Spatial-temporal heterogeneity and meteorological factors of hand-foot-and-mouth disease in Xinjiang, China from 2008 to 2016

The study aims to depict the temporal and spatial distributions of hand-foot-and-mouth disease (HFMD) in Xinjiang, China and reveal the relationships between the incidence of HFMD and meteorological factors in Xinjiang. With the national surveillance data of HFMD in Xinjiang and meteorological parameters in the study area from 2008 to 2016, in GeoDetector Model, we examined the effects of meteorological factors on the incidence of HFMD in Xinjiang, China, tested the spatial-temporal heterogeneity of HFMD risk, and explored the temporal-spatial patterns of HFMD through the spatial autocorrelation analysis. From 2008 to 2016, the HFMD distribution showed a distinct seasonal pattern and HFMD cases typically occurred from May to July and peaked in June in Xinjiang. Relative humidity, precipitation, barometric pressure and temperature had the more significant influences on the incidence of HFMD than other meteorological factors with the explanatory power of 0.30, 0.29, 0.29 and 0.21 (P<0.000). The interaction between any two meteorological factors had a nonlinear enhancement effect on the risk of HFMD. The relative risk in Northern Xinjiang was higher than that in Southern Xinjiang. Global spatial autocorrelation analysis results indicated a fluctuating trend over these years: the positive spatial dependency on the incidence of HFMD in 2008, 2010, 2012, 2014 and 2015, the negative spatial autocorrelation in 2009 and a random distribution pattern in 2011, 2013 and 2016. Our findings revealed the correlation between meteorological factors and the incidence of HFMD in Xinjiang. The correlation showed obvious spatiotemporal heterogeneity. The study provides the basis for the government to control HFMD based on meteorological information. The risk of HFMD can be predicted with appropriate meteorological factors for HFMD prevention and control.

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The epidemic characteristics and spatial autocorrelation analysis of hand, foot and mouth disease from 2010 to 2015 in Shantou, Guangdong, China

BMC Public Health ◽

10.1186/s12889-019-7329-5 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 1

Author(s):

Haoyuan Zhang ◽

Lianpeng Yang ◽

Liping Li ◽

Guangxing Xu ◽

Xubin Zhang

Keyword(s):

Spatial Autocorrelation ◽

Foot And Mouth Disease ◽

Mouth Disease ◽

Spatial Autocorrelation Analysis ◽

Autocorrelation Analysis ◽

Foot And Mouth

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Spatial-temporal heterogeneity of hand, foot and mouth disease and impact of meteorological factors in arid/ semi-arid regions: a case study in Ningxia, China

BMC Public Health ◽

10.1186/s12889-019-7758-1 ◽

2019 ◽

Vol 19 (1) ◽

Author(s):

Jie Li ◽

Xiangxue Zhang ◽

Li Wang ◽

Chengdong Xu ◽

Gexin Xiao ◽

...

Keyword(s):

Arid Regions ◽

Meteorological Factors ◽

Temporal Distribution ◽

Foot And Mouth Disease ◽

Northwest China ◽

Mouth Disease ◽

Temporal Heterogeneity ◽

Climate Conditions ◽

Foot And Mouth ◽

Semi Arid

Abstract Background The incidence of hand, foot and mouth disease (HFMD) varies over space and time and this variability is related to climate and social-economic factors. Majority of studies on HFMD were carried out in humid regions while few have focused on the disease in arid/semi-arid regions, more research in such climates would potentially make the mechanism of HFMD transmission clearer under different climate conditions. Methods In this paper, we explore spatial-temporal distribution of HFMD in Ningxia province, which has an arid/semi-arid climate in northwest China. We first employed a Bayesian space-time hierarchy model (BSTHM) to assess the spatial-temporal heterogeneity of the HFMD cases and its relationship with meteorological factors in Ningxia from 2009 to 2013, then used a novel spatial statistical software package GeoDetector to test the spatial-temporal heterogeneity of HFMD risk. Results The results showed that the spatial relative risks in northern part of Ningxia were higher than those in the south. The highest temporal risk of HFMD incidence was in fall season, with a secondary peak in spring. Meteorological factors, such as average temperature, relative humidity, and wind speed played significant roles in the spatial-temporal distribution of HFMD risk. Conclusions The study provide valuable information on HFMD distribution in arid/semi-arid areas in northwest China and facilitate understanding of the concentration of HFMD.

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Spatial-temporal mapping of hand, foot and mouth disease in relation to climate factors in Xinjiang, China from 2008 to 2016

10.21203/rs.2.17027/v1 ◽

2019 ◽

Author(s):

ling xie ◽

Ruifang Huang ◽

Hongwei Wang ◽

Zhengqing Xiao

Keyword(s):

Spatial Autocorrelation ◽

Meteorological Factors ◽

Foot And Mouth Disease ◽

Spatiotemporal Analysis ◽

Quadratic Equation ◽

Mouth Disease ◽

Spatial Dependency ◽

Foot And Mouth ◽

Epidemiological Characteristics ◽

The Relationship

Abstract [Objectives]: The study mainly aims to depict the epidemiological characteristics of hand, foot and mouth disease (HFMD) in Xinjiang, China and evaluate the effects of meteorological factors (temperature & precipitation) on its dynamics through spatiotemporal analysis. This study provides substantial evidences for disease HFMD control and prevention. [Methods]: With the data from the national surveillance data of HFMD and meteorological parameters in the study area from 2008 to 2016, the correlation between meteorological factors and HFMD incidence was explored through kernel density analysis. Furthermore, the spatial autocorrelation of HFMD in each year was analyzed by the Spatial Autocorrelation (Global Moran's I) tool. [Results]: The relationship between monthly mean temperature (T) and HFMD cases fit best in the following logarithmic equation: y=4.8176ln(T)-19.773, (R2 = 0.5194). The relationship between monthly mean precipitation (P) and HFMD fit best in the following quadratic equation, y=-1E-06×P2+0.0108×P+5.9867, (R2 = 0.5319). HFMD was mainly distributed in northern Xinjiang. Global spatial autocorrelation analysis indicated the spatial dependency on the incidence of HFMD in 2008, 2010, 2012, 2014 and 2015. The spatial dependency is the negative spatial autocorrelation in 2009. The incidence of HFMD in Xinjiang presented a random distribution pattern in 2011 and 2016. [Conclusion]: Our findings show that meteorological (air temperature & precipitation) variables had important effects on HFMD occurrence and transmission. This study provides a basis for the early warning of HFMD.

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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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