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Seasonal variation in habitat use and animal behavior can alter host contact patterns with potential consequences for pathogen transmission dynamics. The endangered Florida panther (Puma concolor coryi) has experienced significant pathogen-induced mortality and continues to be at risk of future epidemics. Prior research has found increased panther movement in Florida's dry versus wet seasons, which may affect panther population connectivity and seasonally increase potential pathogen transmission. Our objective was to determine if Florida panthers are more spatially connected in dry seasons relative to wet seasons, and test if identified connectivity differences resulted in divergent predicted epidemic dynamics. We leveraged extensive panther telemetry data to construct seasonal panther home range overlap networks over an 11 year period. We tested for differences in network connectivity, and used observed network characteristics to simulate transmission of a broad range of pathogens through dry and wet season networks. We found that panthers were more spatially connected in dry seasons than wet seasons. Further, these differences resulted in a trend toward larger and longer pathogen outbreaks when epidemics were initiated in the dry season. Our results demonstrate that seasonal variation in behavioral patterns-even among largely solitary species-can have substantial impacts on epidemic dynamics., (© 2023. Springer Nature Limited.)

Porcine epidemic diarrhea virus (PEDv) was introduced to the U.S. in 2013 and is now considered to be endemic. Like many endemic diseases, it is challenging for producers to estimate and respond to spatial and temporal variation in risk. Utilizing a regional spatio-temporal dataset containing weekly PEDv infection status for ∼15 % of the U.S. sow herd, we present a machine learning platform developed to forecast the probability of PEDv infection in sow farms in the U.S. Participating stakeholders (swine production companies) in a swine-dense region of the U.S. shared weekly information on a) PEDv status of farms and b) animal movements for the past week and scheduled movements for the upcoming week. Environmental (average temperature, humidity, among others) and land use characteristics (hog density, proportion of area with different land uses) in a 5 km radius around each farm were summarized. Using the Extreme Gradient Boosting (XGBoost) machine learning model with Synthetic Minority Over-sampling Technique (SMOTE), we developed a near real-time tool that generates weekly PEDv predictions (pertaining to two-weeks in advance) to farms of participating stakeholders. Based on retrospective data collected between 2014 and 2017, the sensitivity, specificity, positive and negative predictive values of our model were 19.9, 99.9, 70.5 and 99.4 %, respectively. Overall accuracy was 99.3 %, although this metric is heavily biased by imbalance in the data (less than 0.7 % of farms had an outbreak each week). This platform has been used to deliver weekly real-time forecasts since December 2019. The forecast platform has a built-in feature to re-train the predictive model in order to remain as relevant as possible to current epidemiological situations, or to expand to a different disease. These dynamic forecasts, which account for recent animal movements, present disease distribution, and environmental factors, will promote data-informed and targeted disease management and prevention within the U.S. swine industry., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Lumpy skin disease virus (LSDV) is an infectious disease of cattle transmitted by arthropod vectors which results in substantial economic losses due to impact on production efficiency and profitability, and represents an emerging threat to international trade of livestock products and live animals. Since 2015, the disease has spread into the Northern Hemisphere including Azerbaijan, Kazakhstan, the Russian Federation and the Balkans. The rapid expansion of LSDV in those regions represented the emergence of the virus in more temperate regions than those in which LSDV traditionally occurred. The goal of this study was to assess the risk for further LSDV spread through the (a) analysis of environmental factors conducive for LSDV, and (b) estimate of the underlying LSDV risk, using a combination of ecological niche modelling and fine spatiotemporally explicit Bayesian hierarchical model on LSDV outbreak occurrence data. We used ecological niche modelling to estimate the potential distribution of LSDV outbreaks for 2014-2016. That analysis resulted in a spatial representation of environmental limits where, if introduced, LSDV is expected to efficiently spread. The Bayesian space-time model incorporated both environmental factors and the changing spatiotemporal distribution of the disease to capture the dynamics of disease spread and predict areas in which there is an increased risk for LSDV occurrence. Variables related to the average temperature, precipitation, wind speed, as well as land cover and host densities were important drivers explaining the observed distribution of LSDV in both modelling approaches. Areas of elevated LSDV risks were identified mainly in Russia, Turkey, Serbia and Bulgaria. The results suggest that, if current ecological and epidemiological conditions persist, further spread of LSDV in Eurasia may be expected. The results presented here advance our understanding of the ecological requirements of LSDV in temperate regions and may help in the design and implementation of prevention and surveillance strategies in the region., (© 2019 Blackwell Verlag GmbH.)

