and 25 inorganics were detected. Potential human-health risks of detected TW exposures were explored based on cumulative benchmark-based toxicity quotients ( ∑ TQ ). Elevated risks ( ∑ TQ ≥ 1) from organic and inorganic contaminants were observed in all alluvial-aquifer-sourced synoptic samples but not in sandstone-aquifer-sourced samples. Repeated sampling at 3 sites over 52–55 h indicated limited variability in risk over the short-term. Comparable PFAS-specific ∑ TQ for spatial-synoptic, short-term (3 days) temporal, and long-term (3 years quarterly) temporal samples indicated that synoptic results provided useful insight into the risks of TW-PFAS exposures at French Island over the long-term. No PFAS detections in sandstone-aquifer-sourced samples over a 3 year period indicated no PFAS-associated risk and supported the sandstone aquifer as an alternative drinking-water source to mitigate community TW-PFAS exposures. This study illustrated the importance of expanded contaminant monitoring of private-well TW, beyond known concerns (in this case, PFAS), to reduce the risks of a range of unrecognized contaminant exposures.

Wisconsin↗
USGS2007

Concentration, UV-spectroscopic characteristics and fractionation of DOC in stormflow from an urban stream, Southern California, USA

The composition of dissolved organic carbon (DOC) in stormflow from urban areas has been greatly altered, both directly and indirectly, by human activities and there is concern that there may be public health issues associated with DOC, which has unknown composition from different sources within urban watersheds. This study evaluated changes in the concentration and composition of DOC in stormflow in the Santa Ana River and its tributaries between 1995 and 2004 using a simplified approach based on the differences in the optical properties of DOC and using operationally defined differences in molecular weight and solubility. The data show changes in the composition of DOC in stormflow during the rainy season and differences associated with runoff from different parts of the basin, including extensive upland areas burned prior to the 2004 rainy season. Samples were collected from the Santa Ana River, which drains ~6950 km 2 of the densely populated coastal area of southern California, during 23 stormflows between 1995 and 2004. Dissolved organic carbon (DOC) concentrations during the first stormflows of the ‘winter’ (November to March) rainy season increased rapidly with streamflow and were positively correlated with increased faecal indicator bacteria concentrations. DOC concentrations were not correlated with streamflow or with other constituents during stormflows later in the rainy season and DOC had increasing UV absorbance per unit carbon as the rainy season progressed. DOC concentrations in stormflow from an urban drain tributary to the river also increased during stormflow and were greater than concentrations in the river. DOC concentrations in stormflow from a tributary stream, draining urban and agricultural land that contained more than 320 000 animals, mostly dairy cows, were higher than concentrations in stormflow from the river and from the urban drain. Fires that burned large areas of the basin before the 2004 rainy season did not increase DOC concentrations in the river during stormflow after the fires – possibly because the large watershed of the river damped the effect of the fires. However, the fires increased the hydrophobic neutral organic carbon fraction of DOC in stormflow from the urban drain and the tributary stream.

California↗
USGS2025

Landscape changes elevate the risk of avian influenza virus diversification and emergence in the East Asian–Australasian Flyway

Highly pathogenic avian influenza viruses (HPAIV) persistently threaten wild waterfowl, domestic poultry, and public health. The East Asian–Australasian Flyway plays a crucial role in HPAIV dynamics due to its large populations of migratory waterfowl and poultry. Over recent decades, this flyway has undergone substantial landscape changes, including both losses and gains of waterfowl habitats. These changes can affect waterfowl distributions, increase contact with poultry, and consequently alter ecological conditions that favor avian influenza virus (AIV) evolution. However, limited research has assessed these likely impacts. Here, we integrated empirical data and an individual-based model to simulate AIV transmission in migratory waterfowl and domestic poultry, including wild-to-poultry spillover and reassortment dynamics in poultry, across landscapes representing the years 2000 and 2015. We used the reassortment incidence as a proxy for ecological and transmission conditions that support viral diversification and the emergence of novel subtypes. Our simulations show that landscape change reshaped the waterfowl distribution, facilitated bird aggregation at improved habitats, increased coinfection, and raised reassortment rate by 1,593%, indicating a substantially higher potential for viral diversification and emergence. Model-generated risk maps show expanded and increased reassortment risk in southeastern China, the Yellow River Basin, and northeastern China. These findings suggest the importance of landscape change as a driver of potential AIV diversification and subtype emergence. This underscores the need for interdisciplinary approaches that integrate landscape dynamics, host movement, and viral evolution to better assess and mitigate future risk.

