Assessing HPAI-H5 transmission risk across wild bird migratory flyways in the United States

Domain: neuroscience
Published: 2026-02-09
Editorial outcome: minor_revision

Abstract
As natural hosts of avian influenza viruses, wild birds pose an increasing threat to public health. Here, using surveillance data from wild bird infections across the United States (2022-2025), we show that HPAI-H5 transmission exhibits strong interspecific variation, seasonality, and spatial heterogeneity linked to migratory flyways. Phylogeographic analysis reveals that viral genotypes evolve from early, limited transmission along single migratory routes to a nationwide dispersal pattern spanning multiple migratory flyways. Anseriformes exhibits the highest number of infections but the lowest transmission risk, whereas Strigiformes demonstrates the greatest transmission risk. The HPAI-H5 transmission in wild birds along migratory flyways exhibits significant spatial heterogeneity and is associated with bird migration. Meteorological conditions are correlated with outbreak timing and may inform early warning efforts; however, these relationships are nonlinear. These findings provide a foundation for risk assessment, early warning systems, and integrated management of avian influenza in wild bird populations.

Introduction
Avian influenza is a disease caused by avian influenza viruses that primarily spreads among birds but can sometimes undergo cross-species transmission to mammals, including humans, making it a zoonotic disease of wide concern1. Highly pathogenic avian influenza (HPAI), particularly the highly pathogenic H5N1 clade 2.3.4.4b, has caused unprecedented outbreaks in wild birds and poultry globally since 2020 and continues to pose potential pandemic risks to public health2. Wild birds, especially long-distance migratory birds, have long been natural hosts of avian influenza and are considered key hosts and vectors for the virus to achieve intercontinental transmission and maintain an endemic circulation. Their complex migration routes, seasonal aggregations, and ecological interactions with multiple species provide ideal conditions for the continuous evolution, reassortment, and geographic spread of the virus3. The United States is located at the intersection of multiple major global migratory bird flyways (Pacific, Central, Mississippi, and Atlantic Flyways) and has diverse ecosystems and high avian biodiversity, making it a critical region for studying the transmission risks of avian influenza in wild birds4. In March 2024, Texas reported the world’s first case of cattle infection with highly pathogenic H5 avian influenza, which rapidly spread to multiple states across the United States, causing intercontinental transmission. Genetic evolutionary analysis suggests that the infecting virus likely originated from wild birds, was transmitted from wild birds to cattle, and then spread between herds through cattle movement5. In November 2025, Washington State confirmed a case of H5N5 infection resulting in death, the world’s first fatal human case caused by this H5 subtype avian influenza virus. The state health department detected the H5N5 avian influenza virus in the patient’s backyard and concluded that poultry or wild birds were likely the source of the infection6. These findings underscore the urgent need for in-depth research on the transmission mechanisms of avian influenza among wild birds.

Although numerous studies have confirmed the predominant role of migratory birds in the intercontinental transmission of HPAI viruses through metagenomic analysis7–9, there is a lack of systematic understanding of the spatiotemporal dynamic evolution and heterogeneous driving factors of the current global epidemic caused by H5N1 clade 2.3.4.4b. First, the role of host heterogeneity is far more complex than previously understood. The traditional transmission concept centered on Anseriformes is being challenged by the reality of highly efficient transmission and high pathogenicity of the virus in nontraditional hosts such as raptors and mammals10. It is essential to quantify the transmission risk of avian influenza in different categories of birds and analyze their roles in the transmission chain. Second, the underlying patterns of spatial heterogeneity are unclear. While existing studies have confirmed viral transmission along North America’s four migratory flyways, the evolutionary patterns of key genotypes within the currently circulating clades are insufficient explored11. Furthermore, whether each flyway develops distinct spatiotemporal hotspot dynamics due to its unique ecological and geographical characteristics constitutes a critical unanswered question for achieving regionalized precision in prevention and control strategies. According to the universal theoretical framework of climate and disease, climate-related disasters are associated with infectious disease risk12. Although the impacts of extreme climate events (e.g., droughts and heatwaves) on bird migration have been widely reported, the specific ecological mechanisms by which such events may drive the transmission of highly pathogenic avian influenza in wild birds remain unclear13. Therefore, drawing on transmission ecology theory, it is useful to prioritize key meteorological factors, such as drought indices and temperature, as covariates and to employ advanced machine-learning models to disentangle these complex relationships.

This study integrates viral genomics, epidemiological modeling, spatiotemporal heterogeneity analysis, and machine-learning approaches to provide a systematic, multidimensional assessment of HPAI-H5 outbreaks in wild birds in the United States. The results show that viral genotypes evolve from early, limited spread along a single flyway to a diffusion pattern spanning multiple migratory flyways, and this pattern is associated with migratory bird movements. Although Anseriformes account for the largest number of infections and exhibit marked seasonality, they show the lowest transmission risk, whereas Strigiformes display the highest transmission risk. HPAI-H5 activity in wild birds across the United States exhibits a pronounced autumn–winter peak, and transmission hotspot regions shift with bird migration; within each flyway, transmission risk differs between migratory and non-migratory birds. Random forest models indicate nonlinear relationships between meteorological factors and infection risk in wild birds, reflecting heterogeneous impacts of climate change on avian influenza virus circulation. Collectively, these findings provide new insights into the complex transmission ecology of HPAI-H5 among wild bird populations across North America’s four major flyways, and offer data-driven evidence and a theoretical basis to support risk assessment and scientifically targeted prevention and control efforts.