The spread of pathogens in swine populations is in part determined by movements of animals between farms. However, understanding additional characteristics that predict disease outbreaks and uncovering landscape factors related to between-farm spread are crucial steps toward risk mitigation. This study integrates animal movements with environmental risk factors to identify the occurrence of porcine epidemic diarrhea virus (PEDV) outbreaks. Using weekly farm-level incidence data from 332 sow farms, we applied machine-learning algorithms to quantify associations between risk factors and PEDV outbreaks with the ultimate goal of training predictive models and to identify the most important factors associated with PEDV occurrence. Our best algorithm was able to correctly predict whether an outbreak occurred during one-week periods with >80% accuracy. The most important predictors included pig movements into neighboring farms. Other important neighborhood attributes included hog density, environmental and weather factors such as vegetation, wind speed, temperature, and precipitation, and topographical features such as slope. Our neighborhood-based approach allowed us to simultaneously capture disease risks associated with long-distance animal movement as well as local spatial dynamics. The model presented here forms the foundation for near real-time disease mapping and will advance disease surveillance and control for endemic swine pathogens in the United States.

In the United States, slaughter surveillance combined with other measures has effectively maintained a very low prevalence of bovine tuberculosis (bTB). However, bTB continues to be sporadically detected, causing substantial economic burden to the government and cattle producers. To detect the infection earlier and reduce sudden economic losses, additional risk-based surveillance of live animals might be more cost-effective than slaughter surveillance alone to detect and prevent bTB infection. The objective of this study was to evaluate alternative risk-based surveillance strategies targeting high-risk herds to complement slaughter surveillance in a region with very low bTB prevalence. We developed an integrated within- and between-herd bTB transmission model with simulated premises-level cattle movements among beef and dairy herds in Minnesota for 10 years. We constructed ten risk-based surveillance strategies for beef herds and dairy herds, and predicted the epidemiological outcomes and costs for each strategy in combination with slaughter surveillance. Our models showed that slaughter surveillance alone resulted in low risk of between-herd transmission with typically small outbreak sizes, and also cost less compared to alternative risk-based surveillance measures. However, risk-based surveillance strategies could reduce the time to detect infection and the time to reach disease freedom by up to 9 months. At a higher initial prevalence, alternative risk-based surveillance could reduce the number of infected herds and shorten the time to disease freedom by almost 3 years (34-35 months). Our findings suggest that risk-based surveillance could detect infection more quickly and allow affected regions to reach disease freedom faster. If the bTB status of the affected regions changes after an outbreak happens, the reduced time to disease freedom could reduce the economic impact on the affected region., (Published by Elsevier B.V.)

Compartmental models have often been used to test the effectiveness and efficiency of alternative control strategies to mitigate the spread of infectious animal diseases. A fundamental principle of epidemiological modelling is that models should start as simple as possible and become as complex as needed. The simplest version of a compartmental model assumes that the population is closed, void of births and deaths and that this closed population mixes homogeneously, meaning that each infected individual has an equal probability of coming into contact with each susceptible individual in the population. However, this assumption may oversimplify field conditions, leading to conclusions about disease mitigation strategies that are suboptimal. Here, we assessed the impact of the homogeneous mixing/closed population assumption, which is commonly assumed for within-farm models of highly contagious diseases of swine, such as foot-and-mouth disease (FMD), on predictions about disease spread. Incorporation of farm structure (different barns or rooms for breeding and gestation, farrowing, nursery and finishing) and demography (piglet births and deaths, and animal movement within and off of the farm) resulted in transmission dynamics that differed in the latter portion of an outbreak. Specifically, farm structure and demography, which were included in the farrow to finish and farrow to wean farms, resulted in FMD virus persistence within the population under certain conditions. Results here demonstrate the impact of incorporating farm structure and demography into models of FMD spread in swine populations and will ultimately contribute to the design and evaluation of effective disease control strategies to mitigate the impact of potential incursions., (© 2018 Blackwell Verlag GmbH.)

Foot-and-mouth disease (FMD) poses a significant obstacle to international trade and economic development, and for that reason, FMD prevention, control and eradication are major goals guiding animal health policy in most countries. The purpose of this study was to conduct a retrospective spatiotemporal analysis of FMD outbreaks among livestock in the Republic of Kazakhstan (RK) from 1955 to 2013. During that time, several FMD control strategies were implemented in RK, which culminated with the World Organization for Animal Health (OIE) recognition of RK as a country that is FMD-free with partial vaccination (2015). Here, we describe and analyse the changes in spatial and temporal dynamics of FMD under different control strategies that were utilized as the country progressively moved towards eradication of the disease. A total number of 5,260 FMD outbreaks of serotype O and A (including the A 22 lineage) were recorded in the cattle, pig and small ruminant populations of RK during that period. We found that outbreaks occurred in spatiotemporal clusters only prior to 1970, which is before ring vaccination around outbreaks was first employed. This finding suggests that ring vaccination substantially reduced local spread and prevented large FMD epidemics in the country. Disease incidence steadily decreased after the implementation of ring vaccination and culling of infected animals, with spatiotemporal clusters only occurring as a result of an introduction of an antigenically distinct variant of serotype A. From 1955 to 1984, FMD outbreaks demonstrated two seasonal peaks of incidence in the spring and fall. In contrast, only the peak in spring was observed between 1984 and 2013. Quantitative knowledge on how different policy and alternative control strategies contributed to RK achieving FMD-free status could improve prospects for continued control in RK and inform control strategies in other FMD-endemic regions., (© 2018 Blackwell Verlag GmbH.)

