Application of Species Distribution Modeling for Avian Influenza surveillance in the United States considering the North America Migratory Flyways

Legacy parasite collections reveal species-specific population genetic patterns among three species of zoonotic schistosomes

Studies estimating genetic diversity and population structure in multi-host parasites are often constrained by temporally and spatially limited sampling. This study addresses these limitations by analyzing globally distributed samples of three congeneric avian schistosomes (Trematoda: Schistosomatidae: Trichobilharzia), including collections spanning 20 years archived at The Museum of Southwestern Biology, Parasites Division. The three species exhibited significant differences in population genetic parameters across one nuclear and two mitochondrial loci. Trichobilharzia querquedulae (TQ) maintained a well-connected, globally diverse metapopulation, with an effective population size approximately three times larger than that of the other two species, T. physellae (TP) and Trichobilharzia sp. A (TA). TP and TA had lower overall genetic diversity and greater population structure. These differences are likely shaped by the ecologies of the duck definitive hosts that disperse these parasites. This study highlights the value of natural history collections, particularly since Trichobilharzia is a key agent of zoonotic cercarial dermatitis, a disease whose etiology and epidemiology remain poorly understood. Within a comparative congeneric framework, population genetic data can provide insights into host-parasite natural history and its influence on microevolutionary patterns, including contributions to zoonotic disease.

Comprehensive Insights into Highly Pathogenic Avian Influenza H5N1 in Dairy Cattle: Transmission Dynamics, Milk-Borne Risks, Public Health Implications, Biosecurity Recommendations, and One Health Strategies for Outbreak Control

Highly pathogenic avian influenza (HPAI) H5N1 has been traditionally linked to poultry and wild birds, which has recently become a serious concern for dairy cattle, causing outbreaks all over the United States. The need for improved surveillance, biosecurity protocols, and interagency collaboration is highlighted by the discovery of H5N1 in dairy herds in several states and its human transmission. The epidemiology, transmission dynamics, and wide-ranging effects of H5N1 in cattle are reviewed in this paper, with particular attention paid to the disease's effects on agricultural systems, public health, and animal health. Nonspecific clinical symptoms, such as decreased milk production and irregular milk consistency, are indicative of infection in dairy cows. Alarmingly, significant virus loads have been discovered in raw milk, raising worries about potential zoonotic transmission. The dangers of viral spillover between species are further highlighted by cases of domestic cats experiencing severe neurological symptoms after ingesting raw colostrum and milk from infected cows. Even though human cases remain rare, and they are mostly related to occupational exposure, constant attention is required due to the possibility of viral adaptability. The necessity of a One Health approach that integrates environmental, animal, and human health efforts is further supported by the broad occurrence of H5N1 across multiple species. For early detection, containment, and mitigation, cooperation between veterinary clinics, public health organizations, and agricultural stakeholders is crucial. Controlling the outbreak requires stringent movement restrictions, regular testing of dairy cows in reference labs, and adherence to biosecurity procedures. This review highlights the importance of thorough and coordinated efforts to manage H5N1 in dairy cattle by combining existing knowledge and pointing out gaps in surveillance and response strategies. Additionally, it sheds light on the potential risk of consumption of cow's milk contaminated with H5N1 virus by humans and other companion animals like cats. In the face of this changing threat, proactive monitoring, strict biosecurity protocols, and cross-sector cooperation are crucial for reducing financial losses and protecting human and animal health.

Reaction-advection-diffusion model of highly pathogenic avian influenza with behavior of migratory wild birds

Wild birds are one of the main natural reservoirs for avian influenza viruses, and their migratory behavior significantly influences the transmission of avian influenza. To better describe the migratory behavior of wild birds, a system of reaction-advection-diffusion equations is developed to characterize the interactions among wild birds, poultry, and humans. By the next-generation operator, the basic reproduction number of the model is formulated. Then the threshold dynamic of the model is explored by some techniques including the theory of uniform persistence, internally chain transitive sets, and so on. Subsequently, the sensitivity analysis of parameters associated with the basic reproduction number is implemented. According to the temporal and spatial overlapping relationship between wild blue-winged ducks and poultry in North America, the effect of this relationship on the characteristic of spatial-temporal distribution of the viruses is well studied. Additionally, the risk of virus transmission from wild birds to poultry and humans is evaluated. The main results highlight that the basic reproduction number is more significantly affected by the parameters related to wild birds. Interestingly, the model output regarding the spatial distribution of poultry infections is consistent with the actual findings. Moreover, the risk of virus spillover from wild birds into poultry and humans varies with wild bird behavior and has a more substantial impact on poultry. Throughout this study, the critical risk points in the transmission process are identified, providing a theoretical basis for the prevention and control of avian influenza.

