ArticleFrontiers in public health2024
Risk distribution of human infections with avian influenza A (H5N1, H5N6, H9N2 and H7N9) viruses in China.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Quantitative risk assessment of avian influenza: A scoping review.Infectious Disease Modelling · 2026Review
- Adaptive mutations in HA of avian H9N2 influenza viruses facilitate their transmission to swine.Veterinary research · 2025Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aimed to investigate epidemiologic characteristics of major human infection with avian influenza and explore the factors underlying the spatial distributions, particularly H5N6 and H9N2, as H9N2 could directly infect mankind and contribute partial or even whole internal genes to generate novel human-lethal reassortants such as H5N6. They pose potential threats to public health and agriculture. Methods: This study collected cases of H5N1, H5N6, H9N2, and H7N9 in China, along with data on ecoclimatic, environmental, social and demographic factors at the provincial level. Boosted regression tree (BRT) models, a popular approach to ecological studies, has been commonly used for risk mapping of infectious diseases, therefore, it was used to investigate the association between these variables and the occurrence of human cases for each subtype, as well as to map the probabilities of human infections. Results: A total of 1,123 H5N1, H5N6, H9N2, and H7N9 human cases have been collected in China from 2011 to 2024. Factors including density of pig and density of human population emerged as common significant predictors for H5N1 (relative contributions: 5.3, 5.8%), H5N6 (10.8, 6.4%), H9N2 (11.2, 7.3%), and H7N9 (9.4, 8.0%) infection. Overall, each virus has its own ecological and social drivers. The predicted distribution probabilities for H5N1, H5N6, H9N2, and H7N9 presence are highest in Guangxi, Sichuan, Guangdong, and Jiangsu, respectively, with values of 0.86, 0.96, 0.93 and 0.99. Conclusion: This study highlighted the important role of social and demographic factors in the infection of different avian influenza, and suggested that monitoring and control of predicted high-risk areas should be prioritized.
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