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Feral pigs threaten biodiversity in 54 countries and cause an estimated $120 billion in damages annually in the USA. They endanger over 600 native species and have driven 14 to extinction. Additionally, they pose a significant zoonotic disease risk, carrying pathogens such as Brucella, leptospirosis, and Japanese encephalitis. Understanding and controlling disease spread relies on models of social dynamics, but these vary widely across regions, limiting the transferability of findings from the USA and Europe to other locations like Australia. This study addresses this gap by analysing the social interactions of 146 GPS-tracked feral pigs in Australia using a proximity-based social network approach. Findings reveal that females exhibit stronger group cohesion, while males act as key connectors between groups. Contact rates are high within groups, facilitating rapid intra-group disease spread, whereas inter-group transmission is slower. Seasonal variations further impact dynamics, with increased contact in summer. These insights suggest that targeting adult males in control programs could help limit disease outbreaks. Given the rising economic and public health concerns associated with animal diseases, the study highlights the need for localized strategies based on feral pig social behaviour to enhance global control efforts. Sometimes pigs and other domestic animals escape from farms and live independently in a similar way to their wild ancestors. Such animals and their offspring are referred to as “feral”. In Australia, USA and many other countries across the world, feral pigs damage ecosystems and farmland, and carry Japanese encephalitis and other diseases that can spread to humans, livestock, and wildlife. To understand and control these threats, researchers study how feral pigs move around and interact with each other, which is referred to as \"social dynamics\". However, previous studies have focused on groups of feral pigs living in Europe and may not be applicable to pigs living in Australia and other parts of the world. This lack of local data on social dynamics has made it difficult to optimise models of how diseases spread amongst feral pigs in certain areas. Optimising such models is crucial for enabling government agencies to prepare and respond to potential disease outbreaks in humans and other animals. Proboste et al. analysed tracking data collected over a 7-year period from satellite transmitters attached to 146 feral pigs in eastern Australia. The analysis assessed how often the animals came close to each other and how this might influence the spread of diseases, revealing that females tended to remain within the same groups of pigs, while males were more likely to move between groups. The levels of contact between individual pigs varied across the year, with more contact during the summer months. These findings show that male pigs play a key role in connecting different groups of feral pigs and may therefore be important targets for managing diseases in these animals. The findings also highlight that contact varies across the year, which might help pinpoint the best time to carry out management practices. In the future, this will help government agencies develop more effective strategies for managing feral pigs in Australia and other areas where their presence poses a risk to humans, livestock and native wildlife.

Tick paralysis resulting from bites from Ixodes holocyclus and I . cornuatus is one of the leading causes of emergency veterinary admissions for companion animals in Australia, often resulting in death if left untreated. Availability of timely information on periods of increased risk can help modulate behaviors that reduce exposures to ticks and improve awareness of owners for the need of lifesaving preventative ectoparasite treatment. Improved awareness of clinicians and pet owners about temporal changes in tick paralysis risk can be assisted by ecological forecasting frameworks that integrate environmental information into statistical time series models. Using an 11-year time series of tick paralysis cases from veterinary clinics in one of Australia’s hotspots for the paralysis tick Ixodes holocyclus , we asked whether an ensemble model could accurately forecast clinical caseloads over near-term horizons. We fit a series of statistical time series (ARIMA, GARCH) and generative models (Prophet, Generalised Additive Model) using environmental variables as predictors, and then combined forecasts into a weighted ensemble to minimise prediction interval error. Our results indicate that variables related to temperature anomalies, levels of vegetation moisture and the Southern Oscillation Index can be useful for predicting tick paralysis admissions. Our model forecasted tick paralysis cases with exceptional accuracy while preserving epidemiological interpretability, outperforming a field-leading benchmark Exponential Smoothing model by reducing both point and prediction interval errors. Using online particle filtering to assimilate new observations and adjust forecast distributions when new data became available, our model adapted to changing temporal conditions and provided further reduced forecast errors. We expect our model pipeline to act as a platform for developing early warning systems that can notify clinicians and pet owners about heightened risks of environmentally driven veterinary conditions.