East Asian–Australasian Flyway↗
USGS2013

Comparing rapid and culture indicator bacteria methods at inland lake beaches

A rapid method, quantitative polymerase chain reaction (qPCR), for quantifying indicator bacteria in recreational waters is desirable for public health protection. We report that replacing current Escherichia coli standards with new US Environmental Protection Agency beach action values (BAVs) for enterococci by culture or qPCR may result in more advisories being posted at inland recreational lakes. In this study, concentrations of E. coli and enterococci by culture methods were compared to concentrations of Enterococcus spp. by qPCR at 3 inland lake beaches in Ohio. The E. coli and enterococci culture results were significantly related at all beaches; however, the relations between culture results and Enterococcus spp. qPCR results were not always significant and differed among beaches. All the qPCR results exceeded the new BAV for Enterococcus spp. by qPCR, whereas only 23.7% of culture results for E. coli and 79% of culture results for enterococci exceeded the current standard for E. coli or BAV for enterococci.

Lake and Reservoir Management↗
USGS2017

Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset

Cyanobacterial blooms degrade water quality in drinking water supply reservoirs by producing toxic and taste-and-odor causing secondary metabolites, which ultimately cause public health concerns and lead to increased treatment costs for water utilities. There have been numerous attempts to create models that predict cyanobacteria and their secondary metabolites, most using linear models; however, linear models are limited by assumptions about the data and have had limited success as predictive tools. Thus, lake and reservoir managers need improved modeling techniques that can accurately predict large bloom events that have the highest impact on recreational activities and drinking-water treatment processes. In this study, we compared 12 unique linear and nonlinear regression modeling techniques to predict cyanobacterial abundance and the cyanobacterial secondary metabolites microcystin and geosmin using 14 years of physiochemical water quality data collected from Cheney Reservoir, Kansas. Support vector machine (SVM), random forest (RF), boosted tree (BT), and Cubist modeling techniques were the most predictive of the compared modeling approaches. SVM, RF, and BT modeling techniques were able to successfully predict cyanobacterial abundance, microcystin, and geosmin concentrations <60,000 cells/mL, 2.5 µg/L, and 20 ng/L, respectively. Only Cubist modeling predicted maxima concentrations of cyanobacteria and geosmin; no modeling technique was able to predict maxima microcystin concentrations. Because maxima concentrations are a primary concern for lake and reservoir managers, Cubist modeling may help predict the largest and most noxious concentrations of cyanobacteria and their secondary metabolites.

Kansas↗
USGS2009

Use of predictive models and rapid methods to nowcast bacteria levels at coastal beaches

The need for rapid assessments of recreational water quality to better protect public health is well accepted throughout the research and regulatory communities. Rapid analytical methods, such as quantitative polymerase chain reaction (qPCR) and immunomagnetic separation/adenosine triphosphate (ATP) analysis, are being tested but are not yet ready for widespread use. Another solution is the use of predictive models, wherein variable(s) that are easily and quickly measured are surrogates for concentrations of fecal-indicator bacteria. Rainfall-based alerts, the simplest type of model, have been used by several communities for a number of years. Deterministic models use mathematical representations of the processes that affect bacteria concentrations; this type of model is being used for beach-closure decisions at one location in the USA. Multivariable statistical models are being developed and tested in many areas of the USA; however, they are only used in three areas of the Great Lakes to aid in notifications of beach advisories or closings. These “operational” statistical models can result in more accurate assessments of recreational water quality than use of the previous day's Escherichia coli (E. coli) concentration as determined by traditional culture methods. The Ohio Nowcast, at Huntington Beach, Bay Village, Ohio, is described in this paper as an example of an operational statistical model. Because predictive modeling is a dynamic process, water-resource managers continue to collect additional data to improve the predictive ability of the nowcast and expand the nowcast to other Ohio beaches and a recreational river. Although predictive models have been shown to work well at some beaches and are becoming more widely accepted, implementation in many areas is limited by funding, lack of coordinated technical leadership, and lack of supporting epidemiological data.

