Toxoplasmosis epidemic in a population of urbanised allied rock-wallabies (Petrogale assimilis) on Magnetic Island (Yunbenun), North Queensland.

A mortality event involving 23 allied rock-wallabies (Petrogale assimilis) displaying neurological signs and sudden death occurred in late April to May 2021 in a suburban residential area directly adjacent to Magnetic Island National Park, on Magnetic Island (Yunbenun), North Queensland, Australia. Three allied rock-wallabies were submitted for necropsy, and in all three cases, the cause of death was disseminated toxoplasmosis. This mortality event was unusual because only a small, localised population of native wallabies inhabiting a periurban area on a tropical island in the Great Barrier Reef World Heritage Area were affected. A disease investigation determined the outbreak was likely linked to the presence of free-ranging feral and domesticated cats inhabiting the area. There were no significant deaths of other wallabies or wildlife in the same or other parts of Magnetic Island (Yunbenun) at the time of the outbreak. This is the first reported case of toxoplasmosis in allied rock-wallabies (Petrogale assimilis), and this investigation highlights the importance of protecting native wildlife species from an infectious and potentially fatal parasitic disease.

The spectral underpinnings of pathogen spread on animal networks.

Predicting what factors promote or protect populations from infectious disease is a fundamental epidemiological challenge. Social networks, where nodes represent hosts and edges represent direct or indirect contacts between them, are important in quantifying these aspects of infectious disease dynamics. However, how network structure and epidemic parameters interact in empirical networks to promote or protect animal populations from infectious disease remains a challenge. Here we draw on advances in spectral graph theory and machine learning to build predictive models of pathogen spread on a large collection of empirical networks from across the animal kingdom. We show that the spectral features of an animal network are powerful predictors of pathogen spread for a variety of hosts and pathogens and can be a valuable proxy for the vulnerability of animal networks to pathogen spread. We validate our findings using interpretable machine learning techniques and provide a flexible web application for animal health practitioners to assess the vulnerability of a particular network to pathogen spread.

Application of Flow Cytometry in the Diagnosis of Bovine Epidemic Disease.

As science and technology continue to advance, the use of flow cytometry is becoming more widespread. It can provide important information about cells in the body by detecting and analysing them, thereby providing a reliable basis for disease diagnosis. In the diagnosis of bovine epidemic diseases, flow cytometry can be used to detect bovine viral diarrhoea, bovine leukaemia, bovine brucellosis, bovine tuberculosis, and other diseases. This paper describes the structure of a flow cytometer (liquid flow system, optical detection system, data storage and analysis system) and its working principles for rapid quantitative analysis and sorting of single cells or biological particles. Additionally, the research progress of flow cytometry in the diagnosis of bovine epidemic diseases was reviewed in order to provide a reference for future research and application of flow cytometry in the diagnosis of bovine epidemic diseases.

Optimizing testing strategies for early detection of disease outbreaks in animal trade networks via MCMC.

The animal trades between farms and other livestock holdings form a complex livestock trade network. The movement of animals between trade actors plays an important role in the spread of infectious diseases among premises. Particularly, the outbreak of silent diseases that have no clinically obvious symptoms in the animal trade system should be diagnosed by taking special tests. In practice, the authorities regularly conduct examinations on a random number of farms to make sure that there was no outbreak in the system. However, these actions, which aim to discover and block a disease cascade, are yet far from the effective and optimum solution and often fail to prevent epidemics. A testing strategy is defined as making decisions about distributing the fixed testing budget N between farms/nodes in the network. In this paper, first, we apply different heuristics for selecting sentinel farms on real and synthetic pig-trade networks and evaluate them by simulating disease spreading via the SI epidemic model. Later, we propose a Markov chain Monte Carlo (MCMC) based testing strategy with the aim of early detection of outbreaks. The experimental results show that the proposed method can reasonably well decrease the size of the outbreak on both the realistic synthetic and real trade data. A targeted selection of an N/52 fraction of nodes in the real pig-trade network based on the MCMC or simulated annealing can improve the performance of a baseline strategy by 89%. The best heuristic-based testing strategy results in a 75% reduction in the average size of the outbreak compared to that of the baseline testing strategy.

