Impact of PM10 and meteorological factors on the incidence of hand, foot, and mouth disease in female children in Ningbo, China: a spatiotemporal and time-series study

AbstractSeasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.

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Meteorological factors affect the hand, foot, and mouth disease epidemic in Qingdao, China, 2007–2014

Epidemiology and Infection ◽

10.1017/s0950268816000601 ◽

2016 ◽

Vol 144 (11) ◽

pp. 2354-2362 ◽

Cited By ~ 20

Author(s):

F. C. JIANG ◽

F. YANG ◽

L. CHEN ◽

J. JIA ◽

Y. L. HAN ◽

...

Keyword(s):

Meteorological Factors ◽

Enterovirus 71 ◽

Rank Correlation ◽

Foot And Mouth Disease ◽

Late Summer ◽

Atmospheric Temperature ◽

Early Spring ◽

Mouth Disease ◽

Foot And Mouth ◽

Spearman Rank Correlation Analysis

SUMMARYHand, foot, and mouth disease (HFMD) has caused public health concerns worldwide. We aimed to investigate the effect of meteorological factors on the HFMD epidemic in Qingdao, a port city in China. A total of 78641 cases were reported in Qingdao between January 2007 and December 2014. Of those, 71084 (90·39%) occurred in children aged 0–5 years, with an incidence of 1691·2/100000. The incidence increased from early spring, peaked between spring and summer, and decreased in late summer. Aetiological agents in all severe cases and selected mild cases were characterized by examining throat swabs. Except for enterovirus 71 (EV71) and coxsackievirus A16 (CA16), other EVs caused >50% of the HFMD cases between 2011 and 2014. EV71 was more frequent in the off-peak months than in the peak months and prone to causing more severe cases compared to CA16 (χ2 = 46·3, P < 0·001). CA10 caused more severe HFMD than did CA6 (χ2 = 20·49, P < 0·001) and all non-CA10 EVs (χ2 = 41·01, P < 0·001). Community-derived HFMD cases accounted for 65·11%. Spearman rank correlation analysis showed that HFMD incidence in children aged 0–5 years was positively correlated with atmospheric temperature (rs = 0·77, P < 0·001), relative humidity (rs = 0·507, P < 0·001), and precipitation (rs = 0·328, P < 0·001). Climate changes and CA10 surveillance in communities should be integrated into the current prophylactic programme.

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Meteorological factors and its association with hand, foot and mouth disease in Southeast and East Asia areas: a meta-analysis

Epidemiology and Infection ◽

10.1017/s0950268818003035 ◽

2018 ◽

Vol 147 ◽

Cited By ~ 6

Author(s):

Chunxiao Duan ◽

Xuefeng Zhang ◽

Hui Jin ◽

Xiaoqing Cheng ◽

Donglei Wang ◽

...

Keyword(s):

East Asia ◽

Meteorological Factors ◽

Global Climate ◽

Meta Analysis ◽

Foot And Mouth Disease ◽

Correlation Coefficients ◽

Mouth Disease ◽

Asia Pacific ◽

China National Knowledge Infrastructure ◽

Foot And Mouth

AbstractSince the late 1990s, hand, foot and mouth disease (HFMD) has become a common health problem that mostly affects children and infants in Southeast and East Asia. Global climate change is considered to be one of the major risk factors for HFMD. This study aimed to assess the correlation between meteorological factors and HFMD in the Asia-Pacific region. PubMed, Web of Science, Embase, China National Knowledge Infrastructure, Wanfang Data and Weipu Database were searched to identify relevant articles published before May 2018. Data were collected and analysed using R software. We searched 2397 articles and identified 51 eligible papers in this study. The present study included eight meteorological factors; mean temperature, mean highest temperature, mean lowest temperature, rainfall, relative humidity and hours of sunshine were positively correlated with HFMD, with correlation coefficients (CORs) of 0.52 (95% confidence interval (CI) 0.42–0.60), 0.43 (95% CI 0.23–0.59), 0.43 (95% CI 0.23–0.60), 0.27 (95% CI 0.19–0.35), 0.19 (95% CI 0.02–0.35) and 0.19 (95% CI 0.11–0.27), respectively. There were sufficient data to support a negative correlation between mean pressure and HFMD (COR = −0.51, 95% CI −0.63 to −0.36). There was no notable correlation with wind speed (COR = 0.10, 95% CI −0.03 to 0.23). Our findings suggest that meteorological factors affect the incidence of HFMD to a certain extent.

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Forecasting incidence of hand, foot and mouth disease using BP neural networks in Jiangsu province, China

BMC Infectious Diseases ◽

10.1186/s12879-019-4457-6 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 4

Author(s):

Wendong Liu ◽

Changjun Bao ◽

Yuping Zhou ◽

Hong Ji ◽

Ying Wu ◽

...

Keyword(s):

Public Health ◽

Neural Networks ◽

Meteorological Factors ◽

Arima Model ◽

Foot And Mouth Disease ◽

Public Health Problem ◽

Mouth Disease ◽

Jiangsu Province ◽

Foot And Mouth ◽

Bp Model

Abstract Background Hand, foot and mouth disease (HFMD) is a rising public health problem and has attracted considerable attention worldwide. The purpose of this study was to develop an optimal model with meteorological factors to predict the epidemic of HFMD. Methods Two types of methods, back propagation neural networks (BP) and auto-regressive integrated moving average (ARIMA), were employed to develop forecasting models, based on the monthly HFMD incidences and meteorological factors during 2009–2016 in Jiangsu province, China. Root mean square error (RMSE) and mean absolute percentage error (MAPE) were employed to select model and evaluate the performance of the models. Results Four models were constructed. The multivariate BP model was constructed using the HFMD incidences lagged from 1 to 4 months, mean temperature, rainfall and their one order lagged terms as inputs. The other BP model was fitted just using the lagged HFMD incidences as inputs. The univariate ARIMA model was specified as ARIMA (1,0,1)(1,1,0)12 (AIC = 1132.12, BIC = 1440.43). And the multivariate ARIMAX with one order lagged temperature as external predictor was fitted based on this ARIMA model (AIC = 1132.37, BIC = 1142.76). The multivariate BP model performed the best in both model fitting stage and prospective forecasting stage, with a MAPE no more than 20%. The performance of the multivariate ARIMAX model was similar to that of the univariate ARIMA model. Both performed much worse than the two BP models, with a high MAPE near to 40%. Conclusion The multivariate BP model effectively integrated the autocorrelation of the HFMD incidence series. Meanwhile, it also comprehensively combined the climatic variables and their hysteresis effects. The introduction of the climate terms significantly improved the prediction accuracy of the BP model. This model could be an ideal method to predict the epidemic level of HFMD, which is of great importance for the public health authorities.

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