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Wild boar (WB, Sus scrofa) populations are increasing in urban areas, posing an epidemiological risk for zoonotic pathogens such as hepatitis E virus (HEV) and antimicrobial-resistant Campylobacter (AMR-CAMP), as well as non-zoonotic pathogens such as African swine fever virus (ASFV). An epidemiological extension of a validated Agent-Based Model (ABM) was developed to assess the one-year epidemiological scenarios of HEV, AMR-CAMP, and ASFV in the synurbic WB-human interface in Barcelona, Spain. The predicted citizen exposure was similar for HEV and AMR-CAMP, at 0.79% and 0.80% of the human population in Barcelona, respectively, despite AMR-CAMP being more prevalent in the WB population than HEV. This suggests a major role of faeces in pathogen transmission to humans in urban areas, resulting in a non-negligible public health risk. The ASFV model predicted that the entire WB population would be exposed to the virus through carcasses (87.6%) or direct contact (12.6%) in 51–71 days after the first case, with an outbreak lasting 71–124 days and reducing the initial WB population by 95%. The ABM predictions are useful for animal and public health risk assessments and to support risk-based decision-making. The study underscores the need for interdisciplinary cooperation among animal, public, and environmental health managers, and the implementation of the One Health approach to address the epidemiological and public health risks posed by the synurbization of WB in urban areas. The spatially explicit epidemiological predictions of the ABM can be adapted to other diseases and scenarios at the wildlife-livestock-human interface.

Background: The effectiveness of the early treatment for antiviral agents in SARS-CoV-2 infection is closely related to patient comorbidities. Data on effectiveness in immunocompromised patients are limited, with reports involving highly heterogeneous and not well-defined populations. We aimed to assess the effectiveness of treatment in reducing hospitalizations in a real-world cohort of severely immunocompromised COVID-19 outpatients., Patients and Methods: We conducted a multicentre, retrospective, observational cohort study of immunocompromised outpatients attended in infectious diseases departments from 1 January to 31 December 2022. Propensity score matching (PSM) multivariable logistic regression models were used to estimate the adjusted odds ratio [(aOR, 95% confidence interval (CI)] for the association between antiviral prescription and outcome (COVID-19-related hospitalization up to Day 90)., Results: We identified 746 immunocompromised outpatients with confirmed SARS-CoV-2 infection. After eligibility criteria and PSM, a total of 410 patients were analysed: 205 receiving treatment (remdesivir, sotrovimab or nirmatrelvir/ritonavir) and 205 matched controls. Fifty-two patients required at least one COVID-19-related hospitalization 8 (3.9%) versus 44 (21.5%) in the antiviral and matched control cohorts, respectively. There were 13 deaths at 90 days, of which only 4 were COVID-19-related and none in the antiviral treatment group. After adjustment for residual confounders, the use of early therapy was associated with a protective effect on the risk of hospitalization [aOR 0.13 (0.05-0.29)], as was the use of biological immunomodulators [aOR 0.27 (0.10-0.74)], whereas chronic obstructive pulmonary disease [aOR 4.65 (1.09-19.69)] and anti-CD20 use [aOR 2.76 (1.31-5.81)] increased the odds., Conclusions: Early antiviral treatment was associated with a reduced risk of COVID-19-related hospitalization in ambulatory severely immunocompromised COVID-19 patients., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Aquatic habitats provide a bridge for influenza transmission among wild and domestic species. However, water sources pose highly variable physicochemical and ecological characteristics that affect avian influenza virus (AIV) stability. Therefore, the risk of survival or transmissibility of AIV in the environment is quite variable and has been understudied. In this study, we determine the risk of waterborne transmission and environmental persistence of AIV in a wild/domestic bird interface in the Central Mexico plateau (North America) during the winter season using a multi-criteria decision analysis (MCDA). A total of 13 eco-epidemiological factors were selected from public-access databases to develop the risk assessment. The MCDA showed that the Atarasquillo wetland presents a higher persistence risk in January. Likewise, most of the backyard poultry farms at this wild-domestic interface present a high persistence risk (50%). Our results suggest that drinking water may represent a more enabling environment for AIV persistence in contrast with wastewater. Moreover, almost all backyard poultry farms evidence a moderate or high risk of waterborne transmission especially farms close to water bodies. The wildlife/domestic bird interface on the Atarasquillo wetland holds eco-epidemiological factors such as the presence of farms in flood-prone areas, the poultry access to outdoor water, and the use of drinking-water troughs among multiple animal species that may enhance waterborne transmission of AIV. These findings highlight the relevance of understanding the influence of multiple factors on AIV ecology for early intervention and long-term control strategies., (© 2024. The Author(s).)

Background: Recurrent bacterial cystitis, often referred to as recurrent urinary tract infection (UTI), can be difficult to manage and alternative treatments are needed., Hypothesis/objective: Intravesicular administration of asymptomatic bacteriuria (ASB) E. coli 212 will not be inferior to antimicrobial treatment for the management of recurrent UTI in dogs., Animals: Thirty-four dogs with >1 UTI in the 12 months before presentation., Methods: All dogs were deemed normal otherwise based on absence of abnormalities on physical examination, CBC, serum biochemical panel, and abdominal ultrasonography. Dogs were randomized to 1 of 2 treatment groups: Group 1 antimicrobials for 7 days or group 2 intravesicular administration of ASB E. coli 212. Owners were provided a voiding questionnaire regarding their dogs' clinical signs, which was completed daily for 14 days to assess clinical cure. Dogs were examined on days 7 and 14 to assess clinical cure, and urine specimens were submitted for urinalysis and bacterial culture., Results: Clinical cure rates for ASB E. coli 212-treated dogs were not inferior to 7 days of antimicrobial treatment with a 12% margin of difference to determine non-inferiority. No significant difference was found between the treatment groups on days 7 and 14 in the proportion of dogs achieving ≥50% or ≥75% reduction in their clinical score compared with baseline., Conclusions and Clinical Importance: These data suggest that intravesicular administration of ASB E. coli 212 is not inferior to antimicrobials for the treatment of recurrent UTI in dogs. This biotherapeutic agent could help alleviate the need for antimicrobials for some dogs with recurrent UTI, improving antimicrobial stewardship., (© 2024 The Author(s). Journal of Veterinary Internal Medicine published by Wiley Periodicals LLC on behalf of American College of Veterinary Internal Medicine.)

Interspecific interactions are highly relevant in the potential transmission of shared pathogens in multi-host systems. In recent decades, several technologies have been developed to study pathogen transmission, such as proximity loggers, GPS tracking devices and/or camera traps. Despite the diversity of methods aimed at detecting contacts, the analysis of transmission risk is often reduced to contact rates and the probability of transmission given the contact. However, the latter process is continuous over time and unique for each contact, and is influenced by the characteristics of the contact and the pathogen's relationship with both the host and the environment. Our objective was to assess whether a more comprehensive approach, using a movement-based model which assigns a unique transmission risk to each contact by decomposing transmission into contact formation, contact duration and host characteristics, could reveal disease transmission dynamics that are not detected with more traditional approaches. The model was built from GPS-collar data from two management systems in Spain where animal tuberculosis (TB) circulates: a national park with extensively reared endemic cattle, and an area with extensive free-range pigs and cattle farms. In addition, we evaluated the effect of the GPS device fix rate on the performance of the model. Different transmission dynamics were identified between both management systems. Considering the specific conditions under which each contact occurs (i.e. whether the contact is direct or indirect, its duration, the hosts characteristics, the environmental conditions, etc.) resulted in the identification of different transmission dynamics compared to using only contact rates. We found that fix intervals greater than 30 min in the GPS tracking data resulted in missed interactions, and intervals greater than 2 h may be insufficient for epidemiological purposes. Our study shows that neglecting the conditions under which each contact occurs may result in a misidentification of the real role of each species in disease transmission. This study describes a clear and repeatable framework to study pathogen transmission from GPS data and provides further insights to understand how TB is maintained in multi-host systems in Mediterranean environments., (© 2024 The Author(s). Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.)