Human Health Surveillance During Animal Disease Emergencies: Minnesota Department of Health Response to Highly Pathogenic Avian Influenza Outbreaks, 2015 and 2022-2023

OBJECTIVES

Highly pathogenic avian influenza (HPAI) poses an occupational risk for poultry workers, responders, and others in contact with infected birds. The objective of this analysis was to describe HPAI surveillance methods and outcomes, and highlight the challenges, successes, and lessons learned during the Minnesota Department of Health's (MDH's) public health response to HPAI outbreaks in Minnesota poultry flocks in the years 2015 and 2022-2023.

METHODS

During both outbreaks, MDH staff attempted to contact all potentially exposed people and conduct a standardized interview. People were considered exposed and at risk if they had entered a barn with poultry on any HPAI test-positive premises. With their consent, exposed persons were entered into illness monitoring until 10 days from their last exposure. In 2015, MDH monitored the health of poultry workers only. In the 2022-2023 response, MDH monitored the health of poultry workers, backyard flock owners, responders, and private contract workers. In 2022-2023, interview responses were entered into a REDCap (Research Electronic Data Capture) database in real time, which automatically entered the person into monitoring if they consented. Through REDCap, they received an automated email with a unique link to a short survey asking about any symptom development. Where appropriate, interview responses from poultry workers collected in 2015 were compared to interview responses from poultry workers collected in 2022-2023.

RESULTS

From March 3 to June 5, 2015, MDH epidemiologists interviewed and evaluated 375 (86%) of 435 poultry workers from 110 HPAI-infected flocks. From March 25, 2022 through December 31, 2023, MDH epidemiologists interviewed and evaluated 649 (65%) of 992 poultry workers, responders, contractors, and backyard flock owners associated with 151 HPAI-infected flocks. Among poultry workers, self-reported personal protective equipment (PPE) usage declined significantly from 2015 to 2022-2023 (full PPE usage 51.8% vs. 23.9%, p < .01).

CONCLUSION

MDH's long standing relationships with animal health officials and the poultry industry resulted in strong poultry worker participation rates in surveillance efforts during HPAI outbreaks in 2015 and 2022-2023. Self-reported PPE usage was low, particularly in 2022-2023. Improvements in PPE accessibility and technology are needed to protect workers and responders in the on-going HPAI outbreak.

Avian Influenza: Lessons from Past Outbreaks and an Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to Tackle Ongoing Outbreaks

BACKGROUND/OBJECTIVES

The ongoing avian influenza (H5N1) outbreak, one of the most widespread and persistent in recent history, has significantly impacted public health and the poultry and dairy cattle industries. This review covers lessons from past outbreaks, risk factors for transmission, molecular epidemiology, clinical features, surveillance strategies, and socioeconomic impacts. Since 1997, H5N1 has infected over 900 individuals globally, with a fatality rate exceeding 50%. Key factors influencing infection rates include demographic, socioeconomic, environmental, and ecological variables. The virus's potential for sustained human-to-human transmission remains a concern. The current outbreak, marked by new viral clades, has complicated containment efforts.

METHODS

This review discusses how to integrate technological advances, such as mathematical modeling and artificial intelligence (AI), to improve forecasting, hotspot detection, and early warning systems.

RESULTS

We provide inventories of data sources, covering both conventional and unconventional data streams, as well as those of mathematical and AI models, which can be vital for comprehensive surveillance and outbreak responses.

CONCLUSION

In conclusion, integrating AI, mathematical models, and technological innovations into a One-Health approach is essential for improving surveillance, forecasting, and response strategies to mitigate the impacts of the ongoing avian influenza outbreak. Strengthening international collaboration and biosecurity measures will be pivotal in controlling future outbreaks and protecting both human and animal populations from this evolving global threat.

Novel Epidemiologic Features of High Pathogenicity Avian Influenza Virus A H5N1 2.3.3.4b Panzootic: A Review

Avian influenza is one of the most devastating avian diseases. The current high pathogenicity avian influenza (HPAI) A virus H5N1 clade 2.3.4.4b epizootic began in the 2020-2021 season, and has caused a panzootic, considered one of the worst ever reported. The present panzootic has novel epidemiological features that represent a challenge for its prevention and control. This review examines key epidemiological changes of the disease such as seasonality, geographic spread, and host range. The seasonality of the virus has changed, and contrary to previous avian influenza epizootics, this subclade was able to persist during boreal summer. Its geographic range has expanded, with reports in all continents except Australia. During this epizootic, HPAIV H5N1 has broadened its host range, infecting hundreds of bird species, and causing the death of thousands of wild birds and over 300 million poultry. The number and diversity of mammal species infected by H5N1 2.3.4.4b is unprecedented. Although considered low, this strain's potential to spillover to humans should not be underestimated, especially considering the current extremely high viral circulation in animals and increasing adaptation to mammals. Overall, HPAI A(H5N1) clade 2.3.4.4b represents an ongoing and growing threat to poultry, wildlife, and human health.