Results
Spatial dissemination of HPAI-H5 genotypes in the United States
A total of 3867 valid gene sequences of HPAI-H5 subtype viruses from wild birds in the United States were screened from the GISAID database from January 2022 to April 2025. All sequences belonged to the clade 2.3.4.4b evolutionary lineage. In terms of subtype composition, H5N1 virus sequences constituted the vast majority (n=3855), with an additional 12 sequences being H5N5 viruses. The wild bird hosts were classified into different orders on the basis of biological taxonomy, and a Sankey diagram (Supplementary Fig. 1) was constructed to display the associations and flow patterns of various genotypes between different years and bird orders. The results revealed significant inter-annual variations in the number of submitted samples. The number of sequences submitted reached its peak in 2022 (n=1934), while the lowest number of sequences was recorded in 2024 (n=371). In terms of host distribution, Accipitriformes was the primary host group for viral infection (n=1622), followed by Anseriformes (n=747) and Strigiformes (n=422). The dominant viral genotypes and their host preferences exhibited clear annual evolutionary patterns. The greatest viral genotype diversity was detected in 2022, with multiple cocirculating genotypes; however, the data from 2025 revealed that D1.1 (n=717) became the absolute dominant genotype, with its transmission mainly concentrated in Accipitriformes (n=389). Based on the eight genotypes with the largest number of sequences during the study period, discrete phylogeographic reconstructions were performed (Fig. 1). The results showed that the spatial dissemination of HPAI-H5 genotypes across the United States was highly consistent with the major migratory flyways, although distinct diffusion patterns were observed among different genotypes. A1 and A2 genotypes showed transmission primarily concentrated along the Atlantic Flyway, with multiple migration events occurring among northeastern coastal states and relatively limited westward spread. In contrast, B1-lineage viruses (B1.1 and B1.3) exhibited a much broader connectivity pattern, spanning both the Atlantic and Mississippi Flyways and forming dense East–Midwest transmission networks and frequent bidirectional exchange between coastal and inland states. The B2.1 genotype displayed a pronounced inland focal structure, with transmission centered in North Dakota and Minnesota, consistent with a pattern of spread primarily within the Mississippi Flyway. Among all lineages, the B3 (B3.2 and B3.6) and D1.1 genotypes exhibited the widest geographic dissemination, forming extensive and multidirectional transmission networks that spanned the entire United States. The two B3 genotypes generated dense transmission links connecting the Great Lakes region, the central plains, the Gulf Coast and several western states, whereas D1.1, despite also producing wide-ranging movements, maintained a stronger and more stable presence in the Northeast. The spatial scale and orientation of these diffusion patterns indicate that their spread is not consistent with a single migratory flyway; instead, the nationwide distribution of these genotypes reflects the combined influence of the Atlantic, Mississippi, Central, and Pacific Flyways.

Notably, Washington State has detected dozens of avian influenza cases in wild birds, waterfowl, and backyard poultry over the past few weeks, which aligns with the surge in outbreak activity nationwide. Genetic evolutionary analysis revealed that the virus from the world’s first fatal human H5N5 case reported in Washington State in November 2025 was highly homologous to H5N5 sequences from wild bird sources (Supplementary Fig. 2). The human isolate presented as a distinct terminal branch nested within contemporaneous wild bird sequences, indicating a zoonotic spillover event. The human-derived sequence did not exhibit significant topological changes or fundamental positional alterations that typically signify adaptive evolution of the virus toward mammalian hosts. The close phylogenetic relationship with the great black-backed gull isolate suggests that the patient may have had direct or indirect contact with infected wild birds. This finding underscores the necessity and urgency of strengthening avian influenza surveillance in wild birds.

Analysis of the transmission risk of different wild bird categories
From January 2022 to April 2025, a total of 12,808 wild birds infected with the H5 subtype of avian influenza were collected. In accordance with the biological classification of the orders, the different species of wild birds were categorized into different orders (Fig. 2A). The results revealed that the top five orders of infected wild birds in the United States were Anseriformes (9040 birds), Accipitriformes (1953 birds), Strigiformes (445 birds), Passeriformes (405 birds), and Charadriiformes (395 birds). Further analysis was conducted to plot the monthly proportion of infections for different orders of birds (Fig. 2B). Infections in Anseriformes exhibited a clear seasonal pattern, with the highest proportion of infections occurring in autumn and winter each year. The transmission capacity of different orders was quantified using the basic reproduction number (R0). The results revealed (Fig. 2C) that Strigiformes had the strongest transmission capacity, with a basic reproduction number of 3.164 (95% CI: 2.711–3.726), whereas Anseriformes had the weakest transmission capacity, with a basic reproduction number of 0.992 (95% CI: 0.990–0.994). In addition, the basic reproduction number for Accipitriformes was 1.655 (95% CI: 1.571–1.748), that for Passeriformes was 1.029 (95% CI: 1.019–1.039), and that for Charadriiformes was 1.279 (95% CI: 1.198–1.369).