Pig farming in Mexico is critical to the economy and food supply. Mexico has achieved advancements in swine health and established an electronic database that records swine movements (Sistema Nacional de Avisos de Movilización, SNAM). In this study, we characterized swine movement patterns in México between 2017 and 2019 to identify specific areas and periods that require concentrated efforts for effective epidemiological surveillance and disease control. We employed a Social Network Analysis (SNA) methodology to comprehensively describe and analyze the intricate patterns of pig movement. In addition, we sought to integrate swine population density into the analysis. We used metrics to characterize the network structure and identify the most critical nodes in the movement network. Cohesion metrics were used to identify commercial communities characterized by a high level of interconnectivity in swine movements between groups of nodes. Of a cumulative count of 662,255 pig shipments, 95.9% were attributed to slaughterhouse shipments. We observed that 54% of all Mexican municipalities were part of the network; however, the density of the movement network was less than 0.14%. We identified four Swine Production Centers in Mexico with high interconnectivity in the movement network. We detected moderate positive correlations (ρ ≥0.4 and <0.6, p < 0.001) between node metrics and swine population indicators, whereas the number of commercial swine facilities showed weak correlations with the node metrics. We identified six large, geographically clustered commercial communities that aligned with the Swine Production Centers. This study provides a comprehensive overview of swine movement patterns in Mexico and their close association with swine production centers, which play a dual role as producers and traders within the swine industry of Mexico. Our research offers valuable insights for policymakers in developing disease prevention and control strategies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zaldivar-Gomez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Prescribing fenbendazole medicated feed for pheasants in the USA is considered extra-label drug use under CPG Sec 615.115, and a safe estimated withdrawal interval (WDI) must be applied following administration to this minor food-producing species. This study sought to determine the pharmacokinetic and residue depletion profile for fenbendazole and its major metabolites to estimate a WDI for pheasants following fenbendazole administration as an oral medicated feed., Method: Pheasants ( n = 32) were administered fenbendazole as an oral medicated feed (100 ppm) for 7 days. Fenbendazole, fenbendazole sulfoxide, and fenbendazole sulfone (FBZ-SO 2 ) in liver and muscle samples were analyzed using HPLC-UV. Tissue WDIs were estimated using FDA, European Medicines Agency (EMA), and half-life multiplication methods for US poultry tolerances, EMA maximum residue limits, and the analytical limit of detection (LOD; 0.004 ppm). Terminal tissue elimination half-lives (T 1/2 ) were estimated by non-compartmental analysis using a naïve pooled data approach., Results: The tissue T 1/2 was 14.4 h for liver, 13.2 h for thigh muscle, and 14.1 h for pectoral muscle. The maximum estimated withdrawal interval was 153 h (7 days) for FBZ-SO 2 in pectoral muscle using the FDA tolerance method (95% confidence interval for the 99 th percentile of the population), and the LOD as the residue limit., Discussion: The results from this study support the use of FBZ-SO 2 as the marker residue in the liver of pheasants and the provision of evidence based WDIs following the extra-label administration of fenbendazole medicated feed (100 ppm) for 7 days., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Carreño Gútiez, Mercer, Martínez-López, Griffith, Wetzlich and Tell.)

Objective: Readmission for COVID-19 is associated with high mortality, saturation of health services, and high costs. This study aimed to assess the incidence and risk factors of readmissions in COVID-19 patients in a regional hospital of Spain from February 2020 to March 2021., Methods: A retrospective cohort study describing the characteristics of adult patients readmitted within thirty days of discharge after being infected with SARS-CoV-2 was carried out. Readmission associated risk factors were analysed using a binary logistic regression model., Results: Of the 967 patients who survived their first COVID-19 admission, 70 (7.2%) were readmitted within thirty days. Of these, 34.3% presented pneumonia progression, 15.7% functional deterioration, and 12.9% other infections. The mortality rate during readmission was 28.6%. There were no statistically significant differences in the cumulative incidence of readmissions between the epidemic periods (p=0.241). Factors independently associated with readmission were: diabetes mellitus (aOR 1.96, 95%CI 1.07-3.57, p=0.030); acute kidney failure (aOR 2.69, 95%CI 1.43-5.07, p=0.002); not being a candidate for intensive care (aOR 7.68, 95% CI 4.28-13.80, p<0.001); and not being prescribed corticosteroids at discharge (aOR 2.15, 95% CI 1.04-4.44; p=0.039)., Conclusions: A substantial proportion of patients admitted due to COVID-19 are readmitted, and they carry a high letality. Diabetes mellitus, acute kidney failure, not being a candidate for ICU admission, and not being prescribed corticosteroids on discharge are independently associated with an increased risk of readmission.

Background: Community-acquired Staphylococcus aureus (CA-Sa) skin and soft tissue infections (SSTIs) are historically associated with densely populated urban areas experiencing high poverty rates, intravenous drug use, and homelessness. However, the epidemiology of CA-Sa SSTIs in the United States has been poorly understood since the plateau of the Community-acquired Methicillin-resistant Staphylococcus aureus epidemic in 2010. This study examines the spatial variation of CA-Sa SSTIs in a large, geographically heterogeneous population and identifies neighborhood characteristics associated with increased infection risk., Methods: Using a unique neighborhood boundary, California Medical Service Study Areas, a hotspot analysis, and estimates of neighborhood infection risk ratios were conducted for all CA-Sa SSTIs presented in non-Federal California emergency departments between 2016 and 2019. A Bayesian Poisson regression model evaluated the association between neighborhood-level infection risk and population structure, neighborhood poverty rates, and being a healthcare shortage area., Results: Emergency departments in more rural and mountainous parts of California experienced a higher burden of CA-Sa SSTIs between 2016 and 2019. Neighborhoods with high infection rates were more likely to have a high percentage of adults living below the federal poverty level and be a designated healthcare shortage area. Measures of population structure were not associated with infection risk in California neighborhoods., Conclusions: Our results highlight a potential change in the epidemiology of CA-Sa SSTIs in California emergency departments. Future studies should investigate the CA-Sa burden in other geographies to identify whether this shift in epidemiology holds across other states and populations. Further, a more thorough evaluation of potential mechanisms for the clustering of infections seen across California neighborhoods is needed., (© 2024. The Author(s).)