Using an adaptive modeling framework to identify avian influenza spillover risk at the wild-domestic interface

The wild to domestic bird interface is an important nexus for emergence and transmission of highly pathogenic avian influenza (HPAI) viruses. Although the recent incursion of HPAI H5N1 Clade 2.3.4.4b into North America calls for emergency response and planning given the unprecedented scale, readily available data-driven models are lacking. Here, we provide high resolution spatial and temporal transmission risk models for the contiguous United States. Considering virus host ecology, we included weekly species-level wild waterfowl (Anatidae) abundance and endemic low pathogenic avian influenza virus prevalence metrics in combination with number of poultry farms per commodity type and relative biosecurity risks at two spatial scales: 3 km and county-level. Spillover risk varied across the annual cycle of waterfowl migration and some locations exhibited persistent risk throughout the year given higher poultry production. Validation using wild bird introduction events identified by phylogenetic analysis from 2022 to 2023 HPAI poultry outbreaks indicate strong model performance. The modular nature of our approach lends itself to building upon updated datasets under evolving conditions, testing hypothetical scenarios, or customizing results with proprietary data. This research demonstrates an adaptive approach for developing models to inform preparedness and response as novel outbreaks occur, viruses evolve, and additional data become available.

Modeling West Nile Virus transmission in birds and humans: Advantages of using a cellular automata approach

In Canada, the periodic circulation of West Nile Virus (WNV) is difficult to predict and, beyond climatic factors, appears to be related to the migratory movements of infected birds from the southern United States. This hypothesis has not yet been explored in a spatially distributed model. The main objective of this work was to develop a spatially explicit dynamic model for the transmission of WNV in Canada, that allows us to explore non-climate related hypotheses associated with WNV transmission. A Cellular Automata (CA) approach for multiple hosts (birds and humans) is used for a test region in eastern Ontario, Canada. The tool is designed to explore the role of host and vector spatial heterogeneity, host migration, and vector feeding preferences. We developed a spatialized compartmental SEIRDS-SEI model for WNV transmission with a study region divided into 4 k m 2 rectangular cells. We used 2010-2021 bird data from the eBird project and 2010-2019 mosquito data collected by Ontario Public Health to mimic bird and mosquito seasonal variation. We considered heterogeneous bird densities (high and low suitability areas) and homogeneous mosquito and human densities. In high suitability areas for birds, we identified 5 entry points for WNV-infected birds. We compared our simulations with pools of WNV-infected field collected mosquitoes. Simulations and sensitivity analyses were performed using MATLAB software. The results showed good correspondence between simulated and observed epidemics, supporting the validity of our model assumptions and calibration. Sensitivity analysis showed that a 5% increase or decrease in each parameter of our model except for the biting rate of bird by mosquito (c ( B , M ) ) and mosquito natural mortality rate (d M ), had a very limited effect on the total number of cases (newly infected birds and humans), prevalence peak, or date of occurrence. We demonstrate the utility of the CA approach for studying WNV transmission in a heterogeneous landscape with multiple hosts.

Risk factors for avian influenza in Danish poultry and wild birds during the epidemic from June 2020 to May 2021

Exploring the risk factors of avian influenza (AI) occurrence helps us to monitor and control the disease. Since late 2020, the number of avian influenza outbreaks in domestic and wild birds has increased in most European countries, including Denmark. This study was conducted to identify potential risk factors for wild birds and poultry during the epidemic in 2020/2021 in Denmark. Using Danish AI surveillance data of actively surveyed poultry and passively surveyed wild birds from June 2020 to May 2021, we calculated geographical attributes for bird locations and assessed the potential risk factors of AI detections using logistic regression analyses. 4% of actively surveyed poultry and 39% of passively surveyed wild birds were detected with AI circulating or ongoing at the time. Of these, 10 and 99% tested positive for the H5/H7 AI subtypes, respectively. Our analyses did not find any statistically significant risk factors for actively surveyed poultry within the dataset. For passively surveyed wild birds, bird species belonging to the Anseriformes order had a higher risk of being AI virus positive than five other taxonomic bird orders, and Galliformes were of higher risk than two other taxonomic bird orders. Besides, every 1 km increase in the distance to wetlands was associated with a 5.18% decrease in the risk of being AI positive (OR (odds ratio) 0.95, 95% CI 0.91, 0.99), when all other variables were kept constant. Overall, bird orders and distance to wetlands were associated with the occurrence of AI. The findings may provide targets for surveillance strategies using limited resources and assist in risk-based surveillance during epidemics.