Descriptive epidemiology and spatial analysis of African swine fever epidemics in Can Tho, Vietnam, 2019.

The objectives of this study were to describe the epidemiology of African swine fever (ASF) and to identify factors that increased commune-level risk for ASF in Can Tho, a province in the Mekong River Delta of Vietnam. In 2019, a total of 2377 of the 5220 pig farms in Can Tho were ASF positive, an incidence risk of 46 (95% CI 44-47) ASF positive farms for every 100 farms at risk. Throughout the outbreak ASF resulted in either the death or culling of 59,529 pigs out of a total population size of 124,516 (just under half of the total pig population, 48%). After the first detection in Can Tho in May 2019, ASF spread quickly across all districts with an estimated dissemination ratio (EDR) of greater than one up until the end of July 2019. A mixed-effects Poisson regression model was developed to identify risk factors for ASF. One hundred unit increases in the number of pigs per square kilometre was associated with a 1.28 (95% CrI 1.05-1.55) fold increase in commune-level ASF incidence rate. One unit increases in the number of pig farms per square kilometre was associated with a 0.91 (95% CrI 0.84-0.99) decrease in commune-level ASF incidence rate. Mapping spatially contiguous communes with elevated (unaccounted-for) ASF risk provide a means for generating hypotheses for continued disease transmission. We propose that the analyses described in this paper might be run on an ongoing basis during an outbreak and disease control efforts modified in light of the information provided.

Spatio-Temporal Epidemiology of the Spread of African Swine Fever in Wild Boar and the Role of Environmental Factors in South Korea.

Since the first confirmation of African swine fever (ASF) in domestic pig farms in South Korea in September 2019, ASF continues to expand and most notifications have been reported in wild boar populations. In this study, we first performed a spatio-temporal cluster analysis to understand ASF spread in wild boar. Secondly, generalized linear logistic regression (GLLR) model analysis was performed to identify environmental factors contributing to cluster formation. In the meantime, the basic reproduction number (R0) for each cluster was estimated to understand the growth of the epidemic. The cluster analysis resulted in the detection of 17 spatio-temporal clusters. The GLLR model analysis identified factors influencing cluster formation and indicated the possibility of estimating ASF epidemic areas based on environmental conditions. In a scenario only considering direct transmission among wild boar, R0 ranged from 1.01 to 1.5 with an average of 1.10, while, in another scenario including indirect transmission via an infected carcass, R0 ranged from 1.03 to 4.38 with an average of 1.56. We identified factors influencing ASF expansion based on spatio-temporal clusters. The results obtained would be useful for selecting priority areas for ASF control and would greatly assist in identifying efficient vaccination areas in the future.

Modelling the field personnel resources to control foot-and-mouth disease outbreaks in New Zealand.

The objective of the study was to simulate New Zealand's foot-and-mouth disease (FMD) operational plan to determine personnel requirements for an FMD response and understand how the numbers of front-line staff available could affect the size and duration of FMD outbreaks, when using stamping-out (SO) measures with or without vaccination. The model utilized a national dataset of all known livestock farms. Each simulation randomly seeded infection into a single farm. Transmission mechanisms included direct and indirect contacts, local and airborne spread. Prior to each simulation, the numbers of personnel available for front-line tasks (including contact tracing, surveillance of at-risk farms, depopulation and vaccination) were set randomly. In a random subset of simulations, vaccination was allowed to be deployed as an adjunct to SO. The effects of personnel numbers on the size and duration of epidemics were explored using machine learning methods. In the second stage of the study, using a subset of iterations where numbers of personnel were unconstrained, the number of personnel used each day were quantified. When personnel resources were unconstrained, the 95th percentile and maximum number of infected places (IPs) were 78 and 462, respectively, and the 95th percentile and maximum duration were 69 and 217 days, respectively. However, severe constraints on personnel resources allowed some outbreaks to exceed the size of the UK 2001 FMD epidemic which had 2026 IPs. The number of veterinarians available had a major influence on the size and duration of outbreaks, whereas the availability of other personnel types did not. A shortage of veterinarians was associated with an increase in time to detect and depopulate IPs, allowing for continued transmission. Emergency vaccination placed a short-term demand for additional staff at the start of the vaccination programme, but the overall number of person days used was similar to SO-only strategies. This study determined the optimal numbers of front-line personnel required to implement the current operational plans to support an FMD response in New Zealand. A shortage of veterinarians was identified as the most influential factor to impact disease control outcomes. Emergency vaccination led to earlier control of FMD outbreaks but at the cost of a short-term spike in demand for personnel. In conclusion, a successful response needs to have access to sufficient personnel, particularly veterinarians, trained in response roles and available at short notice.