Weaned dairy heifers are a relatively understudied production group. Bovine respiratory disease (BRD) is the most common cause of antimicrobial drug (AMD) use, morbidity, and mortality in this production group. The study of antimicrobial resistance (AMR) is complicated because many variables that may affect AMR are related. This study generates hypotheses regarding the farm- and animal-level variables (e.g., vaccination, lane cleaning, and AMD use practices) that may be associated with AMR in respiratory isolates from weaned dairy heifers. A cross-sectional study was performed using survey data and respiratory isolates ( Pasteurella multocida , Mannheimia haemolytica , and Histophilus somni ) collected from 341 weaned dairy heifers on six farms in California. Logistic regression and Bayesian network analyses were used to evaluate the associations between farm- and animal-level variables with minimum inhibitory concentration (MIC) classification of respiratory isolates against 11 AMDs. Farm-level variables associated with MIC classification of respiratory isolates included the number of source farms of a calf-rearing facility, whether the farm practiced onsite milking, the use of lagoon water for flush lane cleaning, and respiratory and pinkeye vaccination practices. Animal-level variables associated with a MIC classification included whether the calf was BRD-score-positive and time since the last phenicol treatment.

Flea-borne typhus (FBT), also referred to as murine typhus, is an acute febrile disease in humans caused by the bacteria Rickettsia typhi. Currently, cases of FBT are reported for public health surveillance purposes (i.e., to detect incidence and outbreaks) in a few U.S. states. In California, healthcare providers and testing laboratories are mandated to report to their respective local public health jurisdictions whenever R. typhi or antibodies reactive to R. typhi are detected in a patient, who then report cases to state health department. In this study, we characterize the epidemiology of flea-borne typhus cases in California from 2011 to 2019. A total of 881 cases were reported during this period, with most cases reported among residents of Los Angeles and Orange Counties (97%). Demographics, animal exposures, and clinical courses for case patients were summarized. Additionally, spatiotemporal cluster analyses pointed to five areas in southern California with persistent FBT transmission.

African animal trypanosomiasis (AAT) is one of the major constraints to animal health and production in sub-Saharan Africa. To inform AAT control in Uganda and help advance along the progressive control pathway (PCP), we characterized AAT prevalence among eight host species in Uganda and explored factors that influence the prevalence variation between studies. We retrieved AAT prevalence publications (n = 2232) for Uganda (1980-2022) from five life sciences databases, focusing on studies specifying AAT detection methods, sample size, and the number of trypanosome-positive animals. Following PRISMA guidelines, we included 56 publications, and evaluated publication bias by the Luis Furuya-Kanamori (LFK) index. National AAT prevalence under DNA diagnostic methods for cattle, sheep and goats was 22.15%, 8.51% and 13.88%, respectively. Under DNA diagnostic methods, T. vivax was the most common Trypanosoma sp. in cattle (6.15%, 95% CI: 2.91-10.45) while T. brucei was most common among small ruminants (goats: 8.78%, 95% CI: 1.90-19.88, and sheep: 8.23%, 95% CI: 4.74-12.50, respectively). Northern and Eastern regions accounted for the highest AAT prevalence. Despite the limitations of this study (i.e., quality of reviewed studies, underrepresentation of districts/regions), we provide insights that could be used for better control of AAT in Uganda and identify knowledge gaps that need to be addressed to support the progressive control of AAT at country level and other regional endemic countries with similar AAT eco-epidemiology., (© 2023. The Author(s).)

The pork industry is an essential part of the global food system, providing a significant source of protein for people around the world. A major factor restraining productivity and compromising animal wellbeing in the pork industry is disease outbreaks in pigs throughout the production process: widespread outbreaks can lead to losses as high as 10% of the U.S. pig population in extreme years. In this study, we present a machine learning model to predict the emergence of infection in swine production systems throughout the production process on a daily basis, a potential precursor to outbreaks whose detection is vital for disease prevention and mitigation. We determine features that provide the most value in predicting infection, which include nearby farm density, historical test rates, piglet inventory, feed consumption during the gestation period, and wind speed and direction. We utilize these features to produce a generalizable machine learning model, evaluate the model's ability to predict outbreaks both seven and 30 days in advance, allowing for early warning of disease infection, and evaluate our model on two swine production systems and analyze the effects of data availability and data granularity in the context of our two swine systems with different volumes of data. Our results demonstrate good ability to predict infection in both systems with a balanced accuracy of [Formula: see text] on any disease in the first system and balanced accuracies (average prediction accuracy on positive and negative samples) of [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] on porcine reproductive and respiratory syndrome, porcine epidemic diarrhea virus, influenza A virus, and Mycoplasma hyopneumoniae in the second system, respectively, using the six most important predictors in all cases. These models provide daily infection probabilities that can be used by veterinarians and other stakeholders as a benchmark to more timely support preventive and control strategies on farms., (© 2023. Springer Nature Limited.)

Introduction: The evidence for remdesivir therapy in immunocompromised patients is scarce. To evaluate remdesivir (RDV) effectiveness and safety in COVID-19 outpatients at high risk for progression in a real-world setting, we compare the outcome in immunocompromised (IC) patients with that in non-immunocompromised patients. Methods: Two hospitals conducted a retrospective study of all adult patients with mild-to-moderate SARS-CoV-2 infection at high risk for disease progression who were treated as outpatients with a 3-day course of RDV (1st January-30th September 2022). The primary effectiveness endpoint was a composite of any cause of hospitalization or death by day 30. A multiple logistic regression model was built to explore the association between immune status and clinical outcome, estimating adjusted odds ratios [aORs (95% CI)]. Results: We have included 211 patients, of which 57% were males, with a median age of 65 years (IQR 53-77), 70.1% were vaccinated (three or four doses), and 61.1% were IC. The median duration of symptoms before RDV treatment was 3 days (IQR 2-5). During follow-up, 14 (6.6%) patients were hospitalized, of which 6 (2.8%) were hospitalized for COVID-19 progression. No patient required mechanical ventilation, and two patients died (non-COVID-19-related). After accounting for potential confounders, only anti-CD20 treatment was associated with the composed outcome [aOR 5.35 (1.02-27.5, 95% CI)], whereas the immunocompetence status was not [aOR 1.94 (0.49-7.81, 95% CI)]. Conclusion: Early COVID-19 outpatient treatment with a 3-day course of remdesivir in vaccinated patients at high risk for disease progression during the Omicron surge had a good safety profile. It was associated with a low rate of all-cause hospitalization or death, regardless of immunocompetence status., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Ramos-Rincón, Pinargote-Celorio, Llenas-García, Moreno-Pérez, González-Cuello, Gonzalez-de-la-Aleja, Martínez-López, Reus, García-López, Rodríguez, Boix and Merino.)

African swine fever (ASF) is currently threatening the global swine industry. Its unstoppable global spread poses a serious risk to Spain, one of the world's leading producers. Over the past years, there has been an increased global burden of ASF not only in swine but also swine products. Unfortunately, many pigs are not diagnosed before slaughter and their products are used for human consumption. These ASF-contaminated products are only a source for new ASF outbreaks when they are consumed by domestic pigs or wild boar, which may happen either by swill feeding or landfill access. This study presents a quantitative stochastic risk assessment model for the introduction of ASF into Spain via the legal import of swine products, specifically pork and pork products. Entry assessment, exposure assessment, consequence assessment and risk estimation were carried out. The results suggest an annual probability of ASF introduction into Spain of 1.74 × 10 -4 , the highest risk being represented by Hungary, Portugal, and Poland. Monthly risk distribution is homogeneously distributed throughout the year. Illegal trade and pork product movement for own consumption (e.g., air and ship passenger luggage) have not been taken into account due to the lack of available, accredited data sources. This limitation may have influenced the model's outcomes and, the risk of introduction might be higher than that estimated. Nevertheless, the results presented herein would contribute to allocating resources to areas at higher risk, improving prevention and control strategies and, ultimately, would help reduce the risk of ASF introduction into Spain., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2023. Published by Elsevier Ltd.)