Identification of high-risk contact areas between feral pigs and outdoor-raised pig operations in California: Implications for disease transmission in the wildlife-livestock interface

The US is currently experiencing a return to raising domestic pigs outdoors, due to consumer demand for sustainably-raised animal products. A challenge in raising pigs outdoors is the possibility of these animals interacting with feral pigs and an associated risk of pathogen transmission. California has one of the largest and widest geographic distributions of feral pigs. Locations at greatest risk for increased contact between both swine populations are those regions that contain feral pig suitable habitat located near outdoor-raised domestic pigs. The main aim of this study entailed identifying potential high-risk areas of disease transmission between these two swine populations. Aims were achieved by predicting suitable feral pig habitat using Maximum Entropy (MaxEnt); mapping the spatial distribution of outdoor-raised pig operations (OPO); and identifying high-risk regions where there is overlap between feral pig suitable habitat and OPO. A MaxEnt prediction map with estimates of the relative probability of suitable feral pig habitat was built, using hunting tags as presence-only points. Predictor layers were included in variable selection steps for model building. Five variables were identified as important in predicting suitable feral pig habitat in the final model, including the annual maximum green vegetation fraction, elevation, the minimum temperature of the coldest month, precipitation of the wettest month and the coefficient of variation for seasonal precipitation. For the risk map, the final MaxEnt model was overlapped with the location of OPOs to categorize areas at greatest risk for contact between feral swine and domestic pigs raised outdoors and subsequent potential disease transmission. Since raising pigs outdoors is a remerging trend, feral pig numbers are increasing nationwide, and both groups are reservoirs for various pathogens, the contact between these two swine populations has important implications for disease transmission in the wildlife-livestock interface.

Host diversity and behavior determine patterns of interspecies transmission and geographic diffusion of avian influenza A subtypes among North American wild reservoir species

Wild birds can carry avian influenza viruses (AIV), including those with pandemic or panzootic potential, long distances. Even though AIV has a broad host range, few studies account for host diversity when estimating AIV spread. We analyzed AIV genomic sequences from North American wild birds, including 303 newly sequenced isolates, to estimate interspecies and geographic viral transition patterns among multiple co-circulating subtypes. Our results show high transition rates within Anseriformes and Charadriiformes, but limited transitions between these orders. Patterns of transition between species were positively associated with breeding habitat range overlap, and negatively associated with host genetic distance. Distance between regions (negative correlation) and summer temperature at origin (positive correlation) were strong predictors of transition between locations. Taken together, this study demonstrates that host diversity and ecology can determine evolutionary processes that underlie AIV natural history and spread. Understanding these processes can provide important insights for effective control of AIV.

Spatiotemporal risk assessment for avian influenza outbreak based on the dynamics of habitat suitability for wild birds

Highly pathogenic avian influenza (HPAI) has predominantly damaged the poultry industry worldwide. The fundamental prevention and control strategy for HPAI includes early detection and timely intervention enforcement through a systematic surveillance system for wild birds based on the ecological understanding of the dynamics of wild birds' movements. Our study aimed to develop a spatiotemporal risk assessment model for avian influenza (AI) infection in wild birds to empower surveillance information for a contingency strategy. For this purpose, first, we predicted the monthly habitat suitability of seven waterfowl species, using 227,671 Global Positioning System (GPS) tracking records of 562 birds from 2014 to 2018 in the Republic of Korea (ROK). Then, that predicted habitat suitability and 421 coordinates of AI detection sites in wild birds were used to build the risk assessment model. Subsequently, we compared the monthly predicted risk of avian influenza virus (AIv) identification in wild birds between case and non-case poultry farms with HPAI H5N6 outbreak in the ROK between 2016 and 2017. The results reported considerable variation of monthly habitat suitability of seven waterfowls and the impact of predicting AI occurrences in wild birds. The high habitat suitability for spot-billed ducks (contribution rate in November = 40.9%) and mallards (contribution rate in January = 34.3%) significantly contributed to predicting the average risk of AIv identification in wild birds, with high predictive performance [the monthly mean of area under the curve (AUC) = 0.978]. Moreover, our model showed that the averaged risk of identification AI in wild birds was significantly higher in HPAI infected premises, with infected domestic duck holdings exhibiting a significantly higher risk than the chicken farms in November. This study suggests that animal health authority establishes a risk-based HPAI surveillance system grounded on the ecological nature of wild birds to improve the effectiveness of prevention and preparedness of emerging epidemics.

Geospatial modelling to estimate the territory at risk of establishment of influenza type A in Mexico - An ecological study

The aim of this study was to estimate the territory at risk of establishment of influenza type A (EOITA) in Mexico, using geospatial models. A spatial database of 1973 outbreaks of influenza worldwide was used to develop risk models accounting for natural (natural threat), anthropic (man-made) and environmental (combination of the above) transmission. Then, a virus establishment risk model; an introduction model of influenza A developed in another study; and the three models mentioned were utilized using multi-criteria spatial evaluation supported by geographically weighted regression (GWR), receiver operating characteristic analysis and Moran's I. The results show that environmental risk was concentrated along the Gulf and Pacific coasts, the Yucatan Peninsula and southern Baja California. The identified risk for EOITA in Mexico were: 15.6% and 4.8%, by natural and anthropic risk, respectively, while 18.5% presented simultaneous environmental, natural and anthropic risk. Overall, 28.1% of localities in Mexico presented a High/High risk for the establishment of influenza type A (area under the curve=0.923, P<0.001; GWR, r2=0.840, P<0.001; Moran's I =0.79, P<0.001). Hence, these geospatial models were able to robustly estimate those areas susceptible to EOITA, where the results obtained show the relation between the geographical area and the different effects on health. The information obtained should help devising and directing strategies leading to efficient prevention and sound administration of both human and financial resources.