Aquatic Ecosystem Health & Management↗
USGS2024

Environmental drivers and spatial patterns of antibiotic-resistant, enteric coliforms across a forest–urban riverscape

Antibiotic resistant bacteria are prevalent environmental contaminants in freshwaters, and antibiotic resistance genes circulate throughout the urban water cycle. The increase of antibiotic resistant pathogens threatens public health through direct and indirect exposure, and natural resource managers need information on the spatial patterns of antibiotic resistant bacteria and environmental factors associated with their distribution to improve water quality monitoring and to better assess human, animal, and environmental health risks. We collected water and epilithic biofilm samples and measured physicochemical environmental variables at 29 sites distributed longitudinally in the Green-Duwamish River basin, Washington, USA. We characterized catchment-wide patterns of gram-negative fecal indicator bacteria and hypothesized that the presence of antibiotic resistance would be associated with environmental heterogeneity, bacterial primary ecology, stream compartment, and stream type. Antibiotic resistance was determined by microbial growth on selective media supplemented with 3 different antibiotics (ampicillin, chloramphenicol, or tetracycline). Phenotypic antibiotic resistance was positively associated with disturbance, but resistance to at least 1 antibiotic was also detected in undeveloped river segments, with an 83% overall detection rate (i.e., 24 out of 29 sites, 17 in the mainstem and 7 in tributaries). The most probable number of Escherichia coli was associated with higher levels of antibiotic resistance of non- E. coli coliforms across the basin (ρ = 0.38, p < 0.01) but was not associated with antibiotic resistance of E. coli . Phenotypic resistance was highest among non- E. coli coliforms in the water column of tributaries draining moderately to extensively developed subcatchments. Generalized linear mixed-effects model results showed that 18% of the variance in presence of antibiotic resistance was explained by the fixed effects (summed CV across environmental variables, stream type, primary ecology, and stream compartment), and when a spatial random effect was included, the model explained 27% of the variance. Our study provides new evidence that environmental factors and bacterial primary ecology are important underlying factors associated with spatial patterns of antibiotic resistant enteric coliforms. We used macroecological concepts and a riverscape approach to characterize the distribution of antibiotic resistance with methods applicable to municipalities.

Freshwater Science↗
USGS2012

Cross-seasonal patterns of avian influenza virus in breeding and wintering migratory birds: a flyway perspective

The spread of avian influenza viruses (AIV) in nature is intrinsically linked with the movements of wild birds. Wild birds are the reservoirs for the virus and their migration may facilitate the circulation of AIV between breeding and wintering areas. This cycle of dispersal has become widely accepted; however, there are few AIV studies that present cross-seasonal information. A flyway perspective is critical for understanding how wild birds contribute to the persistence of AIV over large spatial and temporal scales, with implications for how to focus surveillance efforts and identify risks to public health. This study characterized spatio-temporal infection patterns in 10,389 waterfowl at two important locations within the Pacific Flyway--breeding sites in Interior Alaska and wintering sites in California's Central Valley during 2007-2009. Among the dabbling ducks sampled, the northern shoveler (Anas clypeata) had the highest prevalence of AIV at both breeding (32.2%) and wintering (5.2%) locations. This is in contrast to surveillance studies conducted in other flyways that have identified the mallard (Anas platyrhynchos) and northern pintail (Anas acuta) as hosts with the highest prevalence. A higher diversity of AIV subtypes was apparent at wintering (n=42) compared with breeding sites (n=17), with evidence of mixed infections at both locations. Our study suggests that wintering sites may act as an important mixing bowl for transmission among waterfowl in a flyway, creating opportunities for the reassortment of the virus. Our findings shed light on how the dynamics of AIV infection of wild bird populations can vary between the two ends of a migratory flyway.