Time series decomposition and seasonal variation in different orders
A time series chart of the monthly infections for the top five orders of wild birds with the greatest numbers of infections was constructed, with a smoothing curve added to visually display the trend of changes (Fig. 3). During the entire study period, avian influenza outbreaks among wild birds in the United States clearly exhibited regular patterns. Among them, the pattern for Anseriformes (migratory birds) was consistent with the overall infection pattern, while the infection patterns for other orders were not as apparent.

The Seasonal-Trend decomposition using LOESS (STL) results show that avian influenza outbreaks among wild birds exhibited clear trends and seasonality (Supplementary Fig. 3). Avian influenza outbreaks among wild birds occur annually, with a seasonal span of six months. Infections in wild bird orders, except for Passeriformes, have shown a continuous downward trend, while Passeriformes were more severely affected in 2024. The seasonality of infections in Anseriformes aligned with the overall seasonal pattern of wild birds.

The time series was divided into the four seasons of the year, with spring and autumn being the migration seasons for migratory birds in the United States, and a seasonal boxplot was created (Fig. 4). During autumn and winter, the number of infections was twice that during spring and five times that during summer. The results of the Kruskal–Wallis test conducted between different seasons revealed that the overall infection count had a chi-square value of 14.66 (P<0.05), indicating significant differences in infection numbers between different seasons. Among them, autumn had the highest number of infections, with a median and interquartile range of 658 (196, 868), whereas summer had the lowest, at 39 (14, 137). Pairwise comparisons between different seasons were conducted using Dunn’s test with Bonferroni correction. The results revealed significant differences in infection numbers between autumn and summer and between winter and summer, whereas the differences between the other seasons were not significant. Further analysis of wild birds of different orders revealed that Anseriformes had significant differences in H5 infection numbers between spring and autumn, spring and winter, summer and autumn, and summer and winter. However, the differences between seasons were not statistically significant for Accipitriformes, Passeriformes, Charadriiformes, or Strigiformes.

Seasonal analysis of avian influenza in wild birds across different migratory flyways
In the bar chart showing the number of wild birds infected with H5 avian influenza across different migratory flyways by month (Supplementary Fig. 4), wild bird infections across different flyways were shown to exhibit distinct patterns of fluctuation and trends. The STL decomposition results (Supplementary Fig. 5) revealed that wild bird infections along the Pacific Flyway and Atlantic Flyway tended to decrease, whereas those along the Central Flyway tended to decrease starting in mid-2022, with the trend gradually stabilizing thereafter. The Mississippi Flyway exhibited a pattern of initial decrease in wild bird infections followed by a gradual increase. The birds along the four migratory flyways in the United States were categorized into migratory and non-migratory species, and boxplots of avian influenza infections across different flyways and seasons were generated, with differential analysis performed (Fig. 5).

Along the Pacific Flyway, overall wild bird infections significantly differed across seasons (chi-square value: 11.45, P<0.05). Pairwise comparisons revealed differences in infection numbers between spring and autumn. For migratory birds along this flyway, there were also significant differences in infection numbers between spring and autumn, while non-migratory birds showed no statistically significant differences in infection numbers across different seasons. Along the Central Flyway, there were no statistically significant differences in H5 avian influenza infection numbers among wild birds across different seasons, whether considering the overall population or migratory and non-migratory birds separately. Along the Mississippi Flyway, there were no statistically significant differences in overall avian influenza infections or non-migratory bird avian influenza infections across different seasons. However, among migratory birds, there were differences in avian influenza infections across seasons (chi-square value: 9.02, P=0.03). Pairwise comparisons indicated that this difference was primarily driven by different infection numbers between summer and autumn. Along the Atlantic Flyway, overall wild bird infections significantly differed across seasons (chi-square value: 9.33, P=0.03). Pairwise comparisons revealed differences in infection numbers between summer and winter. For migratory birds along this flyway, there were also significant differences in infection numbers between spring and winter as well as between summer and winter, while non-migratory birds showed no statistically significant differences in infection numbers across different seasons.