Continental-scale dynamics of avian influenza in U.S. waterfowl are driven by demography, migration, and temperature

Emerging diseases of wildlife origin are increasingly spilling over into humans and domestic animals. Surveillance and risk assessments for transmission between these populations are informed by a mechanistic understanding of the pathogens in wildlife reservoirs. For avian influenza viruses (AIV), much observational and experimental work in wildlife has been conducted at local scales, yet fully understanding their spread and distribution requires assessing the mechanisms acting at both local, (e.g., intrinsic epidemic dynamics), and continental scales, (e.g., long-distance migration). Here, we combined a large, continental-scale data set on low pathogenic, Type A AIV in the United States with a novel network-based application of bird banding/recovery data to investigate the migration-based drivers of AIV and their relative importance compared to well-characterized local drivers (e.g., demography, environmental persistence). We compared among regression models reflecting hypothesized ecological processes and evaluated their ability to predict AIV in space and time using within and out-of-sample validation. We found that predictors of AIV were associated with multiple mechanisms at local and continental scales. Hypotheses characterizing local epidemic dynamics were strongly supported, with age, the age-specific aggregation of migratory birds in an area and temperature being the best predictors of infection. Hypotheses defining larger, network-based features of the migration processes, such as clustering or between-cluster mixing explained less variation but were also supported. Therefore, our results support a role for local processes in driving the continental distribution of AIV.

A framework for the risk prediction of avian influenza occurrence: An Indonesian case study

Avian influenza viruses can cause economically devastating diseases in poultry and have the potential for zoonotic transmission. To mitigate the consequences of avian influenza, disease prediction systems have become increasingly important. In this study, we have proposed a framework for the prediction of the occurrence and spread of avian influenza events in a geographical area. The application of the proposed framework was examined in an Indonesian case study. An extensive list of historical data sources containing disease predictors and target variables was used to build spatiotemporal and transactional datasets. To combine disparate sources, data rows were scaled to a temporal scale of 1-week and a spatial scale of 1-degree × 1-degree cells. Given the constructed datasets, underlying patterns in the form of rules explaining the risk of occurrence and spread of avian influenza were discovered. The created rules were combined and ordered based on their importance and then stored in a knowledge base. The results suggested that the proposed framework could act as a tool to gain a broad understanding of the drivers of avian influenza epidemics and may facilitate the prediction of future disease events.

Different environmental gradients associated to the spatiotemporal and genetic pattern of the H5N8 highly pathogenic avian influenza outbreaks in poultry in Italy

Comprehensive understanding of the patterns and drivers of avian influenza outbreaks is pivotal to inform surveillance systems and heighten nations' ability to quickly detect and respond to the emergence of novel viruses. Starting in early 2017, the Italian poultry sector has been involved in the massive H5N8 highly pathogenic avian influenza epidemic that spread in the majority of the European countries in 2016/2017. Eighty-three outbreaks were recorded in north-eastern Italy, where a densely populated poultry area stretches along the Lombardy, Emilia-Romagna and Veneto regions. The confirmed cases, affecting both the rural and industrial sectors, depicted two distinct epidemic waves. We adopted a combination of multivariate statistics techniques and multi-model regression selection and inference, to investigate how environmental factors relate to the pattern of outbreaks diversity with respect to their spatiotemporal and genetic diversity. Results showed that a combination of eco-climatic and host density predictors were associated with the outbreaks pattern, and variation along gradients was noticeable among genetically and geographically distinct groups of avian influenza cases. These regional contrasts may be indicative of a different mechanism driving the introduction and spreading routes of the influenza virus in the domestic poultry population. This methodological approach may be extended to different spatiotemporal scale to foster site-specific, ecologically informed risk mitigating strategies.

