Based on the MaxEnt model the analysis of influencing factors and simulation of potential risk areas of human infection with avian influenza A (H7N9) in China.
Frontiers in cellular and infection microbiology·
- DOI
- 10.3389/fcimb.2024.1496991
- PMID
- 39831108
- PMCID
- PMC11739160
- OpenAlex
- W4406041675
- Study type
- Journal article
- Publisher
- Publisher unavailable
- Article type
- journal-article
- Integrity
- current
Why this research matters now
Mapping these spatial trends and variable contributions may help guide resource allocation for targeted monitoring and intervention planning.
Structured evidence summary
Research question
The study seeks to identify environmental and demographic drivers associated with human H7N9 infections and to map historical risk zones across China.
Study design
Researchers applied a MaxEnt computational framework to analyze case locations alongside environmental and socioeconomic datasets spanning five years.
Population and setting
The analysis focused on human cases reported throughout China, with particular attention to coastal provinces during the 2013 through 2017 timeframe.
Main findings
Model outputs indicate a steady annual escalation in infection probability, consistently highlighting eastern and southeastern coastal regions as persistent hotspots. Demographic concentration served as the strongest predictor, while ecological and agricultural metrics provided secondary explanatory power. Simulated risk distributions demonstrated strong agreement with documented outbreak locations.
Public-health relevance
Mapping these spatial trends and variable contributions may help guide resource allocation for targeted monitoring and intervention planning.
Important limitations
The authors acknowledge that expanding the dataset with additional variables is necessary to enhance predictive precision and strengthen policy guidance.
GIDS interpretation
This work functions as a retrospective geospatial assessment that contextualizes historical H7N9 distribution patterns and their associated correlates, rather than providing an active monitoring mechanism.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 11 auditable classifier relationships to diseases, places, topics, and study design.