BACKGROUND:Dengue fever remains a critical public health challenge in Thailand, with transmission dynamics driven by complex interactions between environmental and socioeconomic factors. Understanding these predictive factors is essential for developing robust forecasting systems. METHODS:We developed a machine learning framework to classify spatiotemporal dengue risk and identify key predictive factors across Thailand. We analyzed 20 years of monthly dengue hemorrhagic fever surveillance data (2003-2022) from 77 provinces, integrating 54 environmental, climatic, and socioeconomic features. We benchmarked four candidate classifiers - logistic regression, support vector machines, random forests, and eXtreme Gradient Boosting (XGBoost) - and selected XGBoost on the basis of performance across six metrics. SHapley Additive exPlanations (SHAP) were used to interpret feature contributions. The dataset was stratified into training (2003-2016) and testing periods, with the latter subdivided into pre-COVID-19 (2017-2019), COVID-19 (2020-2021), and post-COVID-19 (2022) phases. RESULTS:The XGBoost model achieved an AUC of 0.80 in pre-pandemic testing and 0.74 across the combined during- and post-pandemic period. Temperature dominated the feature-importance ranking, comprising seven of the top ten features, with non-linear thresholds near 21°C for 1-month lagged minimum temperature and near 32°C for 3-month lagged maximum temperature - values that align with established biological constraints on Aedes aegypti-mediated transmission. Precipitation features contributed minimally to model predictions, while a higher Gross Provincial Product was associated with increased dengue risk, consistent with predominantly urban transmission patterns. Model performance deteriorated significantly during the COVID-19 pandemic (AUC = 0.62 in 2021), with systematic overprediction indicating that non-environmental factors operating outside the model dominated dengue dynamics during this period. CONCLUSIONS:Temperature is the dominant predictor of dengue risk in Thailand, and the thresholds we recover correspond closely to known biological constraints on vector competence. Environmentally driven prediction is reliable under stationary conditions but degrades substantially during periods of major societal disruption, underscoring the need to integrate behavioral and surveillance-coverage indicators alongside environmental predictors when applying such models in real time.

Abstract Background Dengue remains a major public health challenge in Thailand despite decades of vector control implementation. While mathematical models have explored dengue transmission dynamics, systematic evaluation of current control strategies under realistic operational conditions remains limited. Methods We developed a temperature-dependent, multi-serotype dengue transmission model that explicitly incorporates three primary vector control strategies: reduction in mosquito biting rates through personal protection measures, further reduction in mosquito birth rates beyond current larval control efforts, and further increase in adult mosquito mortality beyond current adulticide application levels. Using Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC), we fitted the model to dengue hemorrhagic fever (DHF) surveillance data from nine province-year combinations representing high (Rayong), moderate (Ratchaburi), and low (Phrae) transmission settings across three years (2006, 2015, and 2017). The model accounts for four dengue serotypes, temperature-dependent mosquito dynamics, and temporary cross-protective immunity between serotypes. Results The model closely reproduced observed monthly DHF case counts across all nine province-year combinations. Estimated reporting proportions ranged from 1.4% to 16.7%, with the highest values occurring in high-transmission provinces during the 2015 outbreak year. When each strategy was independently intensified by 50% relative to fitted baseline levels, reducing mosquito biting rates and increasing adult mosquito mortality consistently produced greater reductions in transmission than reducing mosquito birth rates. In the highest-transmission scenario (Rayong, 2015), a 50% reduction in biting rate from the baseline level yielded a 96.4% reduction in cumulative infections (95% CrI: 95.4-97.3%), compared with 94.3% (95% CrI: 91.8-95.6%) for a 50% increase in adult mosquito mortality and 77.0% (95% CrI: 58.6-84.6%) for a 50% reduction in mosquito birth rate. Analysis of the time-varying reproduction number ( R t ) confirmed that interventions targeting adult mosquito-human contact achieved the greatest sustained epidemic suppression, although the relative ranking between bite prevention and adulticide application varied by epidemiological setting. Conclusions Under the uniform 50% intensification scenario tested, interventions that directly disrupt adult mosquito-human contact, whether through personal protection or adulticide application, substantially outperformed larval control in reducing dengue transmission across diverse Thai settings. These findings support prioritizing personal protection and adulticide application, while the generalizability of this ranking to other intensification levels and settings warrants further investigation.

