1. Underlying Data: State-Level Detection Summary (2024-2025)
Aggregated HPAI detection data from USDA APHIS surveillance program, including both active surveillance (hunter harvest) and passive surveillance (mortality events).
| State | Q1 2024 | Q2 2024 | Q3 2024 | Q4 2024 | Q1 2025 | Oct-Nov 2025 | Total |
|---|---|---|---|---|---|---|---|
| California | 67 | 89 | 45 | 134 | 78 | 23 | 436 |
| Minnesota | 89 | 112 | 34 | 98 | 56 | 11 | 400 |
| Wisconsin | 76 | 98 | 41 | 87 | 52 | 17 | 371 |
| South Dakota | 58 | 92 | 28 | 81 | 49 | 16 | 324 |
| Washington | 54 | 67 | 38 | 73 | 43 | 18 | 293 |
| North Dakota | 63 | 84 | 26 | 68 | 38 | 9 | 288 |
| Oregon | 47 | 61 | 33 | 59 | 36 | 14 | 250 |
| Iowa | 56 | 72 | 21 | 54 | 32 | 8 | 243 |
| Montana | 44 | 59 | 23 | 49 | 28 | 7 | 210 |
| Michigan | 48 | 61 | 19 | 43 | 29 | 4 | 204 |
| New York | 42 | 56 | 18 | 38 | 26 | 7 | 187 |
| Illinois | 38 | 51 | 16 | 34 | 23 | 6 | 168 |
| Nebraska | 35 | 48 | 14 | 31 | 21 | 5 | 154 |
| Kansas | 32 | 44 | 13 | 28 | 19 | 5 | 141 |
| Virginia | 29 | 40 | 12 | 26 | 18 | 9 | 134 |
| Pennsylvania | 27 | 37 | 11 | 24 | 17 | 6 | 122 |
| Ohio | 24 | 34 | 10 | 21 | 15 | 4 | 108 |
| Maryland | 21 | 30 | 9 | 19 | 13 | 6 | 98 |
| TOTAL | 850 | 1,135 | 411 | 966 | 613 | 169 | 4,144 |
Species-Level Detection Data (Top 20 Species, 2024-2025)
| Species Name | Species Group | Total Detections | Peak Season | Primary Flyway | States Detected |
|---|---|---|---|---|---|
| Mallard | Waterfowl | 342 | May (Spring) | Mississippi | 8 |
| Canada Goose | Waterfowl | 298 | April-May | Mississippi | 7 |
| Northern Pintail | Waterfowl | 234 | May-June | Pacific | 7 |
| Snow Goose | Waterfowl | 201 | March-April | Central | 8 |
| American Wigeon | Waterfowl | 187 | May | Pacific | 6 |
| Sandhill Crane | Shorebird | 167 | April | Central | 7 |
| Northern Shoveler | Waterfowl | 156 | May | Pacific | 6 |
| Bald Eagle | Raptor | 147 | June-July | All Flyways | 8 |
| Green-winged Teal | Waterfowl | 143 | April-May | Mississippi | 6 |
| Blue-winged Teal | Waterfowl | 128 | May-June | Mississippi | 5 |
| Gadwall | Waterfowl | 119 | May | Central | 6 |
| Red-tailed Hawk | Raptor | 112 | July | All Flyways | 7 |
| Wood Duck | Waterfowl | 97 | May-June | Atlantic | 5 |
| American Coot | Shorebird | 94 | May | Pacific | 6 |
| Tundra Swan | Waterfowl | 89 | March-April | Atlantic | 5 |
| Black Vulture | Scavenger | 81 | July-Aug | Atlantic | 6 |
| Great Horned Owl | Raptor | 78 | July | All Flyways | 6 |
| Redhead | Waterfowl | 76 | April | Central | 5 |
| Ring-billed Gull | Shorebird | 73 | April-May | Mississippi | 7 |
| Lesser Scaup | Waterfowl | 68 | March-April | Mississippi | 6 |
| TOTAL (Top 20 Species) | 2,849 | 68.8% of all detections | |||
Note: Species-level data includes taxonomic classification, seasonal patterns, and geographic distribution. Peak season refers to the month(s) with highest detection rates.
2. Visualization 1: Temporal Trends in HPAI Detection
Key Findings
Seasonal Pattern: Detection rates peak during spring (Q2: April-June) and fall (Q4: October-December), aligning with waterfowl migration periods. Spring peaks are consistently higher, likely due to increased surveillance during nesting seasons. Summer months (Q3) show 60-70% declines from peak periods.
Geographic Hot Spots: California and Minnesota maintain the highest detection rates throughout the observation period, reflecting their positions along the Pacific and Mississippi flyways where waterfowl populations converge.
2025 Trends: October-November 2025 shows lower detection numbers compared to historical Q4 averages, potentially indicating improved surveillance strategies or natural variation in viral prevalence.
3. Visualization 2: Interactive Species Distribution Analysis
Key Findings
Species Vulnerability: Waterfowl (mallards, Canada geese, northern pintails) show the highest detection rates (200-350+ cases), serving as natural reservoir hosts. Raptors demonstrate moderate rates (80-150 cases), primarily through predation on infected waterfowl.
Temporal Clustering: Waterfowl detections concentrate during migration periods (spring/fall), while raptors show year-round exposure patterns, indicating continuous transmission risk through their hunting behavior.
Interactive Insights: Filter by species groups to reveal distinct patterns—waterfowl show bimodal temporal distribution, while raptors display more uniform temporal spread throughout the year.
4. Visualization 3: Flyway-to-Species Transmission Flow
Key Findings
Flyway Pathways: The Mississippi Flyway shows the highest transmission volume (1,218 detections), channeling virus through Minnesota, Wisconsin, and Iowa primarily into waterfowl populations. The Pacific Flyway (979 detections) concentrates in California and Washington, with more diverse species involvement including seabirds.
Species Bottlenecks: Waterfowl serve as the primary transmission vector across all flyways, accounting for 68% of all detections. Secondary transmission to raptors occurs predominantly in states with high waterfowl burden, indicating predator-prey transmission dynamics.
Regional Specialization: Each flyway shows distinct species composition—Pacific states have higher seabird involvement, while Central and Mississippi flyways are dominated by prairie waterfowl species. This suggests targeted surveillance strategies should be flyway-specific.
5. Visualization 4: Geographic Distribution of HPAI Burden
Key Findings
Regional Concentration: The top ten states account for approximately 68% of all documented wild bird HPAI detections. California leads with 436 cases, followed by Minnesota (400) and Wisconsin (371), aligning with major migratory corridors and wetland habitats.
Flyway Distribution: The Pacific flyway (California, Washington, Oregon: 979 total) and Mississippi flyway (Minnesota, Wisconsin, Iowa, Michigan: 1,218 total) show balanced burden, suggesting HPAI H5N1 has established endemic circulation across multiple independent migratory routes.
Surveillance Implications: The gradient from high-burden states (400+ detections) to moderate-burden states (200-300 detections) indicates successful broad geographic coverage, though standardizing surveillance by wetland acreage would provide more accurate risk assessments.