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

Epidemiology and etiology of hand, foot, and mouth disease in Zhengzhou, China, from 2009 to 2021

Background

Hand, foot, and mouth disease (HFMD) is a common childhood infectious disease caused by a variety of enteroviruses (EVs). To explore the epidemiological characteristics and etiology of HFMD in Zhengzhou, China, we conducted a systematic analysis of HFMD surveillance data from Zhengzhou Center for Disease Control and Prevention from January 2009 to December 2021 (https://wjw.zhengzhou.gov.cn/).

Methods

Surveillance data were collected from Zhengzhou Center for Disease Control and Prevention from January 2009 to December 2021 (https://wjw.zhengzhou.gov.cn/). Cases were analyzed according to the time of onset, type of diagnosis, characteristics, viral serotype, and epidemiological trends.

Results

We found that the primary causative agent responsible for the HFMD outbreaks in Zhengzhou was Enterovirus A71 (EVA-71) (48.56%) before 2014. After 2015, other EVs gradually became the dominant strains (57.68%). The data revealed that the HFMD epidemics in Zhengzhou displayed marked seasonality, with major peaks occurring from April to June, followed by secondary peaks from October to November, except in 2020. Both the severity and case-fatality ratio of HFMD decreased following the COVID-19 pandemic (severity ‰: 13.46 vs. 0.17; case-fatality ‰: 0.21 vs. 0, respectively). Most severe cases were observed in patients aged 1 year and below, accounting for 45.81%.

Conclusions

Overall, the incidence rate of HFMD decreased in Zhengzhou following the introduction of the EVA-71 vaccine in 2016. However, it is crucial to acknowledge that HFMD prevalence continues to exhibit a distinct seasonal pattern and periodicity, and the occurrence of other EV infections poses a new challenge for children's health.

Analysis of a SARIMA-XGBoost model for hand, foot, and mouth disease in Xinjiang, China

Hand, foot, and mouth disease (HFMD) is a common childhood infectious disease. The incidence of HFMD has a pronounced seasonal tendency and is closely related to meteorological factors such as temperature, rainfall, and wind speed. In this paper, we propose a combined SARIMA-XGBoost model to improve the prediction accuracy of HFMD in 15 regions of Xinjiang, China. The SARIMA model is used for seasonal trends, and the XGBoost algorithm is applied for the nonlinear effects of meteorological factors. The geographical and temporal weighted regression model is designed to analyze the influence of meteorological factors from temporal and spatial perspectives. The analysis results show that the HFMD exhibits seasonal characteristics, peaking from May to August each year, and the HFMD incidence has significant spatial heterogeneity. The meteorological factors affecting the spread of HFMD vary among regions. Temperature and daylight significantly impact the transmission of the disease in most areas. Based on the verification experiment of forecasting, the proposed SARIMA-XGBoost model is superior to other models in accuracy, especially in regions with a high incidence of HFMD.

Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China

BACKGROUND

This study aimed to construct a more accurate model to forecast the incidence of hand, foot, and mouth disease (HFMD) in mainland China from January 2008 to December 2019 and to provide a reference for the surveillance and early warning of HFMD.

METHODS

We collected data on the incidence of HFMD in mainland China between January 2008 and December 2019. The SARIMA, SARIMA-BPNN, and SARIMA-PSO-BPNN hybrid models were used to predict the incidence of HFMD. The prediction performance was compared using the mean absolute error(MAE), mean squared error(MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation analysis.

RESULTS

The incidence of HFMD in mainland China from January 2008 to December 2019 showed fluctuating downward trends with clear seasonality and periodicity. The optimal SARIMA model was SARIMA(1,0,1)(2,1,2)_[12], with Akaike information criterion (AIC) and Bayesian Schwarz information criterion (BIC) values of this model were 638.72, 661.02, respectively. The optimal SARIMA-BPNN hybrid model was a 3-layer BPNN neural network with nodes of 1, 10, and 1 in the input, hidden, and output layers, and the R-squared, MAE, and RMSE values were 0.78, 3.30, and 4.15, respectively. For the optimal SARIMA-PSO-BPNN hybrid model, the number of particles is 10, the acceleration coefficients c1 and c2 are both 1, the inertia weight is 1, the probability of change is 0.95, and the values of R-squared, MAE, and RMSE are 0.86, 2.89, and 3.57, respectively.

CONCLUSIONS

Compared with the SARIMA and SARIMA-BPNN hybrid models, the SARIMA-PSO-BPNN model can effectively forecast the change in observed HFMD incidence, which can serve as a reference for the prevention and control of HFMD.

Spatial homogeneity pursuit of regression coefficients for hand, foot and mouth disease in Xinjiang Uygur Autonomous Region in 2018