Analysis of transmission risk among wild birds across different migratory flyways
The transmission capacity of H5 subtype avian influenza was evaluated for overall wild bird infections and migratory and non-migratory wild bird infections across different migratory flyways, and quantified using the basic reproduction number (Fig. 6). Along the Pacific Flyway, the basic reproduction number for overall H5 subtype avian influenza was 1.009 (95% CI: 1.005–1.012); for migratory birds it was 1.010 (95% CI: 1.007–1.014), and for non-migratory birds it was 1.011 (95% CI: 1.002–1.020). Because the confidence intervals for different bird types overlapped, there were no differences in transmission capacity among different categories of wild birds along this flyway. Along the Central Flyway, there was no difference between the overall H5 subtype avian influenza transmission capacity (R0: 1.099, 95% CI: 1.081–1.116) and the transmission capacity of migratory birds (R0: 1.106, 95% CI: 1.087–1.125), but both were greater than the transmission capacity of non-migratory birds (R0: 1.028, 95% CI: 1.005–1.052). Along the Mississippi Flyway, non-migratory birds had the strongest H5 subtype avian influenza transmission capacity (R0: 6.414, 95% CI: 4.972–8.398), which was much greater than the transmission capacity of migratory birds (R0: 1.071, 95% CI: 1.057–1.085). Along the Atlantic Flyway, the overall H5 subtype avian influenza basic reproduction number was less than 1 (R0: 0.996, 95% CI: 0.995–0.998), with non-migratory birds having the highest basic reproduction number at 1.112 (95% CI: 1.086–1.138) and migratory birds having a basic reproduction number of 1.002 (95% CI: 1.000–1.003).

Spatiotemporal distribution analysis of HPAI-H5 avian influenza in wild birds
Spatiotemporal distribution maps of H5 avian influenza in wild birds across the United States in different years and seasons were created (Supplementary Fig. 6). Throughout the study period, all states reported wild bird infection cases, with Florida having the highest number of infected wild birds at 782. In 2022, Minnesota had the most infections (587 birds); in 2023, California had the most infections (213 birds); in 2024, Florida had the most infections (201 birds); and in 2025, Maine had the most infections (169 birds). The infection situation exhibited a decreasing trend year by year, and the states with severe infections were all located in coastal areas of the United States. Spatial clustering analysis was conducted for all birds in the United States and for migratory and non-migratory birds across different seasons, and the results are shown in Table 1.

Overall, wild birds nationwide and migratory wild birds exhibited spatial clustering (the non-random geographical distribution of H5 infections, where the proximity of high-prevalence locations exceeds what would be expected by chance) in spring, summer, and autumn, while non-migratory wild birds exhibited spatial clustering year-round and in autumn. The local indicators of spatial association (LISA) cluster map and cold spot-hotspot analysis map (Fig. 7) revealed that in spring, infections in overall birds and migratory birds formed hotspot areas (statistically significant high-value areas) in North Dakota, South Dakota, Minnesota, and Wisconsin, indicating high-high clustering. In summer, the hotspot areas shifted to the northwest, appearing in Washington, Oregon, and Idaho. In addition, Alaska showed high-low clustering phenomena in both spring and summer. In winter, for overall and migratory birds, Kansas showed high-low clustering phenomena, whereas non-migratory bird infections exhibited high-low clustering in Texas.

Spatiotemporal distribution analysis of HPAI-H5 avian influenza along migratory flyways
Further spatiotemporal analysis of H5 avian influenza in wild birds across major migratory flyways revealed substantial differences in the timing and geographic distribution of infections among the flyways. With respect to the Pacific Flyway, the global spatial autocorrelation results indicate that different types of wild birds did not exhibit significant spatial clustering across different seasons. Analysis of local spatial clustering patterns (LISA and Getis-Ord Gi*) revealed significant local clustering and seasonal dynamics in the geographical distribution of viral infections (Supplementary Figs. 7 and 8). During spring, Montana was identified as exhibiting high clustering, with Montana, Washington, and Idaho constituting hotspot areas for viral infections, whereas non-migratory bird infections in California and Arizona represented the primary cold spot areas. During summer, viral hotspot areas underwent spatial shifts, with California becoming the primary hotspot area, while Oregon and Montana served as infection hotspots for migratory birds. In autumn, the spring hotspot areas for non-migratory birds transformed into cold spot areas, and during winter, Washington became the hotspot area for non-migratory bird infections. In the Central Flyway, non-migratory birds significantly differed in terms of the Moran’s I index for the entire year and autumn. LISA clustering maps (Supplementary Fig. 9) and cold spot-hotspot maps (Supplementary Fig. 10) indicate that hotspot areas were driven primarily by non-migratory birds, resulting in the formation of clustered regions covering Wyoming, Colorado, and New Mexico. During winter, infections in all birds and migratory birds along this flyway exhibited hotspot areas in Wyoming and New Mexico. On the Mississippi Flyway, the spread of H5 avian influenza virus exhibited strong spatial autocorrelation. Global Moran’s I index analysis revealed that avian influenza infections in total birds and migratory birds exhibited significant spatial clustering patterns during spring, summer, and autumn. In addition, migratory birds demonstrated significant spatial clustering at the annual scale. For non-migratory birds, their spatial clustering effects were primarily concentrated in spring and autumn. According to the LISA clustering map (Supplementary Fig. 11) and cold spot-hotspot map (Fig. 8), Wisconsin exhibited that both total birds and migratory birds formed stable high-high clustering areas, indicating that this state is a key hotspot for viral transmission. During spring, the southern regions of this flyway were all cold spot areas, whereas Minnesota in the northern part of the route was identified as a hotspot area for both migratory and non-migratory bird infections. At the beginning of summer, the infection hotspots further concentrated northwards and were primarily distributed in the northern regions of the flyway. During autumn, Minnesota, Iowa, and Wisconsin were hotspot areas for total and migratory bird infections, whereas Mississippi and Alabama were hotspot areas for non-migratory birds. By winter, the bird infection hotspot areas all shifted to the southern regions of this flyway. On the Atlantic Flyway, migratory birds showed significant spatial clustering during summer, whereas non-migratory birds exhibited significant spatial clustering annually as well as during summer and autumn. According to the LISA clustering map (Supplementary Fig. 12) and cold spot-hotspot map (Supplementary Fig. 13), annual-scale analysis confirmed that Georgia and Florida are two key viral transmission core areas in the southern part of this flyway, constituting persistent infection hotspots. During summer, migratory bird infection hotspot areas were in the north, whereas non-migratory bird infection hotspot areas were in the south, indicating a clear north‒south spatial differentiation. During autumn and winter, non-migratory bird infection hotspot areas remained in Florida and Georgia, whereas migratory birds exhibited no hotspot areas.