A school-based preventive chemotherapy (PC) program has operated since 2013 for soil-transmitted helminths (STHs) and 2014 for schistosomiasis in Huambo, Uige and Zaire provinces, Angola. This program was informed by a prevalence survey in 2014 and evaluated in 2021, demonstrating limited impact in reducing provincial-level prevalence. This geospatial analysis aims to provide granular estimates of the geographic distribution of schistosomiasis and STHs to target control strategies. Parasitological data on schistosomiasis and STHs were obtained from school-based prevalence surveys conducted in 2014 and 2021. These data were supplemented with open access environmental and climatic data to develop risk prediction maps for each parasite at each time point. Variables for the final risk prediction models were selected through non-spatial multivariable regression analyses and residual spatial autocorrelation was investigated using semivariograms. Risk prediction maps were then developed using either non-spatial or spatial (using the Matérn covariance) geostatistical models depending on the presence of residual spatial autocorrelation. The 2014 survey included 17,093 schoolchildren (575 schools) for schistosomiasis and 3,649 schoolchildren (121 schools) for STHs, and the 2021 survey included 17,880 schoolchildren (599 schools) for schistosomiasis and 6,461 schoolchildren (214 schools) for STHs. Our analyses indicated that in Huambo province, there are small areas of high schistosomiasis risk in the north and south, and a centrally located STH hotspot, with no discernible change in predicted risk for either infection over time. In Uige, there has been a reduction in schistosomiasis hotspots in the southwest corner but an overall increase in predicted risk throughout the province, whilst there is a concerning trend for expanding areas of high predicted STH risk throughout. In Zaire, there are increasing areas of higher risk for schistosomiasis and STHs, with co-endemic hotspots. These risk prediction maps importantly identify higher risk areas for schistosomiasis and STHs within large geographic regions that should be prioritised for control with tailored decisions for future PC delivery.

While zoonotic diseases are defined by transmission processes between animals and humans, for many of these diseases the presence of a contaminated environmental source is the cause of transmission. Most zoonoses depend on complex environmentally driven interactions between humans and animals, which occur along an occupational and recreational environmental continuum, including farming and animal marketing systems, environmental management systems, and community leisure environments. Environmentally driven zoonoses (EDZs) are particularly challenging to diagnose and control as their reservoirs are in the natural environment and thus often escape conventional surveillance systems that rely on host monitoring. Changes in the environment as a result of climate change [1], human population density [2], and intensification of agriculture [3] have been linked to increasing transmission events for this group of infections. As such, there is a recognised need to be able to detect the presence of EDZs in the environment as a means to better anticipate transmission events and improve source attribution investigations. Finally, the recognition that a One Health approach is needed to combat these infections is signalling to governments the need to develop policy that optimises trade-offs across human, animal, and environmental health sectors. In this review, we discuss and critically appraise the main challenges relating to the epidemiology, diagnosis, and control of environmental zoonotic disease. Using a set of exemplar diseases, including avian influenza and antimicrobial resistant pathogens, we explore the epidemiological contexts (risk factors) within which these infections not only impact human health but also contribute to animal health and environmental impacts. We then critically appraise the surveillance challenges of monitoring these infections in the environment and examine the policy trade-offs for a more integrated approach to mitigating their impacts.

Background/Objectives: Q-VAX vaccine, approved in Australia, prevents Q fever. However, individuals with prior Coxiella burnetii (Cb) infection have an increased risk of adverse reactions, requiring pre-vaccination screening by an intradermal hypersensitivity skin test for cell-mediated immune memory and a serological assay for anti-Cb antibodies. The week-long interval for skin test assessment limits efficient vaccination. This study evaluated a standardized interferon-γ release assay (IGRA) as a potential skin test alternative. Methods: Immune assays were compared in Australian populations with different incidences of prior Cb exposure. Cell-mediated immunity was assessed by the Q-VAX skin test and IGRA. Serological status was evaluated with established diagnostic assays. Hypothetical vaccine eligibility decisions using combined IGRA and serology results were compared with actual clinical decisions made using current guidelines. Results: All tests performed better in detecting prior infection than in detecting prior vaccination. Only the IGRA identified all individuals with a known history of Q fever. Agreement between the skin test and IGRA was limited. Moderate agreement was observed between hypothetical vaccine eligibility determinations based on IGRA plus serology results and actual clinical decisions. IGRA-positive but serology- and skin test-negative individuals received Q-VAX without clinically significant side effects, suggesting that elevated IGRA responses alone are not predictive of susceptibility to vaccine reactogenicity. Conclusions: The IGRA is not yet a suitable skin test replacement when assessing eligibility for Q fever vaccination, despite the significant limitations of the latter. We offer recommendations for designing future studies that might allow the development of appropriate guidelines for IGRA use in vaccine eligibility screening.

