ArticlePloS one2021
Spatiotemporal characters and influence factors of hand, foot and mouth epidemic in Xinjiang, China.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Epidemiological characteristics and spatiotemporal analysis of hand-foot-mouth disease in Shandong Province, China.International health · 2026Article
- Interactive and joint effects of multiple meteorological and air pollution exposures on hand, foot and mouth disease risk in western China: a time-series analysis.BMJ public health · 2026Article
- Epidemiological, etiological, and serological characteristics of hand, foot, and mouth disease in Guizhou Province, Southwest China, from 2008 to 2023.PLoS neglected tropical diseases · 2025Article
- Spatiotemporal Changes and Influencing Factors of Hand, Foot, and Mouth Disease in Guangzhou, China, From 2013 to 2022: Retrospective Analysis.JMIR public health and surveillance · 2024Article
- Spatiotemporal patterns and risk mapping of provincial hand, foot, and mouth disease in mainland China, 2014-2017.Frontiers in public health · 2024Article
- Analysis of a SARIMA-XGBoost model for hand, foot, and mouth disease in Xinjiang, China.Epidemiology and infection · 2023Article
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4 authors.
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Abstract
Hand, foot and mouth (HFM) disease is a common childhood illness. The paper aims to capture the spatiotemporal characters, and investigate the influence factors of the HFM epidemic in 15 regions of Xinjiang province from 2008 to 2017, China. Descriptive statistical analysis shows that the children aged 0-5 years have a higher HFM incidence, mostly boys. The male-female ratio is 1.5:1. Through the scanning method, we obtain the first cluster high-risk areas. The cluster time is usually from May to August every year. A spatiotemporal model is proposed to analyze the impact of meteorological factors on HFM disease. Comparing with the spatial model, the model is more effective in terms of R2, AIC, deviation, and mean-square error. Among meteorological factors, the number of HFM cases generally increases with the intensity of rainfall. As the temperature increases, there are more HFM patients. Some regions are mostly influenced by wind speed. Further, another spatiotemporal model is introduced to investigate the relationship between HFM disease and socioeconomic factors. The results show that socioeconomic factors have significant influence on the disease. In most areas, the risk of HFM disease tends to rise with the increase of the gross domestic product, the ratios of urban population and tertiary industry. The incidence is closely related to the number of beds and population density in some regions. The higher the ratio of primary school, the lower the number of HFM cases. Based on the above analysis, it is the key measure to prevent and control the spread of the HFM epidemic in high-risk areas, and influence factors should not be ignored.
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