Vector-Borne and Zoonotic Diseases↗
USGS2003

Surgical implantation of transmitters into fish

Although the Animal Welfare Act does not cover poikilotherms, individual institutions and policies and legal requirements other than the Animal Welfare Act (e.g., the US Public Health Service and the Interagency Research Animal Committee's Principles for the Utilization and Care of Vertebrate Animals Used in Testing, Research, and Training) require the review of projects involving fish by institutional animal care and use committees (IACUCs). IACUCs may, however, lack the knowledge and experience to evaluate fish projects judiciously, especially when the projects are in field settings. Surgeries involving implantation of transmitters and other instruments into the coelom, which now comprise a very common research tool in the study of free-ranging fishes, are examples of surgeries that use a broad spectrum of surgical and anesthetic techniques, some of which would not be considered acceptable for similar work on mammals. IACUCs should apply the standards they would expect to be used for surgeries on homeotherms to surgeries on fish. Surgeons should be carefully trained and experienced. Surgical instruments and transmitters should be sterile. Regulations and laws on the use of drugs in animals should be followed, particularly those concerned with anesthetics and antibiotics used on free-ranging fish. Exceptions to surgical procedures should be made only when circumstances are extreme enough to warrant the use of less than optimal procedures.

ILAR Journal↗
USGS2025

Extremophile hotspots linked to containerized industrial waste dumping in a deep-sea basin

Decaying barrels on the seafloor linked to DDT contamination have raised concerns about the public health implications of decades old industrial waste dumped off the coast of Los Angeles. To explore their contents, we collected sediment cores perpendicular to five deep-sea barrels. The concentration of DDT and its breakdown products were highly elevated relative to control sites yet did not vary with distance from the barrels, suggesting that they were not associated with the contamination. Sediment cores collected through white halos surrounding three barrels were enriched in calcite and had elevated pH. The associated microbial communities were low diversity and dominated by alkalophilic bacteria with metagenome-assembled genomes adapted to high pH. A solid concretion sampled between a white halo and barrel was composed of brucite, a magnesium hydroxide mineral that forms at high pH. Based on these findings, we postulate that leakage of containerized alkaline waste triggered the formation of mineral concretions that are slowly dissolving and raising the pH of the surrounding sediment pore water. This selects for taxa adapted to extreme alkalinity and drives the precipitation of “anthropogenic” carbonates forming white halos, which serve as a visual identifier of barrels that contained alkaline waste. Remarkably, containerized alkaline waste discarded >50 years ago represents a persistent pollutant creating localized mineral formations and microbial communities that resemble those observed at some hydrothermal systems. These formations were observed at one-third of the visually identified barrels in the San Pedro Basin and have unforeseen, long-term consequences for benthic communities in the region.

California↗
USGS2022

Social effects of rabies infection in male vampire bats (Desmodus rotundus)

Rabies virus (RABV) transmitted by the common vampire bat ( Desmodus rotundus ) poses a threat to agricultural development and public health throughout the Neotropics. The ecology and evolution of rabies host-pathogen dynamics are influenced by two infection-induced behavioral changes. RABV-infected hosts often exhibit increased aggression which facilitates transmission, and rabies also leads to reduced activity and paralysis prior to death. Although several studies document rabies-induced behavioral changes in rodents and other dead-end hosts, surprisingly few studies have measured these changes in vampire bats, the key natural reservoir throughout Latin America. Here, we take advantage of an experiment designed to test the safety and efficacy of an oral rabies vaccine in captive male vampire bats to quantify for the first time how rabies affects allogrooming and aggressive behaviors in the vampire bat. Compared to non-rabid vampire bats, rabid individuals reduced their allogrooming prior to death, but we did not detect increases in aggression among bats. To put our results in context, we review what is known and what remains unclear about behavioral changes of rabid vampire bats.