Understanding the role of human mobility in disease transmission is crucial for effective intervention. Analyzing mobility patterns can identify routes of transmission and highlight vulnerable populations. While tools exist for mobility analysis and disease simulation, none integrates all the needed capabilities into a single platform. Such integration is essential for seamless exploration of key questions and what-if scenarios. The MoVe (Mobility analysis for Vector-borne disease) platform meets this need by combining mobility analysis with agent-based simulation. It enables exploratory data analysis, generates mobility metrics, identifies stop locations, visualizes data, and allows users to run simulations based on mobility and risk factors. To demonstrate MoVe’s effectiveness, the platform was used in a case study of malaria transmission along the Thai-Myanmar border, where importation of infection complicates elimination efforts in Thailand. The spatial analysis shows distinct mobility patterns into high-risk areas among different occupational groups. By removing different types of cross-border mobility from the simulation, we quantify the impact of Thai and Myanmar migration on malaria infection rates in Thailand. These findings highlight the tool’s potential for understanding vector-borne disease transmission dynamics and its applicability to other regions and diseases.

Dengue fever remains a critical public health challenge in Thailand, with transmission dynamics driven by complex interactions between environmental and socioeconomic factors. Understanding these drivers is essential for developing robust prediction systems. We developed a machine learning framework to classify spatiotemporal dengue risk and identify key drivers of transmission across Thailand. We analyzed 20 years of monthly dengue hemorrhagic fever surveillance data (2003-2022) from 77 provinces, integrating 54 environmental, climatic, and socioeconomic variables. Using eXtreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP), we classified provinces as high-risk or low-risk based on the national median incidence. The dataset was stratified into training (2003-2016) and testing periods, with the latter subdivided into pre-COVID-19 (2017-2019), COVID-19 (2020-2021), and post-COVID-19 (2022) phases. The model achieved robust performance with an area under the curve (AUC) of 0.94 during training and 0.80 in pre-pandemic testing. Temperature emerged as the dominant predictor, with temperature-related variables comprising seven of the ten most influential features. Critical transmission thresholds were identified at approximately 21°C for a 1-month lagged minimum temperature and approximately 32°C for a 3-month lagged maximum temperature. Interestingly, precipitation contributed minimally to model predictions, while a higher Gross Provincial Product was associated with an increased risk of dengue, reflecting urban transmission patterns. Model performance deteriorated significantly during the COVID-19 pandemic (AUC = 0.62 in 2021), with systematic overprediction suggesting that behavioral factors outweighed environmental drivers during the pandemic disruption. Temperature, particularly with lags of 1-3 months, is the primary predictor of dengue risk in Thailand. The pandemic-induced disruption of model accuracy underscores the crucial role of human behavioral factors in influencing dengue transmission dynamics. Our results challenge traditional precipitation-focused models and highlight the importance of temperature-driven approaches for dengue prediction in Thailand. This study used machine learning to predict dengue fever outbreaks across Thailand’s provinces from 2003 to 2022. By analyzing climate data, economic indicators, and satellite imagery, our machine learning model could accurately identify high-risk areas about 80% of the time. We found that temperature is the most important factor determining where and when dengue will spread. Additionally, we identified a critical temperature threshold where dengue transmission essentially stops when minimum temperatures drop below approximately 21°C. Surprisingly, rainfall patterns, which are often emphasized in dengue predictions, played a much smaller role than expected. Higher economic development was also found to be associated with an increased risk of dengue, probably due to urbanization creating ideal conditions for mosquitoes to breed all year round and higher human population density that facilitates virus transmission. However, the COVID-19 pandemic disrupted the model’s accuracy, causing it to predict more dengue cases than actually occurred from 2020 to 2022, likely due to the COVID-19 control measures that climate data alone could not capture. This research demonstrates both the power and limitations of using environmental and socioeconomic data to predict dengue outbreaks in Thailand.