Analysis of meteorological factors based on Random Forest and partial dependence
A Random Forest regression model was established with wild bird infection counts as the dependent variable and selected meteorological factors as independent variables. The three hyperparameters of the Random Forest model were optimized: ntree (number of trees), maxnodes (maximum depth of individual trees), and mtry (number of variables randomly selected at each split). The optimal parameter combination selected was ntree=500, maxnodes=32, and mtry=5. Tenfold cross-validation was performed to obtain the optimal model, and the coefficient of determination was calculated to be 0.87. On the basis of the variable importance calculated from the Random Forest model, higher values indicate greater variable importance (Supplementary Table 1). Based on the current dataset, the variables ranked from highest to lowest impact were: Palmer Drought Severity Index, Palmer Modified Drought Index, average temperature, precipitation, Palmer Z-Index, and cooling degree days.

Partial dependence and individual conditional expectation (ICE) with 500 simulations revealed a nonlinear relationship between infection counts and meteorological factors (Fig. 9). Both the average temperature and the number of cooling degree days monotonically decreased with the increasing number of infections. When the number of cooling degree days exceeded 100 days, the number of wild bird infections exhibited little change as the values increased. The Palmer Drought Severity Index and Palmer Modified Drought Index exhibited an inverted U-shaped relationship with the number of infections, with the highest number of infections occurring when the Palmer Drought Severity Index or Palmer Modified Drought Index was approximately −3.3, followed by a rapid decrease in wild bird infections as the values increased. The Palmer Z-Index and precipitation exhibited a U-shaped relationship with the number of wild bird infections, with the lowest number of wild bird infections occurring when the Palmer Z-Index was between −3 and 0 or when precipitation was ~2.8 inches. Beyond this range, the number of wild bird infections gradually increased as the values either increased or decreased.

Methods
Date collection
In this study, HPAI-H5 virus genome sequences obtained from wild bird samples from the United States from January 2022 to April 2025 from the Global Initiative on Sharing All Influenza Data (GISAID) database were screened24. The data entries included in the analysis contained detailed metadata, primarily including sample collection date, sequence accession number, virus subtype, genotype, host species, and viral evolutionary lineage. Data on the number of wild birds infected with H5 subtype avian influenza from January 2022 to April 2025 were collected from the United States Department of Agriculture (USDA). The data include the date of wild bird sampling, the state where sampling occurred, and the types of wild birds25. Meteorological data were obtained from the National Centers for Environmental Information (NCEI), including average temperature (°F), precipitation (inch), cooling degree days (°Df), Palmer Drought Severity Index, Palmer Modified Drought Index, and Palmer Z-Index for the entire United States from January 2022 to April 2025. These data can be directly downloaded from the website and represent monthly average data weighted by land area or population. The specific algorithms can be found in the official documents from the NCEI26. When wild bird data from different orders and months were classified and statistically analyzed, if no H5 samples were detected for a particular order of wild birds, the count was recorded as 0; otherwise, calculations were performed on the basis of the original existing data. However, wild birds with unknown sampling dates that existed in the original data were removed. During the study period, there were no missing values in the meteorological data. On the basis of bird information from “Birds of the World”27, infected wild birds were classified into two categories: migratory birds and non-migratory birds. With respect to wild bird data for which bird names were ambiguous and difficult to determine, these entries were excluded from the analysis of migratory and non-migratory birds.