Biology Letters↗
USGS2023

Context-dependent representation of within- and between-model uncertainty: Aggregating probabilistic predictions in infectious disease epidemiology

Probabilistic predictions support public health planning and decision making, especially in infectious disease emergencies. Aggregating outputs from multiple models yields more robust predictions of outcomes and associated uncertainty. While the selection of an aggregation method can be guided by retrospective performance evaluations, this is not always possible. For example, if predictions are conditional on assumptions about how the future will unfold (e.g. possible interventions), these assumptions may never materialize, precluding any direct comparison between predictions and observations. Here, we summarize literature on aggregating probabilistic predictions, illustrate various methods for infectious disease predictions via simulation, and present a strategy for choosing an aggregation method when empirical validation cannot be used. We focus on the linear opinion pool (LOP) and Vincent average, common methods that make different assumptions about between-prediction uncertainty. We contend that assumptions of the aggregation method should align with a hypothesis about how uncertainty is expressed within and between predictions from different sources. The LOP assumes that between-prediction uncertainty is meaningful and should be retained, while the Vincent average assumes that between-prediction uncertainty is akin to sampling error and should not be preserved. We provide an R package for implementation. Given the rising importance of multi-model infectious disease hubs, our work provides useful guidance on aggregation and a deeper understanding of the benefits and risks of different approaches.

Journal of the Royal Society Interface↗
USGS2019

Concurrent assessment of epidemiological and operational uncertainties for optimal outbreak control: Ebola as a case study

Determining how to best manage an epidemiological outbreak may be hindered by both epidemiological uncertainty (i.e. about epidemiological processes) and operational uncertainty (i.e. about the effectiveness of candidate interventions). These two uncertainties are rarely addressed concurrently in epidemic studies, impeding decision-making. We present an approach to simultaneously address both sources of uncertainty. Epidemiological uncertainty is represented by a large ensemble of models of the 2014 West African Ebola outbreak. Operational uncertainty about the effectiveness of three classes of intervention is assessed for a wide range of potential effectiveness for each intervention. We ranked each intervention in terms of caseload reduction in each model, initially assuming an unlimited budget. To explore the role of budget limitation, we assessed the influence of three candidate cost functions relating intervention effectiveness and cost for different budget levels. The Value of Information (VoI) to resolve uncertainty is generally high in this study; appropriate information gain could reduce expected caseload by more than 50%. The ranking of interventions is jointly determined by the underlying epidemiological process, the effectiveness of the interventions and the size of the budget available for the program. An epidemiologically effective intervention might not be optimal if its costs outweigh its epidemiological benefit. Under higher budget conditions, resolution of epidemiological uncertainty is most valuable. When budgets are tight, however, operational and epidemiological uncertainty are equally important. Overall, our study demonstrates that significant public health improvements could result from a careful examination of both epidemiological and operational uncertainties within the same modelling structure. This approach can be applied to decision-making for management of other diseases for which multiple models and multiple interventions are available.

Proceedings of the Royal Society B↗
USGS2024

The potential of remote sensing for improved infectious disease ecology research and practice

Outbreaks of Covid-19 in humans, Dutch elm disease in forests, and highly pathogenic avian influenza in wild birds and poultry highlight the disruptive impacts of emerging infectious diseases on public health, ecosystems, and economies. Infectious disease dynamics often depend on environmental conditions that drive occurrence, transmission, and outbreaks. Remote sensing can contribute to infectious disease research and management by providing standardized environmental data across broad spatial and temporal extents, often at no cost to the user. Here, we 1) conduct a systematic review of primary literature to quantify current uses of remote sensing in disease ecology and 2) synthesize qualitative information to identify opportunities for further integration of remote sensing into disease ecology. We identify that modern advances in airborne remote sensing are promoting early detection of forest pathogens and that satellite data is contributing to the study of geographically widespread human diseases. We discuss opportunities for increased use of data products that characterize vegetation, surface water, and soil; provide data at high spatio-temporal and spectral resolutions; and quantify uncertainty in measurements. Additionally, combining remote sensing with animal movement telemetry can provide novel insights into wildlife disease. Integrating these opportunities will advance research and management of infectious diseases.