Rabies is a major zoonotic disease around the world, causing significant mortality to both humans and animals, especially in low- and middle-income countries. In Bangladesh, rabies is transmitted mostly by the bite of infected dogs and jackals to humans and domestic livestock, causing severe economic losses and public health hazards. Our study analyzed national passive surveillance data of veterinary hospital-reported rabies cases in cattle, buffalo, sheep, and goats from 2015 to 2017 in all 64 districts of Bangladesh. We used a zero-inflated negative binomial regression model to identify the main environmental and socio-economic risk factors associated with rabies occurrence in livestock, and we used model results to generate risk maps. Our study revealed that monsoon precipitation (RR=1.28, p-value=0.043) was positively associated with rabies cases in livestock, and the percentage of adults who have completed university education was also a significant predictor (RR=0.58, p-value<0.001) likely suggesting that districts with higher education levels tended to have a lower reporting of rabies cases in livestock. The standardized incidence ratio maps and predicted relative risk maps revealed a high risk of rabies cases in southeast areas in Bangladesh. We recommend implementing risk-based vaccination strategies in dogs and jackals in those high-risk areas before monsoon to reduce the burden of rabies cases in domestic ruminants and humans in Bangladesh., Competing Interests: Declaration of Competing Interest Authors declare no conflict of interests., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Anomaly detection methods have a great potential to assist the detection of diseases in animal production systems. We used sequence data of Porcine Reproductive and Respiratory Syndrome (PRRS) to define the emergence of new strains at the farm level. We evaluated the performance of 24 anomaly detection methods based on machine learning, regression, time series techniques and control charts to identify outbreaks in time series of new strains and compared the best methods using different time series: PCR positives, PCR requests and laboratory requests. We introduced synthetic outbreaks of different size and calculated the probability of detection of outbreaks (POD), sensitivity (Se), probability of detection of outbreaks in the first week of appearance (POD1w) and background alarm rate (BAR). The use of time series of new strains from sequence data outperformed the other types of data but POD, Se, POD1w were only high when outbreaks were large. The methods based on Long Short-Term Memory (LSTM) and Bayesian approaches presented the best performance. Using anomaly detection methods with sequence data may help to identify the emergency of cases in multiple farms, but more work is required to improve the detection with time series of high variability. Our results suggest a promising application of sequence data for early detection of diseases at a production system level. This may provide a simple way to extract additional value from routine laboratory analysis. Next steps should include validation of this approach in different settings and with different diseases., (© 2023. L’Institut National de Recherche en Agriculture, Alimentation et Environnement (INRAE).)

Poverty is an often-cited driver of health disparities, and associations between poverty and community-acquired Methicillin-resistant Staphylococcus aureus (CA-MRSA) infection are well documented. However, the pathways through which poverty influences infection have not been thoroughly examined. This project aims to identify mediating variables, or mechanisms, explaining why area-level poverty is associated with CA-MRSA infection in Californians. Bayesian multilevel models accounting for spatial confounding were developed to test whether the association between area-level poverty and CA-MRSA infection is mediated by living in a primary care shortage area (HCSA), living near an adult correctional facility, and residential environmental degradation. The association between area-level poverty and CA-MRSA infection can be partially explained by spatial autocorrelation, living in an HCSA, and environmental degradation in the neighborhood. Combined, the mediators explain approximately 6% of the odds of CA-MRSA infection for individuals living in neighborhoods with high poverty rates and 50% of the statistical association between area-level poverty and CA-MRSA infection. The statistical association between area-level poverty and infection was completely explained by the mediators for individuals living in neighborhoods with low poverty rates., Competing Interests: Declaration of competing interest None., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Toxoplasma gondii is a globally distributed zoonotic protozoan parasite. Infection with T. gondii can cause congenital toxoplasmosis in developing fetuses and acute outbreaks in the general population, and the disease burden is especially high in South America. Prior studies found that the environmental stage of T. gondii , oocysts, is an important source of infection in Brazil; however, no studies have quantified this risk relative to other parasite stages. We developed a Bayesian quantitative risk assessment (QRA) to estimate the relative attribution of the two primary parasite stages (bradyzoite and oocyst) that can be transmitted in foods to people in Brazil. Oocyst contamination in fruits and greens contributed significantly more to overall estimated T. gondii infections than bradyzoite-contaminated foods (beef, pork, poultry). In sensitivity analysis, treatment, i.e., cooking temperature for meat and washing efficiency for produce, most strongly affected the estimated toxoplasmosis incidence rate. Due to the lack of regional food contamination prevalence data and the high level of uncertainty in many model parameters, this analysis provides an initial estimate of the relative importance of food products. Important knowledge gaps for oocyst-borne infections were identified and can drive future studies to improve risk assessments and effective policy actions to reduce human toxoplasmosis in Brazil.

With all the sensor data currently generated at high frequency in dairy farms, there is potential for earlier diagnosis of postpartum diseases compared with traditional monitoring methodologies. Our objectives were 1) to compare the impact of sensor data pre-processing on classifier performance by using multiple time windows before a given metritis event, while considering other cow-level factors and farm-scheduled activities; 2) to compare the performance of random forest (RF), k-nearest neighbors (k-NN), and support vector machine (SVM) classifiers at different decision thresholds using different number of past observations (time-lags) for the detection of behavioral patterns associated with changes in metritis scores; and 3) to compare classifier performance between each one of the five behaviors registered every hour by an ear-tag 3-axis accelerometer (CowManager, Agis Autimatisering, Harmelen, Netherlands). A total of 239 metritis events were created by comparing metritis scores between two consecutive clinical evaluations from cows that were retrospectively selected from a dataset containing sensor data and health information during the first 21 days postpartum from June 2014 to May 2017. Hourly sensor data classified by the accelerometer as either ruminating, eating, not active (including both standing or lying), and two different levels of activity (active and high activity) behaviors corresponding to the 3 days before each metritis event were aggregated every 24-, 12-, 6-, and 3-hour time windows. Multiple time-lags were also used to determine the optimal number of past observations needed for optimal classification. Similarly, different decision thresholds were compared in terms of model performance. Depending on the classifier, algorithm hyperparameters were optimized using grid search (RF, k-NN, SVM) and random search (RF). All behaviors changed throughout the study period and showed distinct daily patterns. From the three algorithms, RF had the highest F 1 score followed by k-NN and SVM. Furthermore, sensor data aggregated every 6- or 12-h time windows had the best model performance at multiple time-lags. We concluded that the data from the first 3 days post-partum should be discarded when studying metritis, and either one of the five behaviors measured with CowManager could be used when predicting metritis when sensor data were aggregated every 6- or 12-hour time windows, and using time-lags corresponding to 2-3 days before a given event, depending on the time window used. This study shows how to maximize sensor data in their potential for disease prediction, enhancing the performance of algorithms used in machine learning., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens ( Escherichia coli, Streptococcus suis, Salmonella sp., Pasteurella multocida , and Bordetella bronchiseptica ). We found that Seasonal Auto-Regressive Integrated Moving Average (SARIMA) outperformed five baselines, including Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA). We hope this study provides valuable tools to predict the AMR burden not only of the pathogens assessed in this study but also of other bacterial pathogens., Competing Interests: RCR is sole proprietor of R.C. Robbins Swine Consulting Services, PLLC, United States. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kim, Rupasinghe, Halev, Huang, Rezaei, Clavijo, Robbins, Martínez-López and Liu.)