The Evolutionary Dynamics of Influenza A Viruses Circulating in Mallards in Duck Hunting Preserves in Maryland, USA

Duck hunting preserves (DHP) have resident populations of farm-raised mallard ducks, which create potential foci for the evolution of novel influenza A viruses (IAVs). Through an eleven-year (2003–2013) IAV surveillance project in seven DHPs in Maryland, USA, we frequently identified IAVs in the resident, free-flying mallard ducks (5.8% of cloacal samples were IAV-positive). The IAV population had high genetic diversity, including 12 HA subtypes and 9 NA subtypes. By sequencing the complete genomes of 290 viruses, we determined that genetically diverse IAVs were introduced annually into DHP ducks, predominantly from wild birds in the Anatidae family that inhabit the Atlantic and Mississippi flyways. The relatively low viral gene flow observed out of DHPs suggests that raised mallards do not sustain long-term viral persistence nor do they serve as important sources of new viruses in wild birds. Overall, our findings indicate that DHPs offer reliable samples of the diversity of IAV subtypes, and could serve as regional sentinel sites that mimic the viral diversity found in local wild duck populations, which would provide a cost-efficient strategy for long-term IAV monitoring. Such monitoring could allow for early identification and characterization of viruses that threaten bird species of high economic and environmental interest.

Surveillance system for avian influenza in wild birds and implications of its improvement with insights into the highly pathogenic avian influenza outbreaks in Japan

Since the re-emergence of a highly pathogenic avian influenza (HPAI) in 2004, outbreaks of the viral subtypes HPAI, H5N1, H5N8, and H5N6 in wild birds, poultry, and zoo birds have occurred in Japan. In 2008, a nation-wide avian influenza (AI) surveillance program was started for the early detection of the HPAI virus (HPAIV) and for the assessment of HPAIV infection among wild birds. In this study, we aimed to conduct an overview of the AI surveillance system of wild birds in Japan, including those in the regions and prefectures, to assess its overall performance and develop insights on its improvement. We analyzed past surveillance data in Japan and conducted questionnaire surveys for the officers in 11 regional branches of the Ministry of Environment and the nature conservation divisions of 47 prefectures to acquire details regarding those AI surveillance. We found that the early detection of HPAIV in wild birds was successfully achieved in only one of the five outbreak seasons during the 2008-2019 period in Japan, and the assessment of HPAIV infection had possibly not been adequate in the national surveillance system. In the winter season, AI surveillance in most prefectures were mainly conducted by means of passive surveillance through reported dead birds and active surveillance through collected waterbird feces. Conversely, less than half of the prefectures conducted bird monitoring, patrolling in migratory bird habitats, and AI antigen testing in rescued birds. In areas surrounding HPAI occurrence sites (<10 km), bird monitoring and patrolling efforts were enhanced. However, AI testing efforts in waterbird feces and rescued birds were decreased. The AI surveillance for endangered bird species and in national wildlife protection areas was conducted by the branches of the Ministry of Environment and by the prefectures. Based on our results, we concluded that for maximum efficiency, legislation which specialized in wildlife pathogens should be necessary to prepare adequate national budget and testing capacity for appropriate surveillance system with periodical assessment for surveillance results and the system.

Fine-scale tracking of wild waterfowl and their impact on highly pathogenic avian influenza outbreaks in the Republic of Korea, 2014-2015

Wild migratory waterfowl are considered one of the most important reservoirs and long-distance carriers of highly pathogenic avian influenza (HPAI). Our study aimed to explore the spatial and temporal characteristics of wild migratory waterfowl’s wintering habitat in the Republic of Korea (ROK) and to evaluate the impact of these habitats on the risk of HPAI outbreaks in commercial poultry farms. The habitat use of 344 wild migratory waterfowl over four migration cycles was estimated based on tracking records. The association of habitat use with HPAI H5N8 outbreaks in poultry farms was evaluated using a multilevel logistic regression model. We found that a poultry farm within a wild waterfowl habitat had a 3–8 times higher risk of HPAI outbreak than poultry farms located outside of the habitat. The range of wild waterfowl habitats increased during autumn migration, and was associated with the epidemic peak of HPAI outbreaks on domestic poultry farms in the ROK. Our findings provide a better understanding of the dynamics of HPAI infection in the wildlife–domestic poultry interface and may help to establish early detection, and cost-effective preventive measures.

Spatial modeling could not differentiate early SARS-CoV-2 cases from the distribution of humans on the basis of climate in the United States

The SARS-CoV-2 coronavirus is wreaking havoc globally, yet, as a novel pathogen, knowledge of its biology is still emerging. Climate and seasonality influence the distributions of many diseases, and studies suggest at least some link between SARS-CoV-2 and weather. One such study, building species distribution models (SDMs), predicted SARS-CoV-2 risk may remain concentrated in the Northern Hemisphere, shifting northward in summer months. Others have highlighted issues with SARS-CoV-2 SDMs, notably: the primary niche of the virus is the host it infects, climate may be a weak distributional predictor, global prevalence data have issues, and the virus is not in population equilibrium. While these issues should be considered, we believe climate's relationship with SARS-CoV-2 is still worth exploring, as it may have some impact on the distribution of cases. To further examine if there is a link to climate, we build model projections with raw SARS-CoV-2 case data and population-scaled case data in the USA. The case data were from across March 2020, before large travel restrictions and public health policies were impacting cases across the country. We show that SDMs built from population-scaled case data cannot be distinguished from control models (built from raw human population data), while SDMs built on raw case data fail to predict the known distribution of cases in the U.S. from March. The population-scaled analyses indicate that climate did not play a central role in early U.S. viral distribution and that human population density was likely the primary driver. We do find slightly more population-scaled viral cases in cooler areas. Ultimately, the temporal and geographic constraints on this study mean that we cannot rule out climate as a partial driver of the SARS-CoV-2 distribution. Climate's role on SARS-CoV-2 should continue to be cautiously examined, but at this time we should assume that SARS-CoV-2 will continue to spread anywhere in the U.S. where governmental policy does not prevent spread.