Populations are exposed daily to numerous environmental pollutants, particularly through food. To address environmental issues, many agricultural production methods have been developed, including organic farming. To date, there is no exhaustive inventory of the contamination of organic foods as there is for conventional foods. The main objective of this work was to construct a growing and updatable database on chemical substances and their levels in organic foods consumed in Europe. To this end, a literature search was conducted, resulting in a total of 1207 concentration values from 823 food–substances pairs involving 166 food matrices and 209 chemical substances, among which 95% were not authorized in organic farming and 80% were pesticides. The most encountered substance groups are “inorganic contaminants” and “organophosphate”, and the most studied food groups are “fruit used as fruit” and “Cereals and cereal primary derivatives”. Further studies are needed to continue updating the database with robust and comprehensive data on organic food contamination. This database could be used to study the health risks associated with these contaminants.

Air pollution is a major cause of mortality and chronic diseases worldwide, with particulate matter (PM) being the primary contributor to this burden. Few studies have yet been carried out on the effect of modes of transport, times of day or travel locations on the exposure of individuals to PM. We conducted an experiment in Grenoble, France, in spring 2022 to study factors determining the individual exposure to PM. Using low-cost sensors (LCS), PM1 (PM < 1 mu m) and PM2.5 (PM < 2.5 mu m) levels were measured in four transport modes (bike, walk, bus, and tramway), across four different streets and at three different times of the day. Findings are: (i) - the mode of transport plays a more important role on levels of PMratios (PM concentration ratio to reference) than the time of the day or the location, (ii) - PMratios and inhalation doses are higher in active modes of transport (bike, walk) than in passive ones (bus, tram), (iii) - levels of exposure to PM are ranked as: tram < bus < walk < bike, and (iv) - a statistical model has been developed to predict PMratios as a function of transport mode, travel period, street ratio, and traffic. Exposures to PM in trams are found 12%-25% and 13%-20% lower than in passive modes of transport for PM1 and PM2.5, respectively. Wearing LCSs makes it possible to estimate commuters' exposure to PM and their use should be encouraged for prevention purposes.

Selecting appropriate diagnostic methods that take account of the type of vaccine used is important when implementing a vaccination programme against highly pathogenic avian influenza (HPAI). If vaccination is effective, a decreased viral load is expected in the samples used for diagnosis, making molecular methods with high sensitivity the best choice. Although serological methods can be reasonably sensitive, they may produce results that are difficult to interpret. In addition to routine molecular monitoring, it is recommended to conduct viral isolation, genetic sequencing and phenotypic characterisation of any HPAI virus detected in vaccinated flocks to detect escape mutants early. Following emergency vaccination, various surveillance options based on virological testing of dead birds ('bucket sampling') at defined intervals were assessed to be effective for early detection of HPAIV and prove disease freedom in vaccinated populations. For ducks, virological or serological testing of live birds was assessed as an effective strategy. This surveillance could be also applied in the peri-vaccination zone on vaccinated establishments, while maintaining passive surveillance in unvaccinated chicken layers and turkeys, and weekly bucket sampling in unvaccinated ducks. To demonstrate disease freedom with > 99% confidence and to detect HPAI virus sufficiently early following preventive vaccination, monthly virological testing of all dead birds up to 15 per flock, coupled with passive surveillance in both vaccinated and unvaccinated flocks, is recommended. Reducing the sampling intervals increases the sensitivity of early detection up to 100%. To enable the safe movement of vaccinated poultry during emergency vaccination, laboratory examinations in the 72 h prior to the movement can be considered as a risk mitigation measure, in addition to clinical inspection; sampling results from existing surveillance activities carried out in these 72 h could be used. In this Opinion, several schemes are recommended to enable the safe movement of vaccinated poultry following preventive vaccination.

Risk assessment provides decision makers an objective, repeatable and documented assessment of the risks posed by a particular action. There exist well established protocols created by the World Organization for Animal Health (WOAH) and Codex Alimentarius for assessing risk. Risk assessment is the first component of the risk analysis process, which also includes risk management and risk communication. This article is a brief introduction to the risk assessment approach in the animal welfare domain, which is based on the following successive steps: exposure assessment, consequence characterization and risk characterization. In this context, the risk is a function of the likelihood that animals will experience certain welfare consequences and the magnitude of those consequences, following exposure to a factor or exposure scenario, in a target population. Such an assessment may simultaneously include both negative (risk) and positive (benefits) welfare consequences. The concepts inherent in this approach are first described, then two examples are used to illustrate the implementation of the methodology.