Statistical analysis
Spatial dissemination of HPAI-H5 genotypes in the United States. The spatial dissemination of HPAI-H5 genotypes in the United States was analyzed using a discrete phylogeographic framework implemented in BEAST (v1.10.5). Whole-genome sequences collected from 2022 to 2025 were aligned with MAFFT and screened using IQ-TREE (v2.1.4) before genotype-specific datasets were constructed (Supplementary Method 1). In addition, based on information from the U.S. Fish & Wildlife Service and the United States Department of Agriculture websites25,28, migratory flyways in the United States were classified into four routes—the Pacific, Central, Mississippi, and Atlantic flyways—to illustrate the status of different migratory pathways. In November 2025, a fatal case of H5N5 infection occurred in the United States. To understand the association between this fatal case and virus infections in wild birds, HA gene sequences of H5N5 isolates from the United States between 2022 and 2025 were collected and aligned using MAFFT, followed by manual trimming to remove low-quality regions. Maximum likelihood phylogenetic inference was performed in IQ-TREE 2 using the optimal substitution model selected by ModelFinder, with branch support assessed through 1000 ultrafast bootstrap replicates. The resulting maximum likelihood tree was visualized and color-annotated in iTOL. Branch lengths represent the number of nucleotide substitutions per site.

Analysis of serial interval and transmission risk. The serial interval refers to the time interval between the onset of symptoms in a primary case and the onset of symptoms in a secondary case. It follows a certain distribution, and the generation time distribution is a critical step in calculating the basic reproduction number using statistical models. The basic reproduction number is an important metric in epidemiology used to measure the transmission capacity of an infectious disease. It refers to the average number of individuals directly infected by a single infectious individual during their entire infectious period in a completely susceptible population29. In this study, the sample collection date of wild birds was used as a proxy indicator for symptom onset time, representing the time point when infection was detected, which can effectively reflect the actual timing of an epidemiological event. On the basis of the sampling time data, corresponding epidemic curves were constructed, and the est.GT function in the “R0” package was used to estimate the serial interval distribution of the wild birds. On this basis, the package’s function (est.R0.EG) was further applied to estimate the basic reproduction number and its 95% confidence interval for different bird orders and different flyways using an exponential distribution30.

Generalized additive model and loess regression model. A generalized additive model (GAM) is used to reflect the nonlinear relationships between variables. By estimating the influence of each independent variable using individual smooth spline functions, the GAM allows independent nonlinear modeling of the effect of each independent variable while maintaining the linear interpretability of the overall model. The GAM consists of different additive terms, each of which is estimated using a single smooth spline function to explain how the dependent variable changes with the independent variables31. Locally estimated scatterplot smoothing (LOESS) is a nonparametric regression method primarily used to capture trends in data through smooth curves. It combines the concepts of locally weighted regression and polynomial fitting, making it suitable for exploring data with nonlinear relationships32. In this study, GAM and LOESS regression models were used to smooth the original time series data of the top five wild birds with the greatest number of infections in the United States (Supplementary Method 2).

STL decomposition. STL decomposition is a time-series decomposition method based on local weighted regression. It smooths the time series through local weighted regression and iteratively decomposes the time series into three components: trend, seasonality, and residual33. The basic formula is as follows:

Y t = T t + S t + R t (1)

where Y t is the observed value of the time series, T t is the trend component, S t is the seasonal component, and R t is the residual component. In this study, STL decomposition was used to analyze the H5 avian influenza data of the top five wild bird orders with the highest infection numbers in the United States to determine whether there were any seasonality or trends. Similarly, STL decomposition was performed on avian influenza data for wild birds along the different flyways. In this study, March to May was set as spring, June to August as summer, September to November as autumn, and December to February of the following year as winter. Box plots were used to compare and analyze the differences in H5 avian influenza counts among wild birds of different orders across different seasons (Supplementary Method 3).

Spatial autocorrelation analysis. Spatial autocorrelation is a statistical method used to measure the distribution characteristics and mutual relationships of spatial data (Supplementary Method 4). The local Moran’s I index is used to reveal local indicators of spatial association. When the local Moran’s I index is significant, four types of spatial correlation states exist: high-high aggregation areas, high-low aggregation areas, low-high aggregation areas, and low-low aggregation areas34. Hotspot analysis is used to study the clustering patterns and locations of high- or low-value features in local spaces, and the G* i statistic for each feature in the dataset is calculated to determine the state of spatial clustering. In this study, R 4.5.0 was used to create spatial distribution maps for different periods and seasons and to generate spatial clustering maps and cold spot-hotspot analysis maps to calculate Moran’s I index for H5 avian influenza infections in migratory and non-migratory birds across the United States and different flyways.

Random Forest algorithm and partial dependence analysis. The Random Forest algorithm is a classic machine learning algorithm that uses the bagging method to extract multiple sample sets (Supplementary Method 5). Partial dependence represents the result of individual conditional expectations converging to the mean, reflecting the overall effect of the variable, while ICE reflects the relationship for each individual observation3. In this study, Random Forest regression was used to determine the relationships between meteorological factors and the number of wild bird infections. Monthly meteorological data for the United States were used as the input layer of the Random Forest model, while the monthly number of wild bird infections nationwide served as the output layer to train the model. Grid search and cross-validation were used to identify optimal parameters, and the final model was fitted on the basis of these optimal parameters (Supplementary Method 5).

