ArticleInternational journal of environmental research and public health2022
A Study on a Neural Network Risk Simulation Model Construction for Avian Influenza A (H7N9) Outbreaks in Humans in China during 2013-2017.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 4 citations in OpenAlex.
- Quantitative risk assessment of avian influenza: A scoping review.Infectious Disease Modelling · 2026Review
- A systematic review of mathematical and machine learning models of Avian Influenza.One health (Amsterdam, Netherlands) · 2025Review
- Environmental monitoring and spatiotemporal trend analysis of avian influenza virus in Xinjiang, 2021-2023.BMC infectious diseases · 2025Article
- Predicting pulmonary tuberculosis incidence in China using Baidu search index: an ARIMAX model approach.Environmental health and preventive medicine · 2023Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The main purposes of this study were to explore the spatial distribution characteristics of H7N9 human infections during 2013-2017, and to construct a neural network risk simulation model of H7N9 outbreaks in China and evaluate their effects. First, ArcGIS 10.6 was used for spatial autocorrelation analysis, and cluster patterns ofH7N9 outbreaks were analyzed in China during 2013-2017 to detect outbreaks' hotspots. During the study period, the incidence of H7N9 outbreaks in China was high in the eastern and southeastern coastal areas of China, with a tendency to spread to the central region. Moran's I values of global spatial autocorrelation of H7N9 outbreaks in China from 2013 to 2017 were 0.080128, 0.073792, 0.138015, 0.139221 and 0.050739, respectively (
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