Abstract The EFSA Panel on Animal Health and Welfare (AHAW) was asked to deliver a scientific opinion on the use of high‐expansion foam for stunning and killing pigs and poultry. A dossier was provided by the applicant as the basis for an assessment of the extent to which the method is able to provide a level of animal welfare at least equivalent to that ensured by the currently allowed methods for pigs and poultry. According to legislation, to be approved in the EU, new stunning methods must ensure (1) the absence of pain, distress or suffering until the onset of unconsciousness, and (2) that the animal remains unconscious until death. An ad hoc Working Group set up by EFSA performed the assessment as follows: (1) The data provided were checked against the criteria laid down in the EFSA Guidance (EFSA, 2018), and was found to partially fulfil those criteria; (2) extensive literature search; (3) data extraction for quantitative assessment; (4) qualitative exercise based on non‐formal expert elicitation. The assessment led to conclude that it is more likely than not (certainty > 50%–100%) that high‐expansion foam for stunning and killing pigs and poultry, named NEFS in container (Nitrogen Expansion Foam Stunning in container), provides a level of welfare at least equivalent to one or more of the currently allowed methods listed in Annex I of Council Regulation (EC) No 1099/2009. The overall assessment of EFSA is valid only under the technical conditions described in this Opinion for laying hens, broiler chickens of all age and pigs weighing 15–41 kg in situations other than slaughter. The overall assessment of EFSA is that NEFS can be suitable for depopulation using containers for pig and poultry farms respecting the technical conditions and the categories and types of animals defined in this Scientific Opinion.

This Scientific Opinion addresses a European Commission request on the welfare of calves as part of the Farm to Fork strategy. EFSA was asked to provide a description of common husbandry systems and related welfare consequences, as well as measures to prevent or mitigate the hazards leading to them. In addition, recommendations on three specific issues were requested: welfare of calves reared for white veal (space, group housing, requirements of iron and fibre); risk of limited cow-calf contact; and animal-based measures (ABMs) to monitor on-farm welfare in slaughterhouses. The methodology developed by EFSA to address similar requests was followed. Fifteen highly relevant welfare consequences were identified, with respiratory disorders, inability to perform exploratory or foraging behaviour, gastroenteric disorders and group stress being the most frequent across husbandry systems. Recommendations to improve the welfare of calves include increasing space allowance, keeping calves in stable groups from an early age, ensuring good colostrum management and increasing the amounts of milk fed to dairy calves. In addition, calves should be provided with deformable lying surfaces, water via an open surface and long-cut roughage in racks. Regarding specific recommendations for veal systems, calves should be kept in small groups (2-7 animals) within the first week of life, provided with similar to 20 m(2)/calf and fed on average 1 kg neutral detergent fibre (NDF) per day, preferably using long-cut hay. Recommendations on cow-calf contact include keeping the calf with the dam for a minimum of 1 day post-partum. Longer contact should progressively be implemented, but research is needed to guide this implementation in practice. The ABMs body condition, carcass condemnations, abomasal lesions, lung lesions, carcass colour and bursa swelling may be collected in slaughterhouses to monitor on-farm welfare but should be complemented with behavioural ABMs collected on farm. (c) 2023 European Food Safety Authority. EFSA Journal published by Wiley-VCH GmbH on behalf of European Food Safety Authority.

Table A.1 - Available vaccines for HPAI in poultry. The table includes information on vaccine characteristics such as HA seed strain, vaccine dosage and administration route, target species, authorisation status, challenge strain, duration of immunity and source of information. Table A.2 - Challenge experiments. The table includes data extracted from 28 selected studies from literature on challenge experiments. The data was used to quantify VES, VEm, VEsh and VEs,sh for each vaccine assessed within each study. For each value of VEs,sh the probability Rvac < 1 was estimated to infer the probability of the vaccine to potentially stop transmission in vaccinated birds (VET). Table A.3 - Transmission experiments. The table includes data extracted from 12 studies from the literature, data was used from the unvaccinated and vaccinated groups to quantify VES, VEm, VEsh and VEs,sh for each vaccine assessed within each study; the estimated R values for the unvaccinated (Runv) and vaccinated (Rvac) groups were also extracted. The Rvac values were used to create a binomial variable classifying whether the vaccine could stop transmission (R < 1) or not.