Spatial modeling could not differentiate early SARS-CoV-2 cases from the distribution of humans on the basis of climate in the United States

The SARS-CoV-2 coronavirus is wreaking havoc globally, yet, as a novel pathogen, knowledge of its biology is still emerging. Climate and seasonality influence the distributions of many diseases, and studies suggest at least some link between SARS-CoV-2 and weather. One such study, building species distribution models (SDMs), predicted SARS-CoV-2 risk may remain concentrated in the Northern Hemisphere, shifting northward in summer months. Others have highlighted issues with SARS-CoV-2 SDMs, notably: the primary niche of the virus is the host it infects, climate may be a weak distributional predictor, global prevalence data have issues, and the virus is not in population equilibrium. While these issues should be considered, we believe climate's relationship with SARS-CoV-2 is still worth exploring, as it may have some impact on the distribution of cases. To further examine if there is a link to climate, we build model projections with raw SARS-CoV-2 case data and population-scaled case data in the USA. The case data were from across March 2020, before large travel restrictions and public health policies were impacting cases across the country. We show that SDMs built from population-scaled case data cannot be distinguished from control models (built from raw human population data), while SDMs built on raw case data fail to predict the known distribution of cases in the U.S. from March. The population-scaled analyses indicate that climate did not play a central role in early U.S. viral distribution and that human population density was likely the primary driver. We do find slightly more population-scaled viral cases in cooler areas. Ultimately, the temporal and geographic constraints on this study mean that we cannot rule out climate as a partial driver of the SARS-CoV-2 distribution. Climate's role on SARS-CoV-2 should continue to be cautiously examined, but at this time we should assume that SARS-CoV-2 will continue to spread anywhere in the U.S. where governmental policy does not prevent spread.

Using geospatial methods to measure the risk of environmental persistence of avian influenza virus in South Carolina

Avian influenza (AIV) is a highly contagious virus that can infect both wild birds and domestic poultry. This study aimed to define areas within the state of South Carolina (SC) at heightened risk for environmental persistence of AIV using geospatial methods. Environmental factors known to influence AIV survival were identified through the published literature and using a multi-criteria decision analysis with GIS was performed. Risk was defined using five categories following the World Organization for Animal Health Risk Assessment Guidelines. Less than 1% of 1km grid cells in SC showed a high risk of AIV persistence. Approximately 2% - 17% of counties with high or very high environmental risk also had medium to very high numbers of commercial poultry operations. Results can be used to improve surveillance activities and to inform biosecurity practices and emergency preparedness efforts.

Global patterns of avian influenza A (H7): virus evolution and zoonotic threats

Avian influenza viruses (AIVs) continue to impose a negative impact on animal and human health worldwide. In particular, the emergence of highly pathogenic AIV H5 and, more recently, the emergence of low pathogenic AIV H7N9 have led to enormous socioeconomical losses in the poultry industry and resulted in fatal human infections. While H5N1 remains infamous, the number of zoonotic infections with H7N9 has far surpassed those attributed to H5. Despite the clear public health concerns posed by AIV H7, it is unclear why specifically this virus subtype became endemic in poultry and emerged in humans. In this review, we bring together data on global patterns of H7 circulation, evolution and emergence in humans. Specifically, we discuss data from the wild bird reservoir, expansion and epidemiology in poultry, significant increase in their zoonotic potential since 2013 and genesis of highly pathogenic H7. In addition, we analysed available sequence data from an evolutionary perspective, demonstrating patterns of introductions into distinct geographic regions and reassortment dynamics. The integration of all aspects is crucial in the optimisation of surveillance efforts in wild birds, poultry and humans, and we emphasise the need for a One Health approach in controlling emerging viruses such as AIV H7.