Q fever is a zoonotic disease that is caused by Coxiella burnetii and leads to abortion and infertility in ruminants and debilitating disease in humans. Jimma zone, including Jimma town, located in the Oromia region of Ethiopia, was affected by an outbreak of abortions in ruminants related to Q fever infection between 2013 and 2015. This study aimed to investigate the geo-clustering of C. burnetii seroprevalence in dairy farms of Jimma town and identify the environmental risk factors associated with seroprevalence distribution. A total of 227 cattle were tested for antibodies against C. burnetii in 25 farms. We explored the clustering of C. burnetii seroprevalence using semivariograms. A geostatistical regression-based model was implemented to quantify the risk factors and to predict the geographical variation in C. burnetii seroprevalence at unsampled locations in Jimma town using OpenBugs. Our results demonstrated that the risk of exposure in dairy cattle varied across the landscape of Jimma town and was associated with environmental risk factors. The predictive map of C. burnetii seroprevalence showed that communities in the eastern part of Jimma town had the highest risk of exposure. Our results can inform community-level investigations of human seroprevalence in the high-risk areas to the east of Jimma.

Q fever, caused by the bacterium Coxiella burnetii, is an important zoonotic disease worldwide. Australia has one of the highest reported incidences and seroprevalence of Q fever, and communities in the state of Queensland are at highest risk of exposure. Despite Australia’s Q fever vaccination programs, the number of reported Q fever cases has remained stable for the last few years. The extent to which Q fever notifications cluster in circumscribed communities is not well understood. This study aimed to retrospectively explore and identify the spatiotemporal variation in Q fever household and community clusters in Queensland reported during 2002 to 2017, and quantify potential within cluster drivers. We used Q fever notification data held in the Queensland Notifiable Conditions System to explore the geographical clustering patterns of Q fever incidence, and identified and estimated community Q fever spatiotemporal clusters using SatScan, Boston, MA, USA. The association between Q fever household and community clusters, and demographic and socioeconomic characteristics was explored using the chi-squared statistical test and logistic regression analysis. From the total 2175 Q fever notifications included in our analysis, we found 356 Q fever hotspots at a mesh-block level. We identified that 8.2% of Q fever notifications belonged to a spatiotemporal cluster. Within the spatiotemporal Q fever clusters, we found 44 (61%) representing household clusters and 20 (27.8%) were statistically significant with an average cluster size of 3 km radius. Our multivariable model shows statistical differences between cases belonging to clusters in comparison with cases outside clusters based on the type of reported exposure. In conclusion, our results demonstrate that clusters of Q fever notifications are temporally stable and geographically circumscribed, indicating a persistent common exposure. Furthermore, within individuals in household and community clusters, abattoir exposure (a traditional occupational exposure) was rarely reported by individuals.

Background In rural parts of Africa, dogs live in close association with humans and livestock, roam freely, and usually do not receive prophylactic measures. Thus, they are a source of infectious disease for humans and for wildlife such as protected carnivores. In 2011, an epidemiological study was carried out around three conservation areas in Uganda to detect the presence and determine the prevalence of vector-borne pathogens in rural dogs and associated ticks to evaluate the risk that these pathogens pose to humans and wildlife. Methods Serum samples ( n  = 105), blood smears ( n  = 43) and blood preserved on FTA cards ( n  = 38) and ticks (58 monospecific pools of Haemaphysalis leachi and Rhipicephalus praetextatus including 312 ticks from 52 dogs) were collected from dogs. Dog sera were tested by indirect immunofluorescence to detect the presence of antibodies against Rickettsia conorii and Ehrlichia canis . Antibodies against R. conorii were also examined by indirect enzyme immunoassay. Real time PCR for the detection of Rickettsia spp., Anaplasmataceae, Bartonella spp. and Babesia spp. was performed in DNA extracted from FTA cards and ticks. Results 99 % of the dogs were seropositive to Rickettsia spp. and 29.5 % to Ehrlichia spp. Molecular analyses revealed that 7.8 % of the blood samples were infected with Babesia rossi , and all were negative for Rickettsia spp. and Ehrlichia spp. Ticks were infected with Rickettsia sp. (18.9 %), including R. conorii and R. massiliae ; Ehrlichia sp. (18.9 %), including E. chaffeensis and Anaplasma platys ; and B. rossi (1.7 %). Bartonella spp. was not detected in any of the blood or tick samples. Conclusions This study confirms the presence of previously undetected vector-borne pathogens of humans and animals in East Africa. We recommend that dog owners in rural Uganda be advised to protect their animals against ectoparasites to prevent the transmission of pathogens to humans and wildlife.

