ArticlePloS one2015
Time series analyses of hand, foot and mouth disease integrating weather variables.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Temporal relationships between climate variables and hand-foot-mouth disease: a multi-province study in the Mekong Delta Region, Vietnam.International journal of biometeorology · 2020Pooled it
- Probabilistic forecasting of hand, foot and mouth disease in Mainland China using Bayesian additive regression tree model.Scientific reports · 2025Article
- Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China.BMC infectious diseases · 2023Article
- Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.PLoS neglected tropical diseases · 2021Article
- Early warning of hand, foot, and mouth disease transmission: A modeling study in mainland, China.PLoS neglected tropical diseases · 2021Article
- Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations.Journal of medical Internet research · 2020Article
- Spatiotemporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 by the nationwide surveillance system.BMC public health · 2020Article
- Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database.International journal of environmental research and public health · 2020Article
- Forecasting incidence of hand, foot and mouth disease using BP neural networks in Jiangsu province, China.BMC infectious diseases · 2019Article
- AI in Health: State of the Art, Challenges, and Future Directions.Yearbook of medical informatics · 2019Review
- Using Baidu index to nowcast hand-foot-mouth disease in China: a meta learning approach.BMC infectious diseases · 2018Article
- Predicting Infectious Disease Using Deep Learning and Big Data.International journal of environmental research and public health · 2018Article
- Spatial-temporal characteristics and the epidemiology of haemorrhagic fever with renal syndrome from 2007 to 2016 in Zhejiang Province, China.Scientific reports · 2018Article
- Spatio-temporal analysis of the relationship between meteorological factors and hand-foot-mouth disease in Beijing, China.BMC infectious diseases · 2018Article
- Epidemiological Characteristics and Spatial-Temporal Distribution of Hand, Foot, and Mouth Disease in Chongqing, China, 2009-2016.International journal of environmental research and public health · 2018Article
- Different responses of weather factors on hand, foot and mouth disease in three different climate areas of Gansu, China.BMC infectious diseases · 2018Article
- Association between meteorological factors and reported cases of hand, foot, and mouth disease from 2000 to 2015 in Japan.Epidemiology and infection · 2017Article
- Epidemiological characteristics of hand, foot, and mouth disease in Shandong, China, 2009-2016.Scientific reports · 2017Article
- Seasonal modeling of hand, foot, and mouth disease as a function of meteorological variations in Chongqing, China.International journal of biometeorology · 2017Article
- Spatio-Temporal Pattern and Risk Factor Analysis of Hand, Foot and Mouth Disease Associated with Under-Five Morbidity in the Beijing-Tianjin-Hebei Region of China.International journal of environmental research and public health · 2017Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe past decade witnessed an increment in the incidence of hand foot mouth disease (HFMD) in the Pacific Asian region; specifically, in Guangzhou China. This emphasized the requirement of an early warning system designed to allow the medical community to better prepare for outbreaks and thus minimize the number of fatalities.
methodsSamples from 1,556 inpatients (hospitalized) and 11,004 outpatients (non-admitted) diagnosed with HFMD were collected in this study from January 2009 to October 2013. Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish high predictive model for inpatients and outpatient as well as three viral serotypes (EV71, Pan-EV and CA16). To integrate climate variables in the data analyses, data from eight climate variables were simultaneously obtained during this period. Significant climate variable identified by correlation analyses was executed to improve time series modeling as external repressors.
resultsAmong inpatients with HFMD, 248 (15.9%) were affected by EV71, 137 (8.8%) were affected by Pan-EV+, and 436 (28.0%) were affected by CA16. Optimal Univariate SARIMA model was identified: (2,0,3)(1,0,0)52 for inpatients, (0,1,0)(0,0,2)52 for outpatients as well as three serotypes (EV71, (1,0,1)(0,0,1)52; CA16, (1,0,1)(0,0,0)52; Pan-EV, (1,0,1)(0,0,0)52). Using climate as our independent variable, precipitation (PP) was first identified to be associated with inpatients (r = 0.211, P = 0.001), CA16-serotype (r = 0.171, P = 0.007) and outpatients (r = 0.214, P = 0.01) in partial correlation analyses, and was then shown a significant lag in cross-autocorrelation analyses. However, inclusion of PP [lag -3 week] as external repressor showed a moderate impact on the predictive performance of the SARIMA model described here-in.
conclusionClimate patterns and HFMD incidences have been shown to be strongly correlated. The SARIMA model developed here can be a helpful tool in developing an early warning system for HFMD.
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