Several vaccines have been developed against highly pathogenic avian influenza (HPAI), mostly inactivated whole-virus vaccines for chickens. In the EU, one vaccine is authorised in chickens but is not fully efficacious to stop transmission, highlighting the need for vaccines tailored to diverse poultry species and production types. Off-label use of vaccines is possible, but effectiveness varies. Vaccines are usually injectable, a time-consuming process. Mass-application vaccines outside hatcheries remain rare. First vaccination varies from in-ovo to 6 weeks of age. Data about immunity onset and duration in the target species are often unavailable, despite being key for effective planning. Minimising antigenic distance between vaccines and field strains is essential, requiring rapid updates of vaccines to match circulating strains. Generating harmonised vaccine efficacy data showing vaccine ability to reduce transmission is crucial and this ability should be also assessed in field trials. Planning vaccination requires selecting the most adequate vaccine type and vaccination scheme. Emergency protective vaccination is limited to vaccines that are not restricted by species, age or pre-existing vector-immunity, while preventive vaccination should prioritise achieving the highest protection, especially for the most susceptible species in high-risk transmission areas. Model simulations in France, Italy and The Netherlands revealed that (i) duck and turkey farms are more infectious than chickens, (ii) depopulating infected farms only showed limitations in controlling disease spread, while 1-km ring-culling performed better than or similar to emergency preventive ring-vaccination scenarios, although with the highest number of depopulated farms, (iii) preventive vaccination of the most susceptible species in high-risk transmission areas was the best option to minimise the outbreaks' number and duration, (iv) during outbreaks in such areas, emergency protective vaccination in a 3-km radius was more effective than 1- and 10-km radius. Vaccine efficacy should be monitored and complement other surveillance and preventive efforts. This publication is linked to the following EFSA Journal article:

This scientific opinion focuses on the welfare of laying hens, pullets and layer breeders on farm. The most relevant husbandry systems used in Europe are described. For each system, highly relevant welfare consequences were identified, as well as related animal-based measures (ABMs), and hazards leading to the welfare consequences. Moreover, measures to prevent or correct the hazards and/or mitigate the welfare consequences are recommended. The highly relevant welfare consequences based on severity, duration and frequency of occurrence are bone lesions, group stress, inability to avoid unwanted sexual behaviour, inability to perform comfort behaviour, inability to perform exploratory or foraging behaviour, isolation stress, predation stress, resting problems, restriction of movement, skin disorders and soft tissue lesions and integument damage. The welfare consequences of non-cage compared to cage systems for laying hens are described and minimum enclosure characteristics are described for laying hens, pullets and layer breeders. Beak trimming, which causes negative welfare consequences and is conducted to reduce the prevalence and severity of pecking, is described as well as the risks associated with rearing of non-beak-trimmed flocks. Alternatives to reduce sharpness of the beak without trimming are suggested. Finally, total mortality, plumage damage, wounds, keel bone fractures and carcass condemnations are the most promising ABMs for collection at slaughterhouses to monitor the level of laying hen welfare on farm. Main recommendations include housing all birds in non-cage systems with easily accessible, elevated platforms and provision of dry and friable litter and access to a covered veranda. It is further recommended to implement protocols to define welfare trait information to encourage progress in genetic selection, implement measures to prevent injurious pecking, rear pullets with dark brooders and reduce male aggression in layer breeders.

To date, there is no official method for measuring horse welfare after transport. This study aimed to develop a scale to classify horses into four categories: good shape; light affected; affected; down (GLAD) based on their welfare impairment measured at unloading. To this end, 15 animal-based measures (ABMs), previously recorded from 1019 horses, were scored. Weight and severity scores provided by welfare experts, alongside the number of welfare principles highlighted by the ABM, were assigned to each ABM. The welfare impairment (S) of each horse was then calculated as the weighted sum of the severity scores of the 15 ABMs. Three thresholds were also set to define the four GLAD categories; the ABM "down" (i.e., horses unable to stand and walk on arrival, also considered by the law as the indicator of the worst welfare) was used as the higher threshold, Sdown, (category D); the intermediate threshold, S2, was defined by the ABM "injuries", assumed to represent highly impaired welfare (category A); the threshold, S1, was defined assuming that significant welfare impairment starts from 20% of S2 (L category). Horses with an S value below S1 were considered physically and mentally fit (G category). Out of 1019, 43% of horses fell into category G, 48% into L, 9% into A, and 0.3% into D. Our scale could be useful for veterinarians to decide whether a horse can be slaughtered immediately (G), needs rest (L), needs attention (A), or euthanasia (D), but further validation is needed.

