Time series analyses of hand, foot and mouth disease integrating weather variables

Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China

BACKGROUND

This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence.

METHOD

A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions.

RESULTS

Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low (< 7 °C) and high (> 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate.

CONCLUSION

This study's LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.

Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania

The epidemiology of neglected tropical diseases (NTD) is persistently underprioritized, despite NTD being widespread among the poorest populations and in the least developed countries on earth. This situation necessitates thorough and efficient public health intervention. Romania is at the brink of becoming a developed country. However, this South-Eastern European country appears to be a region that is susceptible to an underestimated burden of parasitic diseases despite recent public health reforms. Moreover, there is an evident lack of new epidemiologic data on NTD after Romania's accession to the European Union (EU) in 2007. Using the national ICD-10 dataset for hospitalized patients in Romania, we generated time series datasets for 2008-2018. The objective was to gain deep understanding of the epidemiological distribution of three selected and highly endemic parasitic diseases, namely, ascariasis, enterobiasis and cystic echinococcosis (CE), during this period and forecast their courses for the ensuing two years. Through descriptive and inferential analysis, we observed a decline in case numbers for all three NTD. Several distributional particularities at regional level emerged. Furthermore, we performed predictions using a novel automated time series (AutoTS) machine learning tool and could interestingly show a stable course for these parasitic NTD. Such predictions can help public health officials and medical organizations to implement targeted disease prevention and control. To our knowledge, this is the first study involving a retrospective analysis of ascariasis, enterobiasis and CE on a nationwide scale in Romania. It is also the first to use AutoTS technology for parasitic NTD.

Early warning of hand, foot, and mouth disease transmission: A modeling study in mainland, China

BACKGROUND

Hand, foot, and mouth disease (HFMD) is a global infectious disease; particularly, it has a high disease burden in China. This study was aimed to explore the temporal and spatial distribution of the disease by analyzing its epidemiological characteristics, and to calculate the early warning signals of HFMD by using a logistic differential equation (LDE) model.

METHODS

This study included datasets of HFMD cases reported in seven regions in Mainland China. The early warning time (week) was calculated using the LDE model with the key parameters estimated by fitting with the data. Two key time points, "epidemic acceleration week (EAW)" and "recommended warning week (RWW)", were calculated to show the early warning time.

RESULTS

The mean annual incidence of HFMD cases per 100,000 per year was 218, 360, 223, 124, and 359 in Hunan Province, Shenzhen City, Xiamen City, Chuxiong Prefecture, Yunxiao County across the southern regions, respectively and 60 and 34 in Jilin Province and Longde County across the northern regions, respectively. The LDE model fitted well with the reported data (R2 > 0.65, P < 0.001). Distinct temporal patterns were found across geographical regions: two early warning signals emerged in spring and autumn every year across southern regions while one early warning signals in summer every year across northern regions.

CONCLUSIONS

The disease burden of HFMD in China is still high, with more cases occurring in the southern regions. The early warning of HFMD across the seven regions is heterogeneous. In the northern regions, it has a high incidence during summer and peaks in June every year; in the southern regions, it has two waves every year with the first wave during spring spreading faster than the second wave during autumn. Our findings can help predict and prepare for active periods of HFMD.

Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations

BACKGROUND

In the prevention and control of infectious diseases, previous research on the application of big data technology has mainly focused on the early warning and early monitoring of infectious diseases. Although the application of big data technology for COVID-19 warning and monitoring remain important tasks, prevention of the disease's rapid spread and reduction of its impact on society are currently the most pressing challenges for the application of big data technology during the COVID-19 pandemic. After the outbreak of COVID-19 in Wuhan, the Chinese government and nongovernmental organizations actively used big data technology to prevent, contain, and control the spread of COVID-19.

OBJECTIVE

The aim of this study is to discuss the application of big data technology to prevent, contain, and control COVID-19 in China; draw lessons; and make recommendations.

METHODS

We discuss the data collection methods and key data information that existed in China before the outbreak of COVID-19 and how these data contributed to the prevention and control of COVID-19. Next, we discuss China's new data collection methods and new information assembled after the outbreak of COVID-19. Based on the data and information collected in China, we analyzed the application of big data technology from the perspectives of data sources, data application logic, data application level, and application results. In addition, we analyzed the issues, challenges, and responses encountered by China in the application of big data technology from four perspectives: data access, data use, data sharing, and data protection. Suggestions for improvements are made for data collection, data circulation, data innovation, and data security to help understand China's response to the epidemic and to provide lessons for other countries' prevention and control of COVID-19.

RESULTS

In the process of the prevention and control of COVID-19 in China, big data technology has played an important role in personal tracking, surveillance and early warning, tracking of the virus's sources, drug screening, medical treatment, resource allocation, and production recovery. The data used included location and travel data, medical and health data, news media data, government data, online consumption data, data collected by intelligent equipment, and epidemic prevention data. We identified a number of big data problems including low efficiency of data collection, difficulty in guaranteeing data quality, low efficiency of data use, lack of timely data sharing, and data privacy protection issues. To address these problems, we suggest unified data collection standards, innovative use of data, accelerated exchange and circulation of data, and a detailed and rigorous data protection system.

CONCLUSIONS

China has used big data technology to prevent and control COVID-19 in a timely manner. To prevent and control infectious diseases, countries must collect, clean, and integrate data from a wide range of sources; use big data technology to analyze a wide range of big data; create platforms for data analyses and sharing; and address privacy issues in the collection and use of big data.

Spatiotemporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 by the nationwide surveillance system

Background

China has always been one of the countries with the most serious Tuberculosis epidemic in the world. Our study was to observe the Spatial-temporal characteristics and the epidemiology of Tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model.