Antimicrobial resistance (AMR) is one of the major challenges of the century and should be addressed with a One Health approach. This study aimed to develop a tool that can provide a better understanding of AMR patterns and improve management practices in swine production systems to reduce its spread between farms. We generated similarity networks based on the phenotypic AMR pattern for each farm with information on important bacterial pathogens for swine farming based on the Euclidean distance. We included seven pathogens: Actinobacillus suis, Bordetella bronchiseptica, Escherichia coli, Glaesserella parasuis, Pasteurella multocida, Salmonella spp., and Streptococcus suis; and up to seventeen antibiotics from ten classes. A threshold criterion was developed to reduce the density of the networks and generate communities based on their AMR profiles. A total of 479 farms were included in the study although not all bacteria information was available on each farm. We observed significant differences in the morphology, number of nodes and characteristics of pathogen networks, as well as in the number of communities and susceptibility profiles of the pathogens to different antimicrobial drugs. The methodology presented here could be a useful tool to improve health management, biosecurity measures and prioritize interventions to reduce AMR spread in swine farming., (© 2023. The Author(s).)

The number and popularity of backyard poultry and livestock farming have rapidly increased in California as well as other states in the United States following consumers' preference for local and organic products in the last few years. This study aimed to investigate current on-farm management and farmers' understanding of Veterinary Feed Directive (VFD) and California Senate Bill (SB) 27 implications for disease prevention, biosecurity procedures, and antimicrobial use in small-scale and backyard farms in California. The survey consisted of 38 questions. The responses of 242 backyard and small-scale livestock owners were investigated in this study. Descriptive statistics summarized survey responses, and multivariable logistic regression evaluated the association of antibiotics purchase and use, and the impact of VFD and SB27 on antibiotic use with demographics and on-farm management. Backyard and small-scale farmers in California mostly raised chickens or small ruminants with small herd sizes kept for personal use. Antibiotics were generally used for individual treatment of a sick animal with the guidance of a veterinarian. VFD and SB27 implementation promoted the judicious use of antibiotics, specifically, by enhancing the relationship between backyard and small-scale farmers with veterinarians and treating fewer animals with antibiotics under veterinary oversight. Therefore, better access to veterinary service in backyard and small-scale farms will improve the farmer's knowledge of good husbandry practices with judicious antimicrobial use in livestock and finally contribute to reducing the risk of antimicrobial resistance in California., Competing Interests: No authors have competing interests, (Copyright: © 2022 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Babesia species are intraerythrocytic piroplasms that can result in disease characterized by hemolytic anemia and thrombocytopenia. Of the 5 species that are known to infect canids in the United States, Babesia conradae is most frequently diagnosed in California, and Babesia vogeli is prevalent in the US. Despite the recent re-emergence of B. conradae , the mechanism of transmission is not known. Coyotes ( Canis latrans ) have been a proposed reservoir of disease, and previous work has shown that dogs with known aggressive interactions with coyotes are at greater risk for infection. This study aimed to determine the prevalence of B. conradae in wild coyote populations in California to assess the viability of coyotes as a potential source of infection for domestic dogs. Four hundred and sixty-one splenic samples were obtained during post-mortem examination of coyote carcasses from Southern California, Fresno, and Hopland. Demographic data including age, sex, cause of death, and urbanity were collected for each coyote. DNA was extracted from samples and amplified using real-time PCR with primers specific for the B. conradae ITS-2 gene. The 18S gene was amplified and sequenced using conventional PCR primers specific to the Babesia genus from any coyotes positive for B. conradae. In total, 22 coyotes tested positive for B. conradae in Fresno (n = 15), Orange (n = 4), San Bernardino (n = 1), and Los Angeles counties (n = 1) with an overall prevalence of 4.8%. Coyotes from Fresno ( P< .01) and rural coyotes ( P< .01) were significantly more likely to be infected with B. conradae . Ten of 14 samples sequenced were 99-100% homologous to B. conradae, and 4 samples were 100% homologous with B. vogeli DNA indicating co-infection with both pathogens. This study demonstrates that coyotes can become infected and harbor B. conradae and B. vogeli and should be investigated as a possible source of infection in domestic dogs., Competing Interests: None., (© 2022 The Authors.)

Systemic phaeohyphomycosis caused by the dematiaceous mould Veronaea botryosa is an important emergent disease affecting captive sturgeons (Acipenser spp.). The disease, colloquially known as "fluid belly," causes morbidity and mortality in adult animals resulting in significant economic losses to the aquaculture industry. Advancements in therapeutic and prophylactic protocols have been partially hampered by the lack of basic protocols to grow and manipulate the fungus in the laboratory. In this study, microbroth kinetic protocols were established to analyse V. botryosa growth in seven nutrient media at different temperatures. Generated area under the curve (AUC) indicates that potato flake dextrose broth (PFD-B) and Sabouraud dextrose broth (SD-B) incubated at 25°C provided the greatest growth. The generated protocol was then used to test the susceptibility of V. botryosa isolates to natamycin, a macrolide polyene antifungal agent used as a food preservative. SD-B and RPMI with l-glutamine (+RPMI-B) containing different concentrations of natamycin were inoculated with V. botryosa conidia and the generated growth curves were compared using cubic smoothing spline model. The non-inhibitory concentration and minimal inhibitory concentration (MIC; decrease of AUC by 90% compared with control) were determined to be <1 μg/mL and 16 μg/mL of natamycin in SD-B media. To gain an understanding of the tissue distribution of natamycin in white sturgeon, pharmacokinetics was tested. Based on pharmacokinetic parameters determined in this study and targeting a blood concentration >16 μg/mL for 24 h, an intravenous dose >1 g/kg would be needed, making the use of this drug unrealistic. The information presented in this study can be used to investigate susceptibility of pathogenic fungus to antimicrobials and disinfectants as well as support future therapeutic protocols against emerging fungal diseases like fluid belly., (© 2022 John Wiley & Sons Ltd.)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a viral pathogen that quickly became a global pandemic in the winter of 2020-2021. In response, governments issued social distancing orders to minimize transmission by reducing community contacts. We tested the efficacy of this social distancing at the state level during the first 2 months of the pandemic in the United States. We utilized data on daily SARS-CoV-2 case numbers and human community mobility (anonymized, aggregated cell phone location data stratified into six categories used as an index of social distancing), the date of government-issued social distancing orders, demographics, urbanization and public transportation. We implemented cross-correlation to identify lag times between declines in mobility and SARS-CoV-2 cases. Incorporating state-specific lag times, we tested for associations between case counts and mobility metrics using Bayesian multilevel models. Decreased mobility around grocery stores/pharmacies, retail/recreation locations, transit stations and workplaces was correlated with decreases in SARS-CoV-2 cases with significant lag times of ≥21 days. Social distancing orders were associated with fewer cumulative SARS-CoV-2 cases when they were put in place earlier. Community mobility had already started declining prior to most social distancing orders, especially the more restrictive orders implemented later in the pandemic. Social distancing is an important tool that has been implemented throughout the pandemic to decrease SARS-CoV-2 transmission, although with significant social and economic impacts. Our results suggest that declines in cases were observed several weeks subsequent to implementation of social distancing measures, and that implementing social distancing earlier could potentially minimize the duration of time these policies need to be in effect. Our findings can inform ongoing management of this pandemic and other emerging infectious disease outbreaks by identifying areas where reductions in mobility are associated with reduced disease transmission, and the expected time frame between behavioural changes and measurable population outcomes., (© 2022 Wiley-VCH GmbH.)