Ethical approval
As this study did not involve patients or the public, no formal ethical evaluation or informed consent was required.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
The authors declare that the data supporting the findings of this study are available in the paper and the Supplementary Information file. The raw data and code have been uploaded to GitHub (https://github.com/kfang0/wild-bird-analysis-and-risk-assessment). Details on how to access the previously reported H5 sequencing data analyzed in this study (including GISAID accession numbers) are provided in the Source Data file accompanying this manuscript. For further information, please contact the corresponding author. Source data are provided with this paper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Please find my review comments attached.", "reviewer_2": "Authors used wild bird influenza A virus surveillance data reported by the USDA to conduct spatio-temporal analysis and evaluate climate factors that may influence IAV transmission. While the analysis and publication of summaries of these data are important to build the knowledge base around HPAI, there are too many issues with the manuscript in it’s current form for this reviewer to recommend publication in this journal. Data was cited as being downloaded from CDC, however the data is maintained by the USDA, with the CDC linking to the USDA page on their website. In addition, several statements in introduction and discussion need addressed. In the spatial analysis there is no mention of the Mississippi Flyway, which accounts for the largest number of migratory waterfowl in North American. It is unacceptable to not include this flyway in the analysis."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "I would like to begin by commending the authors for their substantial and diligent efforts in addressing the concerns raised during the initial round of peer review. The revised manuscript demonstrates significant improvements in methodological transparency and analytical rigor, particularly through the inclusion of additional sensitivity analyses and clarified procedural descriptions.\n\nHowever, while the manuscript has certainly matured, several critical conceptual and interpretive issues persist. For a high-impact journal such as Nature Communications, it is imperative that the narrative not only reflects the statistical results but also adheres to the highest standards of epidemiological reasoning and linguistic precision. Currently, some responses remain partially convincing, as they address the \"how\" of the implementation without fully resolving the \"why\" or the broader implications.\n\nThe following points require further refinement before the manuscript can be considered for publication.\n\nMajor Comments\n1. Linguistic Precision: Association vs. Causality\nThe authors explicitly state in their rebuttal that the study design is inherently associational. However, this acknowledgment is not yet fully or consistently reflected in the manuscript's prose.\n\nThe frequent use of active, unidirectional verbs—such as \"drives,\" \"leads to,\" or \"results in\"—implicitly suggests a causal relationship that the study design cannot formally support. Merely stating in a cover letter that causality is not claimed does not suffice if the public-facing text invites such an inference.\n\nI urge the authors to conduct a comprehensive \"linguistic audit\" of the Abstract, Discussion, and Conclusion. Phrases implying directionality or causal mechanisms should be replaced with more nuanced, associational terminology (e.g., \"is associated with,\" \"is linked to,\" or \"co-occurs with\") to align the narrative with the study's evidentiary limits.\n\n2. Epidemiological Rationale for Model Selection\nWhile the statistical soundness of the modeling framework is no longer in question, the epidemiological justification for specific structural choices remains underdeveloped.\n\nThe current manuscript describes the implementation of covariates well, but it lacks a robust defense of why these specific variables were prioritized over other biologically or ecologically plausible alternatives. A Nature Communications audience expects a bridge between statistical selection (e.g., AIC/BIC or LASSO) and domain-specific theory.\n\nPlease provide a more detailed rationale for the model structure. Specifically, explain how the chosen covariates align with the current understanding of transmission ecology or the specific dynamics of the system under study. This context is vital for multidisciplinary readers to interpret the biological relevance of the findings.\n\n3. Defining the Boundaries of Generalisability\nThe authors position their framework as a broadly applicable tool for surveillance and early warning. While the framework is indeed promising, the current discussion does not sufficiently delineate its boundary conditions.\n\nThe response to previous concerns regarding spatial and temporal specificity felt somewhat generic. It is essential to distinguish between the framework’s procedural generalisability and the resultant context-specificity of the findings.\n\nI recommend adding a dedicated subsection or a focused paragraph in the Discussion addressing \"External Validity and Scope.\" Clearly delineate which conclusions are likely system-specific and which aspects of the framework are expected to hold across different ecological or regional contexts.\n\n4. Synthesizing Uncertainty into the Narrative\nStatistical quantification of uncertainty is now well-presented; however, the interpretation of that uncertainty remains somewhat siloed within the results section.\n\nThere remains a slight disconnect where the narrative conclusions occasionally sound more definitive than the underlying data warrants, particularly where confidence intervals are wide or model agreement is modest.\n\nEnsure that the narrative interpretation in the Discussion explicitly reflects the quantified uncertainty. When identifying hotspots or ranking risks, please use qualifying language that acknowledges the degree of confidence, thereby avoiding overconfident policy-relevant statements that might misguide stakeholders.\n\nMinor Points\nMethodological Conciseness: To improve readability, certain highly technical or repetitive methodological descriptions currently in the main text should be migrated to the Supplementary Information. This will allow the primary narrative to flow more effectively.\n\nVisual Consistency and Clarity: - Ensure that all figures (especially maps and ensemble plots) explicitly display uncertainty measures (e.g., credible intervals or variability across model runs).