BOARD INVITED REVIEW: Prospects for improving management of animal disease introductions using disease-dynamic models

Management and policy decisions are continually made to mitigate disease introductions in animal populations despite often limited surveillance data or knowledge of disease transmission processes. Science-based management is broadly recognized as leading to more effective decisions yet application of models to actively guide disease surveillance and mitigate risks remains limited. Disease-dynamic models are an efficient method of providing information for management decisions because of their ability to integrate and evaluate multiple, complex processes simultaneously while accounting for uncertainty common in animal diseases. Here we review disease introduction pathways and transmission processes crucial for informing disease management and models at the interface of domestic animals and wildlife. We describe how disease transmission models can improve disease management and present a conceptual framework for integrating disease models into the decision process using adaptive management principles. We apply our framework to a case study of African swine fever virus in wild and domestic swine to demonstrate how disease-dynamic models can improve mitigation of introduction risk. We also identify opportunities to improve the application of disease models to support decision-making to manage disease at the interface of domestic and wild animals. First, scientists must focus on objective-driven models providing practical predictions that are useful to those managing disease. In order for practical model predictions to be incorporated into disease management a recognition that modeling is a means to improve management and outcomes is important. This will be most successful when done in a cross-disciplinary environment that includes scientists and decision-makers representing wildlife and domestic animal health. Lastly, including economic principles of value-of-information and cost-benefit analysis in disease-dynamic models can facilitate more efficient management decisions and improve communication of model forecasts. Integration of disease-dynamic models into management and decision-making processes is expected to improve surveillance systems, risk mitigations, outbreak preparedness, and outbreak response activities.

Risk of biodiversity collapse under climate change in the Afro-Arabian region

For 107 endemic mammal species in the Afro-Arabian region, Sahara-Sahel and Arabian Desert, we used ensemble species distribution models to: (1) identify the hotspot areas for conservation, (2) assess the potential impact of the projected climate change on the distribution of the focal species, and (3) assign IUCN threat categories for the focal species according to the predicted changes in their potential distribution range. We identified two main hotspot areas for endemic mammals: the Sinai and its surrounding coastal area in the East, and the Mediterranean Coast around Morocco in the West. Alarmingly, our results indicate that about 17% of the endemic mammals in the Afro-Arabian region under the current climate change scenarios could go extinct before 2050. Overall, a substantial number of the endemic species will change from the IUCN threat category “Least Concern” to “Critically Endangered” or “Extinct” in the coming decades. Accordingly, we call for implementing an urgent proactive conservation action for these endemic species, particularly those that face a high risk of extinction in the next few years. The results of our study provide conservation managers and practitioners with the required information for implementing an effective conservation plan to protect the biodiversity of the Afro-Arabian region.

Identification of high risk areas for avian influenza outbreaks in California using disease distribution models

The coexistence of different types of poultry operations such as free range and backyard flocks, large commercial indoor farms and live bird markets, as well as the presence of many areas where wild and domestic birds co-exist, make California susceptible to avian influenza outbreaks. The 2014-2015 highly pathogenic Avian Influenza (HPAI) outbreaks affecting California and other states in the United States have underscored the need for solutions to protect the US poultry industry against this devastating disease. We applied disease distribution models to predict where Avian influenza is likely to occur and the risk for HPAI outbreaks is highest. We used observations on the presence of Low Pathogenic Avian influenza virus (LPAI) in waterfowl or water samples at 355 locations throughout the state and environmental variables relevant to the disease epidemiology. We used two algorithms, Random Forest and MaxEnt, and two data-sets Presence-Background and Presence-Absence data. The models performed well (AUCc > 0.7 for testing data), particularly those using Presence-Background data (AUCc > 0.85). Spatial predictions were similar between algorithms, but there were large differences between the predictions with Presence-Absence and Presence-Background data. Overall, predictors that contributed most to the models included land cover, distance to coast, and broiler farm density. Models successfully identified several counties as high-to-intermediate risk out of the 8 counties with observed outbreaks during the 2014-2015 HPAI epizootics. This study provides further insights into the spatial epidemiology of AI in California, and the high spatial resolution maps may be useful to guide risk-based surveillance and outreach efforts.

Novel approaches for Spatial and Molecular Surveillance of Porcine Reproductive and Respiratory Syndrome Virus (PRRSv) in the United States

The US swine industry has been impaired over the last 25 years by the far-reaching financial losses caused by the porcine reproductive and respiratory syndrome (PRRS). Here, we explored the relations between the spatial risk of PRRS outbreaks and its phylodynamic history in the U.S during 1998–2016 using ORF5 sequences collected from swine farms in the Midwest region. We used maximum entropy and Bayesian phylodynamic models to generate risk maps for PRRS outbreaks and reconstructed the evolutionary history of three selected phylogenetic clades (A, B and C). High-risk areas for PRRS were best-predicted by pig density and climate seasonality and included Minnesota, Iowa and South Dakota. Phylodynamic models demonstrated that the geographical spread of the three clades followed a heterogeneous spatial diffusion process. Furthermore, PRRS viruses were characterized by typical seasonality in their population size. However, endemic strains were characterized by a substantially slower population growth and evolutionary rates, as well as smaller spatial dispersal rates when compared to emerging strains. We demonstrated the prospects of combining inferences derived from two unique analytical methods to inform decisions related to risk-based interventions of an important pathogen affecting one of the largest food animal industries in the world.