Equine influenza virus (EIV) is a highly contagious pathogen of equids, and a well-known burden in global equine health. EIV H3N8 variants seasonally emerged and resulted in EIV outbreaks in the United States and worldwide. The present study evaluated the pattern of cross-regional EIV H3N8 spread and evolutionary characteristics at US and global scales using Bayesian phylogeography with balanced subsampling based on regional horse population size. A total of 297 haemagglutinin (HA) sequences of global EIV H3N8 were collected from 1963 to 2019 and subsampled to global subset (n = 67), raw US sequences (n = 100) and US subset (n = 44) datasets. Discrete trait phylogeography analysis was used to estimate the transmission history of EIV using four global and US genome datasets. The North American lineage was the major source of globally dominant EIV variants and spread to other global regions. The US EIV strains generally spread from the southern and midwestern regions to other regions. The EIV H3N8 accumulated approximately three nucleotide substitutions per year in the HA gene under heterogeneous local positive selection. Our findings will guide better decision making of target intervention strategies of EIV H3N8 infection and provide the better scheme of genomic surveillance in the United States and global equine health., (© 2022 Wiley-VCH GmbH.)

Mastitis is a prevalent and expensive disease in dairy herds worldwide. Blanket dry cow therapy (BDCT), in which all quarters of all cows are infused with antimicrobials at the dry-off, is a cornerstone for mastitis control in many countries. An alternative approach is the use of selective dry cow therapy (SDCT), in which only cows with high risk for intramammary infection (IMI) at dry-off receive antimicrobials. Our objectives in this cross-sectional study were to estimate the potential reduction in the use of antimicrobials if SDCT was adopted in the US by using cow-level dairy herd data and to describe the factors associated with cows being classified as high-risk for an IMI at dry-off. Besides, we aimed to describe the seasonality in IMI at dry-off. We used cow-level somatic cell score (SCS) test-day data from herds in the western US (DHIA, Dairy Herd Improvement Association, AgriTech, Visalia, CA) to create five scenarios to classify cows as high risk for IMI at dry-off. Associations between cow-level data and state were also used in logistic regression models. We also calculated the average animal-defined daily dosage of antimicrobials per cow per year around the dry period if a BDCT or SDCT approach is used, adjusting for the risk of cases of clinical mastitis in the next lactation for the SDCT approach. The point prevalence of IMI at the last test-day before dry-off varied between 15.0 % and 54 % for primiparous and 34.0 % and 85 % for multiparous cows, depending on the scenario. By extrapolating our results obtained from using data from dairies enrolled in the DHIA testing program for the western US, we demonstrated that regardless of the criteria used to classify cows as high risk of IMI at dry-off (scenarios 1-5), if selective dry cow therapy is adopted in the US, the dairy industry could reduce the use of antimicrobials around the dry-off between 31 % and 66 %. Multiparous cows had greater odds of being classified as high-risk than primiparous cows. Cows dried off in the spring, summer, and fall had lower odds of being classified as high-risk compared to cows dried off in the winter. Advanced days in milk at dry-off was associated with greater risk of IMI at dry-off. Greater milk yield and higher protein percentage at the last test-day before dry-off were associated with decreased odds of a cow being classified as high-risk at dry-off, cows in small herds had greater odds of being classified as high-risk at dry-off, and a variation among states was observed., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Globalization of trade, and the interconnectivity of animal production systems, continues to challenge efforts to control disease. A better understanding of trade networks supports development of more effective strategies for mitigation for transboundary diseases like African swine fever (ASF), classical swine fever (CSF), and foot-and-mouth disease (FMD). North Macedonia, bordered to the north and east by countries with ongoing ASF outbreaks, recently reported its first incursion of ASF. This study aimed to describe the distribution of pigs and pig farms in North Macedonia, and to characterize the live pig movement network. Network analyses on movement data from 2017 to 2019 were performed for each year separately, and consistently described weakly connected components with a few primary hubs that most nodes shipped to. In 2019, the network demonstrated a marked decrease in betweenness and increase in communities. Most shipments occurred within 50 km, with movements <6 km being the most common (22.5%). Nodes with the highest indegree and outdegree were consistent across years, despite a large turnover among smallholder farms. Movements to slaughterhouses predominated (85.6%), with movements between farms (5.4%) and movements to market (5.8%) playing a lesser role. This description of North Macedonia's live pig movement network should enable implementation of more efficient and cost-effective mitigation efforts strategies in country, and inform targeted educational outreach, and provide data for future disease modeling, in the region., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 O'Hara, Beltrán-Alcrudo, Hovari, Tabakovski and Martínez-López.)

Research on cancer in dogs and cats, among other diseases, finds an important source of information in registry data collected from hospitals. These sources have proved to be decisive in establishing incidences and identifying temporal patterns and risk factors. However, the attendance of patients is not random, so the correct delimitation of the hospital catchment area (CA) as well as the identification of the factors influencing its shape is relevant to prevent possible biases in posterior inferences. Despite this, there is a lack of data-driven approaches in veterinary epidemiology to establish CA. Therefore, our aim here was to apply a Bayesian method to estimate the CA of a hospital. We obtained cancer ( n = 27,390) and visit ( n = 232,014) registries of dogs and cats attending the Veterinary Medical Teaching Hospital of the University of California, Davis from 2000 to 2019 with 2,707 census tracts (CTs) of 40 neighboring counties. We ran hierarchical Bayesian models with different likelihood distributions to define CA for cancer cases and visits based on the exceedance probabilities for CT random effects, adjusting for species and period (2000-2004, 2005-2009, 2010-2014, and 2015-2019). The identified CAs of cancer cases and visits represented 75.4 and 83.1% of the records, respectively, including only 34.6 and 39.3% of the CT in the study area. The models detected variation by species (higher number of records in dogs) and period. We also found that distance to hospital and average household income were important predictors of the inclusion of a CT in the CA. Our results show that the application of this methodology is useful for obtaining data-driven CA and evaluating the factors that influence and predict data collection. Therefore, this could be useful to improve the accuracy of analysis and inferences based on registry data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Díaz Cao, Kent, Rupasinghe and Martínez-López.)