\n\nHarmonize color scales and legends across all panels to facilitate direct comparison between different scenarios or time points.\n\nTerminological Consistency: There is still some overlap in the use of \"hotspot,\" \"high-risk area,\" and \"cluster.\" Please provide a brief, clear definition of these terms early in the text and use them consistently throughout.", "reviewer_2": "Authors substantially added to and revised the manuscript, providing excellent clarification and filling gaps present in the first draft. Thank you for your hard work to improve the quality of this manuscript to add useful information to useful information to the IAV knowledge bank!\n\n[ATTACHED REVIEWER REPORT — IMPORTANT]\n\nReviewer #1:\nThis paper presents a comprehensive analysis of HPAI-H5 transmission in wild birds in the United States, utilizing various epidemiological and machine learning techniques. The study addresses an important and timely topic, especially given the recent spillover events to mammals. The use of a multi-faceted approach, combining transmission capacity estimation, spatio-temporal analysis, and meteorological factor analysis, provides valuable insights into the dynamics of H5 avian influenza. The findings regarding the differing transmission capacities among bird orders, the strong seasonality, and the identification of hotspots are significant for public health and wildlife management. The application of machine learning to understand non-linear relationships with meteorological factors is also a strength.\n\nA significant finding is the varied transmission capacity among different wild bird orders. Specifically, Strigiformes showed the strongest transmission capability (R =3.164) while 0 Anseriformes, despite being a primary concern for avian influenza due to their migratory patterns, had the weakest (R =0.992). This challenges conventional assumptions about transmission in 0 migratory waterfowl and highlights the need for a more nuanced understanding of avian influenza ecology. Additionally, the study clearly demonstrates a pronounced seasonal pattern in H5 infections, with autumn and winter exhibiting twice the number of infections compared to spring, and five times that of summer. This consistent seasonality provides valuable information for surveillance and control planning. The analysis reveals complex, nonlinear relationships between meteorological factors and H5 infections. Notably, average temperature, precipitation, and cooling degree days showed a monotonically decreasing relationship with infection counts, while drought indices (PDSI and PMDI) exhibited an inverted \"U-shaped\" relationship, and the Palmer Z Index a \"U-shaped\" relationship. This moves beyond simple linear correlations and offers a more realistic understanding of environmental influences.\n\nThe work is of significant value to veterinary epidemiology, wildlife health, and public health, especially given the recent H5N1 spillover events to mammals, including cattle and humans. By linking HPAI-H5 transmission to various meteorological indices, the study contributes to understanding the broader impact of climate change on infectious disease dynamics in wildlife, a critical and evolving area of research.\n\nHowever, there are several areas where the paper could be improved for clarity, rigor, and impact."}, "author_response": "Thank you very much for your meticulous review and valuable comments on this research. Your acknowledgment of the importance of the research topic, the advantages of the multidisciplinary approach, and the key findings (such as differences in transmission capacity among different bird orders, seasonal patterns, and nonlinear effects of meteorological factors) is highly encouraging to us. Your evaluation not only affirms the value of this research in the fields of veterinary epidemiology, wildlife health, and public health, but also provides direction for us to further refine our paper. Regarding your comment that \"the paper still has room for improvement in terms of clarity, rigor, and impact,\" we take this very seriously and have made improvements based on your suggestions. Regarding the issue you mentioned about the smoothing methods for different models in Figure 2 of the article, we have provided detailed supplementation in the methodology section of the article, explaining that GAM or Loess regression models need to be adopted for different epidemic curves. For the non-parametric tests across different seasons, we have revised this section by first conducting overall Kruskal-Wallis tests for different wild bird species across different seasons, followed by pairwise comparisons with Bonferroni correction. For the definition of spatial weights, we have also provided detailed descriptions in the methodology section to facilitate reader reproduction. Regarding the issue of exaggerating the United States' control of avian influenza virus in wild birds, we have also made modifications according to your suggestions to make the conclusions more objective. For \"symptom onset time,\" we have also provided a definition and made modifications in both the methods and results sections. For the spelling errors that appeared in the article, we have corrected them one by one. Additionally, we also had native English-speaking medical researchers polish the article to ensure more scientific and accurate language. Regarding the association between meteorological factors and wild birds, we have also detailed the sources of meteorological factors and explained how the data were obtained and how they were associated with wild bird avian influenza infections. Regarding the final grid search and hyperparameter tuning, we have added more important hyperparameters to the original foundation, adopted ten-fold cross-validation to obtain the optimal model, calculated variable importance, re-analysed this section, and also added the coefficient of determination (R²) to evaluate model fitting performance.\n\nI believe that these adjustments to the manuscript will greatly enhance the clarity and statistical rigor of the manuscript, and will be more conducive to readers' reproduction of the article. The following is a point-by-point response to the review comments, where the bold text represents the original review comments, and we will provide detailed responses below each review comment and revise the corresponding sections of the article according to the review comments. All revised sections in the manuscript have been highlighted in yellow to facilitate clear identification of the modifications.\n\n[...]"}]

Decision letter