Reported Global Avian Influenza Detections Among Humans and Animals During 2013-2022: Comprehensive Review and Analysis of Available Surveillance Data
Bibliographic record
Abstract
BACKGROUND: Avian influenza (AI) virus detections occurred frequently in 2022 and continue to pose a health, economic, and food security risk. The most recent global analysis of official reports of animal outbreaks and human infections with all reportable AI viruses was published almost a decade ago. Increased or renewed reports of AI viruses, especially high pathogenicity H5N8 and H5N1 in birds and H5N1, H5N8, and H5N6 in humans globally, have established the need for a comprehensive review of current global AI virus surveillance data to assess the pandemic risk of AI viruses. OBJECTIVE: This study aims to provide an analysis of global AI animal outbreak and human case surveillance information from the last decade by describing the circulating virus subtypes, regions and temporal trends in reporting, and country characteristics associated with AI virus outbreak reporting in animals; surveillance and reporting gaps for animals and humans are identified. METHODS: We analyzed AI virus infection reports among animals and humans submitted to animal and public health authorities from January 2013 to June 2022 and compared them with reports from January 2005 to December 2012. A multivariable regression analysis was used to evaluate associations between variables of interest and reported AI virus animal outbreaks. RESULTS: From 2013 to 2022, 52.2% (95/182) of World Organisation for Animal Health (WOAH) Member Countries identified 34 AI virus subtypes during 21,249 outbreaks. The most frequently reported subtypes were high pathogenicity AI H5N1 (10,079/21,249, 47.43%) and H5N8 (6722/21,249, 31.63%). A total of 10 high pathogenicity AI and 6 low pathogenicity AI virus subtypes were reported to the WOAH for the first time during 2013-2022. AI outbreaks in animals occurred in 26 more Member Countries than reported in the previous 8 years. Decreasing World Bank income classification was significantly associated with decreases in reported AI outbreaks (P<.001-.02). Between January 2013 and June 2022, 17/194 (8.8%) World Health Organization (WHO) Member States reported 2000 human AI virus infections of 10 virus subtypes. H7N9 (1568/2000, 78.40%) and H5N1 (254/2000, 12.70%) viruses accounted for the most human infections. As many as 8 of these 17 Member States did not report a human case prior to 2013. Of 1953 human cases with available information, 74.81% (n=1461) had a known animal exposure before onset of illness. The median time from illness onset to the notification posted on the WHO event information site was 15 days (IQR 9-30 days; mean 24 days). Seasonality patterns of animal outbreaks and human infections with AI viruses were very similar, occurred year-round, and peaked during November through May. CONCLUSIONS: Our analysis suggests that AI outbreaks are more frequently reported and geographically widespread than in the past. Global surveillance gaps include inconsistent reporting from all regions and human infection reporting delays. Continued monitoring for AI virus outbreaks in animals and human infections with AI viruses is crucial for pandemic preparedness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".