Proceedings of the Royal Society B: Biological Sci↗
USGS2024

A systematic review of laboratory investigations into the pathogenesis of avian influenza viruses in wild avifauna of North America

The lack of consolidated information regarding the response of wild bird species to infection with avian influenza virus (AIV) is a challenge to both conservation managers and researchers alike, with related sectors also impacted, such as public health and commercial poultry. Using two independent searches, we reviewed published literature for studies describing wild bird species experimentally infected with avian influenza to assess host species’ relative susceptibility to AIVs. Additionally, we summarize broad-scale parameters for elements such as shedding duration and minimum infectious dose that can be used in transmission modelling efforts. Our synthesis shows that waterfowl (i.e. Anatidae) compose the vast majority of published AIV pathobiology studies, whereas gulls and passerines are less represented in research despite evidence that they also are susceptible and contribute to highly pathogenic avian influenza disease dynamics. This study represents the first comprehensive effort to compile available literature regarding the pathobiology of AIVs in all wild birds in over a decade. This database can now serve as a tool to all researchers, providing generalized estimates of pathobiology parameters for a variety of wild avian families and an opportunity to critically examine and assess what is known and identify where further insight is needed.

Proceeding of the Royal Society B↗
USGS2021

The roles of antimicrobial resistance, phage diversity, isolation source, and selection in shaping the genomic architecture of Bacillus anthracis

Bacillus anthracis, the causative agent of anthrax disease, is a worldwide threat to livestock, wildlife and public health. While analyses of genetic data from across the globe have increased our understanding of this bacterium’s population genomic structure, the influence of selective pressures on this successful pathogen is not well understood. In this study, we investigate the effects of antimicrobial resistance, phage diversity, geography and isolation source in shaping population genomic structure. We also identify a suite of candidate genes potentially under selection, driving patterns of diversity across 356 globally extant B. anthracis genomes. We report ten antimicrobial resistance genes and 11 different prophage sequences, resulting in the first large-scale documentation of these genetic anomalies for this pathogen. Results of random forest classification suggest genomic structure may be driven by a combination of antimicrobial resistance, geography and isolation source, specific to the population cluster examined. We found strong evidence that a recombination event linked to a gene involved in protein synthesis may be responsible for phenotypic differences between comparatively disparate populations. We also offer a list of genes for further examination of B. anthracis evolution, based on high-impact single nucleotide polymorphisms (SNPs) and clustered mutations. The information presented here sheds new light on the factors driving genomic structure in this notorious pathogen and may act as a road map for future studies aimed at understanding functional differences in terms of B. anthracis biogeography, virulence and evolution.

Microbial Genomics↗
USGS2020

A multi-state occupancy modelling framework for robust estimation of disease prevalence in multi-tissue disease systems

Given the public health, economic and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance. The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection. A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi‐tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues. We developed a multi‐state occupancy modelling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained). We applied this framework to pseudorabies virus (PrV) DNA detection data obtained from whole blood; and oral, nasal and genital mucosa of 510 feral swine Sus scrofa during the years 2014–2016 in Florida, USA. The constrained model was better supported by data. PrV prevalence estimates varied among tissues and were higher than the naïve estimates, ranging from to 0.06 (CI: 0.02–0.14) in genital to 0.54 (CI: 0.14 ‐ 0.82) in nasal tissue. Probability of PrV detection ranged from 0.11 (CI: 0.06–0.18) in nasal to 0.51 (CI: 0.21–0.81) in genital tissue. PrV prevalence was not affected by the age or sex of the animal or the year of sampling, but prevalence increased as drought severity increased. The conditional probability of detecting PrV given infection in at least one tissue type within an individual was highest for nasal tissue, suggesting that nasal is the best tissue to sample for PrV surveillance if only one tissue can be sampled, at least for systems with tissue‐specific prevalence and detection probabilities similar to ours. Synthesis and applications . We focused on inferences about pathogen prevalence in multi‐tissue disease systems, dealing with both nondetection and potential dependencies among tissues in infection status. We found strong evidence of variation in both prevalence and detection probabilities among tissues. Our results emphasize the importance of sampling multiple tissues and of applying inference methods that account for imperfect detection in the surveillance of systemic diseases. The multi‐state modelling framework is broadly applicable to the surveillance of pathogens that infect multiple tissues and can be used even when the infection status of the pathogen in one tissue may depend on the infection status of the pathogen in other tissue(s).

Journal of Applied Ecology↗