Calls grow for global avian flu jabs.

Amid an unprecedented epidemic of highly pathogenic avian influenza sweeping the Northern hemisphere, experts from around the world gathered in Paris for a two-day meeting to discuss whether - and how - to vaccinate poultry in Europe and North America. Josh Loeb was there to witness an extraordinary global gathering of animal health leaders.

Two major epidemics of highly pathogenic avian influenza virus H5N8 and H5N1 in domestic poultry in France, 2020-2022.

The spread of highly pathogenic avian influenza (HPAI) viruses worldwide has serious consequences for animal health and a major economic impact on the poultry production sector. Since 2014, Europe has been severely hit by several HPAI epidemics, with France being the most affected country. Most recently, France was again affected by two devastating HPAI epidemics in 2020-21 and 2021-22. We conducted a descriptive analysis of the 2020-21 and 2021-22 epidemics, as a first step towards identifying the poultry sector's remaining vulnerabilities regarding HPAI viruses in France. We examined the spatio-temporal distribution of outbreaks that occurred in France in 2020-21 and 2021-22, and we assessed the outbreaks' spatial distribution in relation to the 2016-17 epidemic and to the two 'high-risk zones' recently incorporated into French legislation to strengthen HPAI prevention and control. There were 468 reported outbreaks during the 2020-21 epidemic and 1375 outbreaks during the 2021-22 epidemic. In both epidemics, the outbreaks' distribution matched extremely well that of 2016-17, and most outbreaks (80.6% and 68.4%) were located in the two high-risk zones. The southwestern high-risk zone was affected in both epidemics, while the western high-risk zone was affected for the first time in 2021-22, explaining the extremely high number of outbreaks reported. As soon as the virus reached the high-risk zones, it started to spread between farms at very high rates, with each infected farm infecting between two and three other farms at the peaks of transmission. We showed that the spatial distribution model used to create the two high-risk zones was able to predict the location of outbreaks for the 2020-21 and 2021-22 epidemics. These zones were characterized by high poultry farm densities; future efforts should, therefore, focus on reducing the density of susceptible poultry in highly dense areas.

Multilayer network analysis of FMD transmission and containment among beef cattle farms.

As a highly contagious livestock viral disease, foot-and-mouth disease poses a great threat to the beef-cattle industry. Direct animal movement is always considered as a major route for between-farm transmission of FMD virus. Sharing contaminated equipment and vehicles have also attracted increasing interests as an indirect but considerable route for FMD virus transmission. With the rapid development of communication technologies, information-sharing techniques have been used to control epidemics. In this paper, we built farm-level time-series three-layer networks to simulate the between-farm FMD virus transmission in southwest Kansas by cattle movements (direct-contact layer) and truck visits (indirect-contact layer) and evaluate the impact of information-sharing techniques (information-sharing layer) on mitigating the epidemic. Here, the information-sharing network is defined as the structure that enables the quarantine of farms that are connected with infected farms. When a farm is infected, its infection status is shared with the neighboring farms in the information-sharing network, which in turn become quarantined. The results show that truck visits can enlarge the epidemic size and prolong the epidemic duration of the FMD outbreak by cattle movements, and that the information-sharing technique is able to mitigate the epidemic. The mitigation effect of the information-sharing network varies with the information-sharing network topology and different participation levels. In general, an increased participation leads to a decreased epidemic size and an increased quarantine size. We compared the mitigation performance of three different information-sharing networks (random network, contact-based network, and distance-based network) and found the outbreak on the network with contact-based information-sharing layer has the smallest epidemic size under almost any participation level and smallest quarantine size with high participation. Furthermore, we explored the potential economic loss from the infection and the quarantine. By varying the ratio of the average loss of quarantine to the loss of infection, we found high participation results in reduced economic losses under the realistic assumption that culling costs are much greater than quarantine costs.

Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey.

The structure of contact networks affects the likelihood of disease spread at the population scale and the risk of infection at any given node. Though this has been well characterized for both theoretical and empirical networks for the spread of epidemics on completely susceptible networks, the long-term impact of network structure on risk of infection with an endemic pathogen, where nodes can be infected more than once, has been less well characterized. Here, we analyze detailed records of the transportation of cattle among farms in Turkey to characterize the global and local attributes of the directed-weighted shipments network between 2007-2012. We then study the correlations between network properties and the likelihood of infection with, or exposure to, foot-and-mouth disease (FMD) over the same time period using recorded outbreaks. The shipments network shows a complex combination of features (local and global) that have not been previously reported in other networks of shipments; i.e. small-worldness, scale-freeness, modular structure, among others. We find that nodes that were either infected or at high risk of infection with FMD (within one link from an infected farm) had disproportionately higher degree, were more central (eigenvector centrality and coreness), and were more likely to be net recipients of shipments compared to those that were always more than 2 links away from an infected farm. High in-degree (i.e. many shipments received) was the best univariate predictor of infection. Low in-coreness (i.e. peripheral nodes) was the best univariate predictor of nodes always more than 2 links away from an infected farm. These results are robust across the three different serotypes of FMD observed in Turkey and during periods of low-endemic prevalence and high-prevalence outbreaks.

The African swine fever modelling challenge: Objectives, model description and synthetic data generation.

African swine fever (ASF) is an emerging disease currently spreading at the interface between wild boar and pig farms in Europe and Asia. Current disease control regulations, which involve massive culling with significant economic and animal welfare costs, need to be improved. Modelling enables relevant control measures to be explored, but conducting the exercise during an epidemic is extremely difficult. Modelling challenges enhance modellers' ability to timely advice policy makers, improve their readiness when facing emerging threats, and promote international collaborations. The ASF-Challenge, which ran between August 2020 and January 2021, was the first modelling challenge in animal health. In this paper, we describe the objectives and rules of the challenge. We then demonstrate the mechanistic multi-host model that was used to mimic as accurately as possible an ASF-like epidemic, provide a detailed explanation of the surveillance and intervention strategies that generated the synthetic data, and describe the different management strategies that were assessed by the competing modelling teams. We then outline the different technical steps of the challenge as well as its environment. Finally, we synthesize the lessons we learnt along the way to guide future modelling challenges in animal health.

Evaluation of the economic impact of classical and African swine fever epidemics using OutCosT, a new spreadsheet-based tool.

African swine fever (ASF) and classical swine fever (CSF) are two major transboundary animal diseases of swine with important socioeconomic consequences at farm, subnational and national level. The objective of this study was to evaluate the direct cost of outbreaks and their control at country/regional level in four countries: namely CSF in Colombia in 2015-2016, the retrospective cost of ASF in the Philippines in 2019 and in a province of Vietnam in 2020 and a hypothetical ASF scenario in one region in North Macedonia, using the newly developed Outbreak Costing Tool (OutCosT). The tool calculates the costs of 106 different items, broken down by up to four types of farms, and by who assumes the cost (whether veterinary services, farmers or other stakeholders). The total cost of CSF in Colombia was US$ 3.8 million, of which 88% represented the cost of the vaccination campaign. For ASF, there were wide differences between countries: US$ 8,26,911 in Lao Cai (Vietnam), US$ 33,19,666 in North Macedonia and over US$ 58 million in the Philippines. While in the Philippines and Vietnam, 96-98% of the cost occurred in the affected farms, the highest expenditure in North Macedonia scenario was the movement control of the neighbouring and at-risk farms (77%). These important differences between countries depend on the spread of the disease, but also on the production systems affected and the measures applied. Apart from the financial cost, these diseases have other negative impacts, especially in the livelihoods of smallholder farms. The OutCosT tool also allows users to evaluate qualitatively other important aspects related to the epidemics, such as the impact on human health, the environment, animal welfare, socioeconomic vulnerability, trading and political response. OutCosT, which is a FAO corporate tool (available online at: https://www.fao.org/fileadmin/user_upload/faoweb/animal-health/OutCosT_PIG.xlsx), can be an important tool to support country authorities to rapidly respond to a swine disease outbreak by estimating the associated costs and for advocacy purposes to mobilize resources at national or international levels.