Background: Lumpy skin disease (LSD) is a devastating transboundary viral disease of cattle which causes significant loss in production. Although this disease has been reported in Uganda and throughout East Africa, there is almost no information about its epidemiology, spatial or spatio-temporal distribution. We carried out a retrospective study on the epidemiology of LSD in Uganda between the years 2002 and 2016, using data on reported outbreaks collected monthly by the central government veterinary administration. Descriptive statistics were computed on frequency of outbreaks, number of cases, vaccinations and deaths. We evaluated differences in the number of reported outbreaks across different regions (agro-ecological zones), districts, months and years. Spatial, temporal and space-time scan statistics were used to identify possible epidemiological clusters of LSD outbreaks., Results: A total of 1161 outbreaks and 319,355 cases of LSD were reported from 55 out of 56 districts of Uganda. There was a significant difference in incidence between years (P = 0.007) and across different regions. However, there was no significant difference in the number of outbreaks per month (P = 0.443). The Central region reported the highest number of outbreaks (n = 418, 36%) followed by Eastern (n = 372, 32%), Southwestern (n = 140, 12%), Northern (n = 131, 11%), Northeastern (n = 37, 3%), Western (n = 41, 4%) and Northwestern (n = 22, 2%) regions. Several endemic hotspots for the circulation of LSD were identified in the Central and Eastern regions using spatial cluster analyses. Outbreaks in endemic hotspots were less seasonal and had strikingly lower mortality and case-fatality rates than the other regions, suggesting an underlying difference in the epidemiology and impact of LSD in these different zones., Conclusion: Lumpy Skin disease is endemic in Uganda, with outbreaks occurring annually in all regions of the country. We identified potential spatial hotspots for LSD outbreaks, underlining the need for risk-based surveillance to establish the actual disease prevalence and risk factors for disease maintenance. Space-time analysis revealed that sporadic LSD outbreaks tend to occur both within and outside of endemic areas. The findings from this study will be used as a baseline for further epidemiological studies for the development of sustainable programmes towards the control of LSD in Uganda.

Foot-and-mouth disease (FMD) is an important transboundary disease with substantial economic impacts. Although between-herd transmission of the disease has been well studied, studies focusing on within-herd transmission using farm-level outbreak data are rare. The aim of this study was to estimate parameters associated with within-herd transmission, host physiological factors and FMD virus (FMDV) persistence using data collected from an outbreak that occurred at a large, organized dairy farm in India. Of 1,836 regularly vaccinated, adult dairy cattle, 222 had clinical signs of FMD over a 39-day period. Assuming homogenous mixing, a frequency-dependent compartmental model of disease transmission was built. The transmission coefficient and basic reproductive number were estimated to be between 16.2-18.4 and 67-88, respectively. Non-pregnant animals were more likely to manifest clinical signs of FMD as compared to pregnant cattle. Based on oropharyngeal fluid (probang) sampling and FMDV-specific RT-PCR, four of 36 longitudinally sampled animals (14%) were persistently infected carriers 10.5 months post-outbreak. There was no statistical difference between subclinical and clinically infected animals in the duration of the carrier state. However, prevalence of NSP-ELISA antibodies differed significantly between subclinical and clinically infected animals 12 months after the outbreak with 83% seroprevalence amongst clinically infected cattle compared to 69% of subclinical animals. This study further elucidates within-herd FMD transmission dynamics during the acute-phase and characterizes duration of FMDV persistence and seroprevalence of FMD under natural conditions in an endemic setting., (© 2017 Blackwell Verlag GmbH.)

The goal of this study was to characterize the properties and duration of the foot-and-mouth disease (FMD) carrier state and associated serological responses subsequent to vaccination and naturally occurring infection at two farms in northern India. Despite previous vaccination of cattle in these herds, clinical signs of FMD occurred in October 2013 within a subset of animals at the farms containing juvenile-yearling heifers and steers (Farm A) and adult dairy cattle (Farm B). Subsequent to the outbreak, FMD virus (FMDV) asymptomatic carriers were identified in both herds by seroreactivity to FMDV non-structural proteins and detection of FMDV genomic RNA in oropharyngeal fluid. Carriers' seroreactivity and FMDV genome detection status were subsequently monitored monthly for 23 months. The mean extinction time of the carrier state was 13.1 ± 0.2 months, with extinction having occurred significantly faster amongst adult dairy cattle at Farm B compared to younger animals at Farm A. The rate of decrease in the proportion of carrier animals was calculated to be 0.07 per month. Seroprevalence against FMDV non-structural proteins decreased over the course of the study period, but was found to increase transiently following repeated vaccinations. These data provide novel insights into viral and host factors associated with the FMDV carrier state under natural conditions. The findings reported herein may be relevant to field veterinarians and governmental regulatory entities engaged in FMD response and control measures., (© 2017 Blackwell Verlag GmbH.)