Equine influenza virus (EIV) is a major infectious pathogen causing significant respiratory signs in equids worldwide. Voluntary surveillances in the United States recently reported EIV detection in horses with respiratory signs even with adequate vaccine protocols and biosecurity programs and posed a concern about suboptimal effectiveness of EIV vaccine in the United States. This study aims to determine the genetic characteristics of 58 field EIV H3N8 strains in the United States from 2012 to 2017 using the phylogenetic analysis based on the haemagglutinin (HA) gene. Amino acid substitution and acquisition of N-glycosylation of the HA gene were also evaluated. Phylogenetic analysis identified that almost all US field strains belonged to the Florida clade 1 (FC1) except one Florida clade 2 strain from a horse imported in 2014. US EIV strains in 2017 shared 11 fixed amino acid substitutions in the HA gene, compared to the vaccine strain (A/equine/Ohio/2003), and two additional amino acid substitutions were detected in 2019. The introduction of foreign EIV strains into the United States was not detected, but antigenic drift without acquisition of N-glycosylation in the HA gene was observed in US field strains until 2017. Considering the global dominance of FC1 strains, subsequent antigenic drift of US EIV strains should be monitored for better effectiveness of the EIV vaccine in the United States and global equine industries., (© 2021 Wiley-VCH GmbH.)

The current COVID-19 pandemic highlights the need for zoonotic infectious disease surveillance. Avian influenza virus (AIV) poses a significant threat to animal and public health due to its pandemic potential. Virus-contaminated water has been suggested as an important AIV spread mechanism among multiple species. Nevertheless, few studies have characterized the global AIV subtype diversity and distribution in environmental water. Therefore, this study aims to provide an updated descriptive and phylogenetic analysis of AIVs isolated in water samples from high risk-sites for influenza outbreaks (i.e. live bird markets, poultry farms, and wild bird habitats) on a global scale. The descriptive analysis evidenced that 21 subtypes were reported from nine countries between 2003 and 2020. Fourteen AIV subtypes were solely reported from Asian countries. Most of the viral sequences were obtained in China and Bangladesh with 47.44% and 23.93%, respectively. Likewise, the greatest global AIV subtype diversity was observed in China with 12 subtypes. Live bird markets represented the main sampling site for AIV detection in water samples (64.1%), mostly from poultry cage water. Nevertheless, the highest subtype diversity was observed in water samples from wild bird habitats, especially from the Izumi plain and the Dongting Lake located in Japan and China, respectively. Water from drinking poultry troughs evidenced the greatest subtype diversity in live bird markets; meanwhile, environmental water used by ducks had the highest number of different subtypes in poultry farms. Maximum-likelihood phylogenetic trees of hemagglutinin (HA) and neuraminidase (NA) genes showed that some sequences were closely related among different poultry/wild bird-related environments from different geographic origins. Therefore, the results suggest that even though the availability of gene sequences in public-access databases varies greatly among countries, environmental AIV surveillance represents a useful tool to elucidate potential viral diversity in wild and domestic bird populations., (© 2021 Wiley-VCH GmbH.)

Rabies is endemic in Cambodia. For exposed humans, post-exposure prophylaxis (PEP) is very effective in preventing this otherwise fatal disease. The Institut Pasteur du Cambodge (IPC) in Phnom Penh was the primary distributor of PEP in Cambodia until 2018. Since then, and to increase distribution of PEP, two new centers have been opened by IPC in the provinces of Battambang and Kampong Cham. Data on bitten patients, who sometimes bring the head of the biting animal for rabies analyses, have been recorded by IPC since 2000. However, human cases are not routinely recorded in Cambodia, making it difficult to establish a human burden of disease and generate a risk map of dog bites to inform the selection of future PEP center locations in high-risk areas. Our aim was to assess the impact of accessibility to rabies centers on the yearly rate of PEP patients in the population and generate a risk map to identify the locations where new centers would be the most beneficial to the Cambodian population. To accomplish this, we used spatio-temporal Bayesian regression models with the number of PEP patients as the outcome. The primary exposure variable considered was travel time to the nearest IPC center. Secondary exposure variables consisted of travel time to a provincial capital and urban proportion of the population. Between 2000 and 2016, a total of 293,955 PEP patient records were identified. Our results showed a significant negative association between travel time to IPC and the rate of PEP patients: an increase in one hour travel time from the living location to IPC PEP centers leads to a reduction in PEP rate of 70% to 80%. Five provinces were identified as the most efficient locations for future centers to maximize PEP accessibility: Banteay Meanchey, Siem Reap, Takeo, Kampot and Svay Rieng. Adding a PEP center in every provincial capital would increase the proportion of Cambodians living within 60 minutes of a PEP center from 26.6% to 64.9%, and living within 120 minutes from 52.8% to 93.3%, which could save hundreds of lives annually., Competing Interests: The authors have declared that no competing interests exist.

Porcine reproductive and respiratory syndrome virus (PRRSV) poses an extensive economic threat to the United States swine industry. The high degree of PRRSV genetic and antigenic variability challenges existing vaccination programs. We evaluated the ORF5 sequence of 1,931 PRRSV-2 strains detected from >300 farms managed by two pork production systems in the midwestern United States from 2001 to 2020 to assess the genetic diversity and molecular characteristics of heterologous PRRSV-2 strains. Phylogenetic analysis was performed on ORF5 sequences and classified using the global PRRSV classification system. N-glycosylation and the global and local selection pressure in the putative GP5 encoded by ORF5 were estimated. The PRRSV-2 sequences were classified into lineage 5 (L5; n  = 438[22.7%]) or lineage 1 (L1; n  = 1,493[77.3%]). The L1 strains belonged to one of three subclades: L1A ( n  = 1,225[63.4%]), L1B ( n  = 69[3.6%]), and L1C/D ( n  = 199[10.3%]). 10 N-glycosylation sites were predicted, and positions N44 and N51 were detected in most GP5 sequences ( n  = 1,801[93.3%]). Clade-specific N-glycosylation sites were observed: 57th in L1A, 33rd in L1B, 30th and 34th in L1C/D, and 30th and 33rd in L5. We identified nine and 19 sites in GP5 under significant positive selection in L5 and L1, respectively. The 13th, 151st, and 200th positive selection sites were exclusive to L5. Heterogeneity of N-glycosylation and positive selection sites may contribute to varying the evolutionary processes of PRRSV-2 strains circulating in these swine production systems. L1A and L5 strains denoted excellence in adaptation to the current swine population by their extensive positive selection sites with higher site-specific selection pressure. IMPORTANCE Porcine reproductive and respiratory syndrome virus (PRRSV) is known for its high genetic and antigenic variability. In this study, we evaluated the ORF5 sequences of PRRSV-2 strains circulating in two swine production systems in the midwestern United States from 2001 to 2020. All the field strains were classified into four major groups based on genetic relatedness, where one group is closely related to the Ingelvac PRRS MLV strain. Here, we systematically compared differences in the ORF5 polymorphisms, N-glycosylation sites, and local and global evolutionary dynamics between different groups. Sites 44 and 51 were common for N-glycosylation in most amino acid sequences ( n  = 1,801, 93.3%). We identified that the L5 sequences had more positive selection pressure compared to the L1 strains. Our findings will provide valuable insights into the evolutionary mechanisms of PRRSV-2 and these molecular changes may lead to suboptimal effectiveness of Ingelvac PRRS MLV vaccine.