Network analysis of Standardbred horse movements between racetracks in Canada and the United States in 2019: Implications for disease spread and control.

Horse movements are one of the most important factors for the spread of equine diseases, and past epidemics indicate that contact networks play an important role. Network analysis was used to describe the spatial and temporal characteristics of horse movements between Standardbred racetracks in Canada and the United States during 2019, and to characterize the network to provide a better understanding of the potential racetrack-to-racetrack spread of infectious disease within the network. Networks were constructed and analyzed as an overall network (the entire study period) and monthly networks. There were 254 active Standardbred racetracks in 2019, organized in 24 geographically clustered communities. Movements and subsequent network measures of cohesiveness and centrality exhibited strong seasonal variation. Networks were more highly connected during the summer and early autumn, coinciding with peak racing activities. Monthly networks showed evidence of small-world properties, whereby disease introduction into a racetrack within a local cluster could result in the rapid spread to other racetracks within that cluster, and to other topologically distant clusters through few additional movements. Using centrality measures, a small subset of racetracks were identified as highly influential in the network and could be considered high-risk for disease introduction and disease spread to other racetracks. Enhancement of disease prevention strategies might be most appropriate if targeted to the months associated with peak racing season, and particularly to influential racetracks. The networks produced in this study were not a true representation of the entire contact network as the information contained within the race records only allowed for the consideration of between-racetrack movements. Other non-recorded movements represent further contacts in the network that can have a substantial effect on the spread of disease within a network, and the exclusion of this information can result in incorrect network measure estimates. While likely not an easy task, given the initial findings of this study and experiences from past horse industry infectious disease outbreaks, it could be beneficial for the Standardbred industry to put a movement recording strategy in place. One benefit would be enhanced ability to respond rapidly and efficiently in the event of an outbreak, thereby limiting potential animal health and economic impacts. Additional movement data could also enable further characterization of the network to inform optimal disease prevention and control strategies.

Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions.

Infectious diseases in livestock are well-known to infect multiple hosts and persist through a combination of within- and between-host transmission pathways. Uncertainty remains about the epidemic dynamics of diseases being introduced on farms with more than one susceptible host species. Here, we describe multi-host contact networks and elucidate the potential of disease spread through farms with multiple hosts. Four years of between-farm animal movement among all farms of a Brazilian state were described through a static and monthly snapshot of network representations. We developed a stochastic multilevel model to simulate scenarios in which infection was seeded into single host and multi-host farms to quantify disease spread potential, and simulate network-based control actions used to evaluate the reduction of secondarily infected farms. We showed that the swine network was more connected than cattle and small ruminants in both the static and monthly snapshots. The small ruminant network was highly fragmented, however, contributed to interconnecting farms, with other hosts acting as intermediaries throughout the networks. When a single host was initially infected, secondary infections were observed across farms with all other species. Our stochastic multi-host model demonstrated that targeting the top 3.25% of the farms ranked by degree reduced the number of secondarily infected farms. The results of the simulation highlight the importance of considering multi-host dynamics and contact networks while designing surveillance and preparedness control strategies against pathogens known to infect multiple species.

The impact of the epidemic experience on the recovery of production of pig farmers after the outbreak-Evidence from the impact of African swine fever (ASF) in Chinese pig farming.

The African swine fever (ASF) has triggered considerable shocks to the pig farming industry, which has become a significant animal disease epidemic. The study explores the effect of epidemic experience on post-outbreak production recovery from resilience and risk perception based on 340 micro-survey data from Sichuan, Henan, and Shandong provinces. Epidemic experience has been shown to impact the degree of post-outbreak production recovery positively, and farmers who have endured epidemics are more likely to recover their production after outbreaks. The mechanistic study indicates that past epidemics in African swine fever shocks can effectively improve farmers' cognitive resilience and management capability, enhance recovery, and reduce risk perception in the aftermath of production recovery. In order to alleviate the endogenous problems caused by selection bias, missing variables, and two-way causality. This paper uses factor analysis to comprehensively measure production recovery capacity and production risk perception, and uses propensity score matching(PSM), instrumental variable method and replacement measurement methods to conduct robustness tests, and find the conclusions are still robust. The empirical analysis shows that the experience of the epidemic will promote the recovery of farmers after the outbreak; the experience of the epidemic will significantly impact the recovery of production after the outbreak for both free-range and professional farmers.