Feral pigs threaten biodiversity in 54 countries and cause an estimated $120 billion in damages annually in the USA. They endanger over 600 native species and have driven 14 to extinction. Additionally, they pose a significant zoonotic disease risk, carrying pathogens such as Brucella, leptospirosis, and Japanese encephalitis. Understanding and controlling disease spread relies on models of social dynamics, but these vary widely across regions, limiting the transferability of findings from the USA and Europe to other locations like Australia. This study addresses this gap by analysing the social interactions of 146 GPS-tracked feral pigs in Australia using a proximity-based social network approach. Findings reveal that females exhibit stronger group cohesion, while males act as key connectors between groups. Contact rates are high within groups, facilitating rapid intra-group disease spread, whereas inter-group transmission is slower. Seasonal variations further impact dynamics, with increased contact in summer. These insights suggest that targeting adult males in control programs could help limit disease outbreaks. Given the rising economic and public health concerns associated with animal diseases, the study highlights the need for localized strategies based on feral pig social behaviour to enhance global control efforts. Sometimes pigs and other domestic animals escape from farms and live independently in a similar way to their wild ancestors. Such animals and their offspring are referred to as “feral”. In Australia, USA and many other countries across the world, feral pigs damage ecosystems and farmland, and carry Japanese encephalitis and other diseases that can spread to humans, livestock, and wildlife. To understand and control these threats, researchers study how feral pigs move around and interact with each other, which is referred to as \"social dynamics\". However, previous studies have focused on groups of feral pigs living in Europe and may not be applicable to pigs living in Australia and other parts of the world. This lack of local data on social dynamics has made it difficult to optimise models of how diseases spread amongst feral pigs in certain areas. Optimising such models is crucial for enabling government agencies to prepare and respond to potential disease outbreaks in humans and other animals. Proboste et al. analysed tracking data collected over a 7-year period from satellite transmitters attached to 146 feral pigs in eastern Australia. The analysis assessed how often the animals came close to each other and how this might influence the spread of diseases, revealing that females tended to remain within the same groups of pigs, while males were more likely to move between groups. The levels of contact between individual pigs varied across the year, with more contact during the summer months. These findings show that male pigs play a key role in connecting different groups of feral pigs and may therefore be important targets for managing diseases in these animals. The findings also highlight that contact varies across the year, which might help pinpoint the best time to carry out management practices. In the future, this will help government agencies develop more effective strategies for managing feral pigs in Australia and other areas where their presence poses a risk to humans, livestock and native wildlife.

Feral pigs threaten biodiversity in 54 countries and cause an estimated $120 billion in damages annually in the USA. They endanger over 600 native species and have driven 14 to extinction. Additionally, they pose a significant zoonotic disease risk, carrying pathogens such as Brucella, leptospirosis, and Japanese encephalitis. Understanding and controlling disease spread relies on models of social dynamics, but these vary widely across regions, limiting the transferability of findings from the USA and Europe to other locations like Australia. This study addresses this gap by analysing the social interactions of 146 GPS-tracked feral pigs in Australia using a proximity-based social network approach. Findings reveal that females exhibit stronger group cohesion, while males act as key connectors between groups. Contact rates are high within groups, facilitating rapid intra-group disease spread, whereas inter-group transmission is slower. Seasonal variations further impact dynamics, with increased contact in summer. These insights suggest that targeting adult males in control programs could help limit disease outbreaks. Given the rising economic and public health concerns associated with animal diseases, the study highlights the need for localized strategies based on feral pig social behaviour to enhance global control efforts.