The US is currently experiencing a return to raising domestic pigs outdoors, due to consumer demand for sustainably-raised animal products. A challenge in raising pigs outdoors is the possibility of these animals interacting with feral pigs and an associated risk of pathogen transmission. California has one of the largest and widest geographic distributions of feral pigs. Locations at greatest risk for increased contact between both swine populations are those regions that contain feral pig suitable habitat located near outdoor-raised domestic pigs. The main aim of this study entailed identifying potential high-risk areas of disease transmission between these two swine populations. Aims were achieved by predicting suitable feral pig habitat using Maximum Entropy (MaxEnt); mapping the spatial distribution of outdoor-raised pig operations (OPO); and identifying high-risk regions where there is overlap between feral pig suitable habitat and OPO. A MaxEnt prediction map with estimates of the relative probability of suitable feral pig habitat was built, using hunting tags as presence-only points. Predictor layers were included in variable selection steps for model building. Five variables were identified as important in predicting suitable feral pig habitat in the final model, including the annual maximum green vegetation fraction, elevation, the minimum temperature of the coldest month, precipitation of the wettest month and the coefficient of variation for seasonal precipitation. For the risk map, the final MaxEnt model was overlapped with the location of OPOs to categorize areas at greatest risk for contact between feral swine and domestic pigs raised outdoors and subsequent potential disease transmission. Since raising pigs outdoors is a remerging trend, feral pig numbers are increasing nationwide, and both groups are reservoirs for various pathogens, the contact between these two swine populations has important implications for disease transmission in the wildlife-livestock interface., Competing Interests: No competing interests or conflict of interests.

Accurate, timely, and cost-effective blood chemistry analysis is an essential tool for directing emergency treatment, monitoring the health status of captive and free-ranging individuals and flocks, and improving the efficacy of conservation actions. Blood samples were obtained from 52 canvasbacks ( Aythya valisineria ) that were captured on San Francisco Bay, California, during December 2017 as part of a long-term study. Reference values and clinical agreement were determined for blood chemistry and plasma protein parameters among four commonly used point-of-care devices (VetScan ® VS2, i-STAT ® , AlphaTRAK ® 2 glucometer, refractometer) and two gold standard laboratory analyzers (Roche cobas ® c501, Helena SPIFE 3000 system). Canvasback reference values were generally within expected ranges for Anatidae species with the exception of higher upper limits for sodium and chloride. Creatine kinase and aspartate transaminase values exceeded a published threshold for diagnosis of capture myopathy even though study birds were captured using low-stress techniques and successfully released. With the exception of higher alkaline phosphatase in hatch-year canvasbacks, no age or sex differences were observed for any analyte in this population that was captured during a nonbreeding period. Analysis of analyzer agreement found raw VetScan aspartate transaminase, calcium, glucose, and uric acid values; corrected VetScan albumin, potassium, sodium, and total protein values; raw i-STAT glucose and potassium values; and corrected i-STAT sodium and chloride values were clinically interchangeable with Roche cobas values. Raw VetScan and i-STAT glucose values were also interchangeable. However, none of the Roche or point-of-care analyzer plasma protein values were in clinical agreement with gold standard electrophoresis values. The findings of this study highlight the need for analyzer- or technique-specific reference values and provide biologists and veterinarians quantitative reference values using currently available analyzers to better assess and respond to the health of individuals and populations.

Background: Efforts to protect residents in nursing homes involve non-pharmaceutical interventions, testing, and vaccine. We sought to quantify the effect of testing and vaccine strategies on the attack rate, length of the epidemic, and hospitalization., Methods: We developed an agent-based model to simulate the dynamics of SARS-CoV-2 transmission among resident and staff agents in a nursing home. Interactions between 172 residents and 170 staff based on data from a nursing home in Los Angeles, CA. Scenarios were simulated assuming different levels of non-pharmaceutical interventions, testing frequencies, and vaccine efficacy to reduce transmission., Results: Under the hypothetical scenario of widespread SARS-CoV-2 in the community, 3-day testing frequency minimized the attack rate and the time to eradicate an outbreak. Prioritization of vaccine among staff or staff and residents minimized the cumulative number of infections and hospitalization, particularly in the scenario of high probability of an introduction. Reducing the probability of a viral introduction eased the demand on testing and vaccination rate to decrease infections and hospitalizations., Conclusions: Improving frequency of testing from 7-days to 3-days minimized the number of infections and hospitalizations, despite widespread community transmission. Vaccine prioritization of staff provides the best protection strategy when the risk of viral introduction is high., (© 2022. The Author(s).)

Objective: Many antimicrobial resistance (AMR) studies in both human and veterinary medicine use traditional statistical methods that consider one bacteria and one antibiotic match at a time. A more robust analysis of AMR patterns in groups of animals is needed to improve on traditional methods examining antibiotic resistance profiles, the associations between the patterns of resistance or reduced susceptibility for all isolates in an investigation. The use of Bayesian network analysis can identify associations between distributions; this investigation seeks to add to the growing body of AMR pattern research by using Bayesian networks to identify relationships between susceptibility patterns in Escherichia coli ( E. coli ) isolates obtained from weaned dairy heifers in California., Methods: A retrospective data analysis was performed using data from rectal swab samples collected from 341 weaned dairy heifers on six farms in California and selectively cultured for E. coli . Antibiotic susceptibility tests for 281 isolates against 15 antibiotics were included. Bayesian networks were used to identify joint patterns of reduced susceptibility, defined as an increasing trend in the minimum inhibitory concentration (MIC) values. The analysis involved learning the network structure, identifying the best fitting graphical mode, and learning the parameters in the final model to quantify joint probabilities., Results: The graph identified that as susceptibility to one antibiotic decreases, so does susceptibility to other antibiotics in the same or similar class. The following antibiotics were connected in the final graphical model: ampicillin was connected to ceftiofur; spectinomycin was connected with trimethoprim-sulfamethoxazole, and this association was mediated by farm; florfenicol was connected with tetracycline., Conclusions: Bayesian network analysis can elucidate complex relationships between MIC patterns. MIC values may be associated within and between drug classes, and some associations may be correlated with farm of sample origin. Treating MICs as discretized variables and testing for joint associations in trends may overcome common research problems surrounding the lack of clinical breakpoints., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Morgan, Depenbrock and Martínez-López.)

Emerging infectious diseases (EIDs), especially those with zoonotic potential, are a growing threat to global health, economy, and safety. The influence of global warming and geoclimatic variations on zoonotic disease epidemiology is evident by alterations in the host, vector, and pathogen dynamics and their interactions. The objective of this article is to review the current literature on the observed impacts of climate change on zoonoses and discuss future trends. We evaluated several climate models to assess the projections of various zoonoses driven by the predicted climate variations. Many climate projections revealed potential geographical expansion and the severity of vector-borne, waterborne, foodborne, rodent-borne, and airborne zoonoses. However, there are still some knowledge gaps, and further research needs to be conducted to fully understand the magnitude and consequences of some of these changes. Certainly, by understanding the impact of climate change on zoonosis emergence and distribution, we could better plan for climate mitigation and climate adaptation strategies., (Copyright © 2021. Published by Elsevier B.V.)