Modelling the role of mortality-based response triggers on the effectiveness of African swine fever control strategies.

African swine fever (ASF) is considered the most impactful transboundary swine disease. In the absence of effective vaccines, control strategies are heavily dependent on mass depopulation and shipment restrictions. Here, we developed a nested multiscale model for the transmission of ASF, combining a spatially explicit network model of animal shipments with a deterministic compartmental model for the dynamics of two ASF strains within 3 km × 3 km pixels in one Brazilian state. The model outcomes are epidemic duration, number of secondary infected farms and pigs, and distance of ASF spread. The model also shows the spatial distribution of ASF epidemics. We analyzed quarantine-based control interventions in the context of mortality trigger thresholds for the deployment of control strategies. The mean epidemic duration of a moderately virulent strain was 11.2 days, assuming the first infection is detected (best-case scenario), and 15.9 days when detection is triggered at 10% mortality. For a highly virulent strain, the epidemic duration was 6.5 days and 13.1 days, respectively. The distance from the source to infected locations and the spatial distribution was not dependent on strain virulence. Under the best-case scenario, we projected an average number of infected farms of 23.77 farms and 18.8 farms for the moderate and highly virulent strains, respectively. At 10% mortality-trigger, the predicted number of infected farms was on average 46.27 farms and 42.96 farms, respectively. We also demonstrated that the establishment of ring quarantine zones regardless of size (i.e. 5 km, 15 km) was outperformed by backward animal movement tracking. The proposed modelling framework provides an evaluation of ASF epidemic potential, providing a ranking of quarantine-based control strategies that could assist animal health authorities in planning the national preparedness and response plan.

Spatial and temporal patterns of white spot disease in Rayong Province, Thailand, from october 2015 to september 2018.

This study aimed to analyze the spatial and temporal patterns of white spot disease (WSD) in shrimp farms in Rayong Province, Thailand, between October 2015 and September 2018. The longitudinal data of all active shrimp farms were collected and categorized into two groups: cases and non-cases. A space-time permutation scan, epidemic curve, and time-series analysis were used to evaluate the spatiotemporal patterns. We assessed a total of 1126 ponds across 176 farms in two districts (Klaeng and Mueang Rayong) and identified three significant (P < 0.05) clusters of WSD cases. The biggest cluster encompassed 21 geographical coordinates. This cluster had a radius of 1.14 km and occurred between January 31, 2017, and February 28, 2017. The epidemic curve showed that the biggest outbreak peaked from December 2017 to February 2018. In the time-series analysis, the highest probability of actual WSD cases was at the beginning of each calendar year, consistent with the prominent high probability recorded in WSD forecasts. Our analysis presents the interaction between hotspot areas and time period. These results should help the relevant authorities implement appropriate surveillance programs and control measures to limit the occurrence and transmission of WSD.

Thoughts on African Swine Fever Vaccines.

African swine fever (ASF) is an acute viral hemorrhagic disease of domestic swine with mortality rates approaching 100%. Devastating ASF outbreaks and continuing epidemics starting in the Caucasus region and now in the Russian Federation, Europe, China, and other parts of Southeast Asia (2007 to date) highlight its significance. ASF strain Georgia-07 and its derivatives are now endemic in extensive regions of Europe and Asia and are "out of Africa" forever, a situation that poses a grave if not an existential threat to the swine industry worldwide. While our current concern is Georgia-07, other emerging ASFV strains will threaten for the indefinite future. Economic analysis indicates that an ASF outbreak in the U.S. would result in approximately $15 billion USD in losses, assuming the disease is rapidly controlled and the U.S. is able to reenter export markets within two years. ASF's potential to spread and become endemic in new regions, its rapid and efficient transmission among pigs, and the relative stability of the causative agent ASF virus (ASFV) in the environment all provide significant challenges for disease control. Effective and robust methods, including vaccines for ASF response and recovery, are needed immediately.