This Scientific Opinion addresses a European Commission's mandate on the welfare of dairy cows as part of the Farm to Fork strategy. It includes three assessments carried out based on literature reviews and complemented by expert opinion. Assessment 1 describes the most prevalent housing systems for dairy cows in Europe: tie-stalls, cubicle housing, open-bedded systems and systems with access to an outdoor area. Per each system, the scientific opinion describes the distribution in the EU and assesses the main strengths, weaknesses and hazards potentially reducing the welfare of dairy cows. Assessment 2 addresses five welfare consequences as requested in the mandate: locomotory disorders (including lameness), mastitis, restriction of movement and resting problems, inability to perform comfort behaviour and metabolic disorders. Per each welfare consequence, a set of animal-based measures is suggested, a detailed analysis of the prevalence in different housing systems is provided, and subsequently, a comparison of the housing systems is given. Common and specific system-related hazards as well as management-related hazards and respective preventive measures are investigated. Assessment 3 includes an analysis of farm characteristics (e.g. milk yield, herd size) that could be used to classify the level of on-farm welfare. From the available scientific literature, it was not possible to derive relevant associations between available farm data and cow welfare. Therefore, an approach based on expert knowledge elicitation (EKE) was developed. The EKE resulted in the identification of five farm characteristics (more than one cow per cubicle at maximum stocking density, limited space for cows, inappropriate cubicle size, high on-farm mortality and farms with less than 2 months access to pasture). If one or more of these farm characteristics are present, it is recommended to conduct an assessment of cow welfare on the farm in question using animal-based measures for specified welfare consequences.

Infection with salmonid alphavirus (SAV) was assessed according to the criteria of the Animal Health Law (AHL), in particular the criteria of Article 7 on disease profile and impacts, Article 5 on its eligibility to be listed, Annex IV for its categorisation according to disease prevention and control rules as laid out in Article 9 and Article 8 for listing animal species related to infection with SAV. The assessment was performed following the ad hoc method on data collection and assessment developed by AHAW Panel and already published. The outcome reported is the median of the probability ranges provided by the experts, which indicates whether each criterion is fulfilled (lower bound >= 66%) or not (upper bound <= 33%), or whether there is uncertainty about fulfilment. Reasoning points are reported for criteria with an uncertain outcome. According to the assessment, it was uncertain whether infection with salmonid alphavirus can be considered eligible to be listed for Union intervention according to Article 5 of the AHL (50-80% probability). According to the criteria in Annex IV, for the purpose of categorisation related to the level of prevention and control as in Article 9 of the AHL, the AHAW Panel concluded that infection with salmonid alphavirus does not meet the criteria in Section 1 (Category A; 5-10% probability of meeting the criteria) and it is uncertain whether it meets the criteria in Sections 2, 3, 4 and 5 (Categories B, C, D and E; 50-90%, probability of meeting the criteria). The animal species to be listed for infection with SAV according to Article 8 criteria are provided.

Abstract Infectious pancreatic necrosis (IPN) was assessed according to the criteria of the Animal Health Law (AHL), in particular, the criteria of Article 7 on disease profile and impacts, Article 5 on its eligibility to be listed, Annex IV for its categorisation according to disease prevention and control rules as in Article 9, and Article 8 for listing animal species related to IPN. The assessment was performed following a methodology previously published. The outcome reported is the median of the probability ranges provided by the experts, which indicates whether each criterion is fulfilled (lower bound ≥ 66%) or not (upper bound ≤ 33%), or whether there is uncertainty about fulfilment. Reasoning points are reported for criteria with an uncertain outcome. According to the assessment here performed, it is uncertain whether IPN can be considered eligible to be listed for Union intervention according to Article 5 of the AHL (50–90% probability). According to the criteria in Annex IV, for the purpose of categorisation related to the level of prevention and control as in Article 9 of the AHL, the AHAW Panel concluded that IPN does not meet the criteria in Section 1 (Category A; 0–1% probability of meeting the criteria) and it is uncertain whether it meets the criteria in Sections 2, 3, 4 and 5 (Categories B, C, D and E; 33–66%, 33–66%, 50–90% and 50–99% probability of meeting the criteria, respectively). The animal species to be listed for IPN according to Article 8 criteria are provided.