To explore the complex spatial pattern between the incidence of hand, foot, and mouth disease (HFMD) and meteorological factors [average temperature (AT), average relative humidity (ARH), average air pressure (AP), average wind speed (AW)], this paper constructed a Spatial Clustering coefficient (SCC) regression model to detect spatial clustering patterns of each regression coefficients in different seasons. The results revealed that compared with geographically weighted regression (GWR), the coefficients estimated by SCC method were more smooth with clearly identified spatial and improved edge effects. Therefore, interesting spatial patterns were easy to identify in the SCC estimated coefficients. And then, the SCC method had better estimation accuracy in estimating the relationship between potential meteorological factors and HFMD cases. Meteorological factors had different significance in their effect on HFMD incidence depending on the season. Specifically, the influence of AT on HFMD was negatively correlated in summer and winter, especially in the Altay region, Bayingoleng Mongolian Autonomous Prefecture, Turpan region and Hami region. Second, AW had positive effects with HFMD in summer, but the AW played a negative role in the whole Xinjiang in winter. In Tianshan district, Shayibake district, Shuimogou district, etc. in summer, ARH showed a strong negative correlation, but in Alar city it had a high positive correlation, however, in winter ARH showed a high negative correlation in Altay regions, Aksu region and other places had negative effects, and it showed a strong positive correlation in Shayibak district. Finally, AP had a strong positive correlation with HFMD in summer in Shaybak district, but in winter, AP showed a strong negative correlation in Altay district and Buxel Mongolia Autonomous county. In summary, Xinjiang should adapt measures to local conditions, and formulate appropriate HFMD prevention strategies according to the characteristics of different regions, time, and meteorological factors.

Meta-analysis on the epidemiology of gastroesophageal reflux disease in China

BACKGROUND

No large-scale epidemiological survey on the prevalence of gastroesophageal reflux disease (GERD) in China has been conducted. China has a large population and a complex geographical environment. It is important to understand the prevalence and spatial distribution of GERD in China.

AIM

To explore the prevalence and the spatial, temporal, and population distributions of GERD in the natural Chinese population.

METHODS

We searched Chinese and English databases for literature on the prevalence of GERD in the natural Chinese population. The prevalence of GERD was pooled using a random-effects meta-analysis model. Subgroup analysis was performed according to time, region, and population. We used ArcGIS software to draw statistical maps and trend analysis charts. Spatial autocorrelation analysis was carried out using Geoda software. Spearman correlation analysis was used to assess the spatial distribution relationship between GERD and upper digestive tract tumours.

RESULTS

Altogether, 70 studies involving 276014 individuals from 24 provinces of China were included. The overall pooled prevalence of GERD was 8.7% (95%CI: 7.5%-9.9%) in mainland China. Over the past two decades, the prevalence of GERD in China has increased from 6.0% to 10.6%. GERD was more common in people aged 40-60, with body mass index ≥ 24, and of Uygur ethnicity. The prevalence was higher in the west and east than in the centre, and there may be a local spatial autocorrelation between the Qinghai-Tibet Plateau and the southeast. GERD was correlated with gastric (r = 0.421, P = 0.041) and oesophageal tumours (r = 0.511, P = 0.011) in spatial distribution.

CONCLUSION

GERD is becoming common in China. The prevalence differs by region and population. The development of appropriate strategies for the prevention and treatment of GERD is needed.

Trend of hand, foot, and mouth disease from 2010 to 2021 and estimation of the reduction in enterovirus 71 infection after vaccine use in Zhejiang Province, China

Background

Zhejiang, ranked in the top three in HFMD (hand, foot, and mouth disease) incidence, is located in the Yangtze River Delta region of southeast China. Since 2016, the EV71 vaccine has been promoted in Zhejiang Province. This study aimed to investigate the trend and seasonal variation characteristics of HFMD from 2010 to 2021 and estimate the reduction in enterovirus 71 infection after vaccine use.

Methods

The data on HFMD cases in Zhejiang Province from January 2010 to December 2021 were obtained from this network system. Individual information on cases and deaths was imported, and surveillance information, including demographic characteristics and temporal distributions, was computed by the system. The Joinpoint regression model was used to describe continuous changes in the incidence trend. The BSTS (Bayesian structural time-series models) model was used to estimate the monthly number of cases from 2017 to 2021 based on the observed monthly incidence during 2010–2016 by accounting for seasonality and long-term trends. The seasonal variation characteristics of HFMD pathogens were detected by wavelet analysis.

Results

From 2010 to 2021, the annual incidence rate fluctuated between 98.81 cases per 100,000 in 2020 and 435.63 cases per 100,000 in 2018, and 1711 severe HFMD cases and 106 fatal cases were reported in Zhejiang Province, China. The annual percent change (APC) in EV71 cases was -30.72% (95% CI: -45.10 to -12.50) from 2016 to 2021. The wavelet transform of total incidence and number of cases of the three pathogens all showed significant periodicity on the 1-year scale. The average 2-year scale periodicity was significant for the total incidence, EV71 cases and Cox A16 cases, but the other enterovirus cases showed significant periodicity on the 30-month scale. The 6-month scale periodicity was significant for the total incidence, EV71 case and Cox A16 case but not for the other enteroviruses case. The relative error percentage of the performance of the BSTS model was 0.3%. The estimated number of cases from 2017 to 2021 after the EV-A71 vaccines were used was 9422, and the reduction in the number of cases infected with the EV71 virus was 73.43% compared to 70.80% when the impact of the COVID-19 epidemic in 2020 was excluded.

Conclusions

Since 2010, the incidence of EV71 infections has shown an obvious downward trend. All types of viruses showed significant periodicity on the 1-year scale. The periodicity of the biennial peak is mainly related to EV71 and Cox A16 before 2017 and other enteroviruses since 2018. The half-year peak cycle of HFMD was mainly caused by EV71 and Cox A6 infection. The expected incidence will be 2.76 times(include the cases of 2020) and 2.43 times(exclude the cases of 2020) higher than the actual value assuming that the measures of vaccination are not taken. EV71 vaccines are very effective and should be administered in the age window between 5 months and 5 years.