This Scientific Opinion concerns the welfare of Domestic ducks (Anas platyrhynchos domesticus), Muscovy ducks (Cairina moschata domesticus) and their hybrids (Mule ducks), Domestic geese (Anser anser f. domesticus) and Japanese quail (Coturnix japonica) in relation to the rearing of breeders, birds for meat, Muscovy and Mule ducks and Domestic geese for foie gras and layer Japanese quail for egg production. The most common husbandry systems (HSs) in the European Union are described for each animal species and category. The following welfare consequences are described and assessed for each species: restriction of movement, injuries (bone lesions including fractures and dislocations, soft tissue lesions and integument damage and locomotory disorders including lameness), group stress, inability to perform comfort behaviour, inability to perform exploratory or foraging behaviour and inability to express maternal behaviour (related to prelaying and nesting behaviours). Animal-based measures relevant for the assessment of these welfare consequences were identified and described. The relevant hazards leading to the welfare consequences in the different HSs were identified. Specific factors such as space allowance (including minimum enclosure area and height) per bird, group size, floor quality, characteristics of nesting facilities and enrichment provided (including access to water to fulfil biological needs) were assessed in relation to the welfare consequences and, recommendations on how to prevent the welfare consequences were provided in a quantitative or qualitative way.

In 2013, the International Agency for Research on Cancer classified particulate matter (PM) as carcinogenic to humans. It is therefore essential to measure PM concentrations to minimize the exposure of individuals. Our objective was to investigate personal exposure to PM2.5 (PM with diameter <= 2.5 mu m) in Grenoble (France) during commuting in different transportation modes: bike, walk, bus and tramway. PM2.5 measurements were found to be the highest for bikes, followed by walk, bus, and tramway. In this study, conducted in spring during low pollution levels of PM, exposure levels are greatly influenced by the time of day. Pedestrian and cyclists' exposure generally stayed under background reference values. Exposure in public transportation was usually below reference values, but when background PM2.5 levels went lower (evening), levels registered in the tramway or bus reached those of the reference. Therefore, public transport users could be less exposed than active commuters, except when ambient pollutant levels are low. Environmental parameters like wind might be important in Grenoble, and it would be worthwhile to reproduce this study at a time when wind speed is lower.

The epidemiological situation of SARS-CoV-2 in humans and animals is continually evolving. To date, animal species known to transmit SARS-CoV-2 are American mink, raccoon dog, cat, ferret, hamster, house mouse, Egyptian fruit bat, deer mouse and white-tailed deer. Among farmed animals, American mink have the highest likelihood to become infected from humans or animals and further transmit SARS-CoV-2. In the EU, 44 outbreaks were reported in 2021 in mink farms in seven MSs, while only six in 2022 in two MSs, thus representing a decreasing trend. The introduction of SARS-CoV-2 into mink farms is usually via infected humans; this can be controlled by systematically testing people entering farms and adequate biosecurity. The current most appropriate monitoring approach for mink is the outbreak confirmation based on suspicion, testing dead or clinically sick animals in case of increased mortality or positive farm personnel and the genomic surveillance of virus variants. The genomic analysis of SARS-CoV-2 showed mink-specific clusters with a potential to spill back into the human population. Among companion animals, cats, ferrets and hamsters are those at highest risk of SARS-CoV-2 infection, which most likely originates from an infected human, and which has no or very low impact on virus circulation in the human population. Among wild animals (including zoo animals), mostly carnivores, great apes and white-tailed deer have been reported to be naturally infected by SARS-CoV-2. In the EU, no cases of infected wildlife have been reported so far. Proper disposal of human waste is advised to reduce the risks of spill-over of SARS-CoV-2 to wildlife. Furthermore, contact with wildlife, especially if sick or dead, should be minimised. No specific monitoring for wildlife is recommended apart from testing hunter-harvested animals with clinical signs or found-dead. Bats should be monitored as a natural host of many coronaviruses.