Methods

The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the Chinese Center for Disease Control and Prevention. The incidence trend of TB was observed by the Joinpoint regression analysis. The Seasonal autoregressive integrated moving average (SARIMA) model was used to predict the monthly incidence. Geographic clusters was employed to analyze the spatial autocorrelation. The relative importance component of TB was detected by the multivariate time series model.

Results

We included 13,991,850 TB cases from January 2004 to December 2017, with a yearly average morbidity of 999,417 cases. The final selected model was the 0 Joinpoint model (P = 0.0001) with an annual average percent change (AAPC) of − 3.3 (95% CI: − 4.3 to − 2.2, P < 0.001). A seasonality was observed across the 14 years, and the seasonal peaks were in January and March every year. The best SARIMA model was (0, 1, 1) X (0, 1, 1)₁₂ which can be written as (1-B) (1-B¹²) X_t = (1–0.42349B) (1–0.43338B¹²) ε_t, with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The MSE, RMSE, MAE, and MAPE of the modelling performance were 201.76, 14.2, 8.4 and 0.06, respectively. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The hotspot of TB transmission was mainly located at southern region of China from 2004 to 2008, including Hainan, Guangxi, Guizhou, and Chongqing, which disappeared in the later years. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5–84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences (> 70 cases per 100,000) were influenced by the autoregressive component for the past 14 years.

Conclusion

In a word, China still has a high TB incidence. However, the incidence rate of TB was significantly decreasing from 2004 to 2017 in China. Seasonal peaks were in January and March every year. Obvious geographical clusters were observed in Tibet and Xinjiang Province. The relative importance component of TB driving transmission was distinguished from the multivariate time series model. For every provinces over the past 14 years, the autoregressive component played a leading role in the incidence of TB which need us to enhance the early protective implementation.

Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database

The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most accurate model in order to perform time series predictions. By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008–2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. Forecast results for the years 2019 and 2020 were generated on a NUTS 2 (Nomenclature of Territorial Units for Statistics) regional level. This is the first study to our knowledge to perform time series forecasting of multiple diseases at a regional level using automated time series machine learning on a national ICD-10 dataset. The deployment of AutoTS technology can help decision makers in implementing targeted national health policies more efficiently.

Temporal relationships between climate variables and hand-foot-mouth disease: a multi-province study in the Mekong Delta Region, Vietnam

Hand-foot-mouth disease (HFMD) is an emerging infectious disease that affects thousands of children every year in Vietnam, especially in the Mekong Delta Region (MDR). This study aims to analyse both provincial and regional level effects of climate factors on HFMD in multiple provinces of this high-risk region. Generalized linear models were used to analyse the daily effects of average temperature, humidity and rainfall on HFMD incidence in each province (provincial-level effects), and random-effect meta-analysis was used to estimate the pooled effect size of these climate-HFMD associations (regional-level effects). Daily effects of the climate factors on HFMD were found at both provincial level and regional level. At provincial level, temperature and humidity had statistically significant positive associations with HFMD while rainfall had both positive and negative associations with HFMD at different lag days. At regional level, temperature and humidity were positively associated with HFMD at lag 0 days (1.7%; 95%CI 0.1%-3.3%) and at lag 3 days (0.3%; 95%CI 0.1%-0.5%), respectively. In contrast, rainfall was found to be negatively associated with HFMD at lag 5 days (- 0.3%; 95%CI - 0.4% to - 0.1%). Heterogeneities of the effects of rainfall on HFMD were found to be higher than those of temperature or humidity. This is the first study to address the climate-HFMD associations in multiple provinces of the MDR. These associations draw attention to climate-related health issues and will help in developing an environment-based early warning system for HFMD prevention and control.

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

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.

AI in Health: State of the Art, Challenges, and Future Directions

INTRODUCTION

Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health.

OBJECTIVE

The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future.

METHODS

Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed.

RESULTS

The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed.

CONCLUSION

Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.

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

Since 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.

Temperature and humidity affect the incidence of hand, foot, and mouth disease: a systematic review of the literature - a report from the International Society of Dermatology Climate Change Committee

Hand, foot, and mouth disease (HFMD) is an enterovirus-mediated condition that predominantly affects children under 5 years of age. The tendency for outbreaks to peak in warmer summer months suggests a relationship between HFMD and weather patterns. We reviewed the English-language literature for articles describing a relationship between meteorological variables and HFMD. Seventy-two studies meeting criteria were identified. A positive, statistically significant relationship was identified between HFMD cases and both temperature (61 of 67 studies, or 91.0%, reported a positive relationship) [CI 81.8-95.8%, P = 0.0001] and relative humidity (41 of 54 studies, or 75.9%) [CI 63.1-85.4%, P = 0.0001]. No significant relationship was identified between HFMD and precipitation, wind speed, and/or sunshine. Most countries reported a single peak of disease each year (most commonly early Summer), but subtropical and tropical climate zones were significantly more likely to experience a bimodal distribution of cases throughout the year (two peaks a year; most commonly late spring/early summer, with a smaller peak in autumn). The rising global incidence of HFMD, particularly in Pacific Asia, may be related to climate change. Weather forecasting might be used effectively in the future to indicate the risk of HFMD outbreaks and the need for targeted public health interventions.

Using Baidu index to nowcast hand-foot-mouth disease in China: a meta learning approach

BACKGROUND

Hand, foot, and mouth disease (HFMD) has been recognized as one of the leading infectious diseases among children in China, which causes hundreds of annual deaths since 2008. In China, the reports of monthly HFMD cases usually have a delay of 1-2 months due to the time needed for collecting and processing clinical information. This time lag is far from optimal for policymakers making decisions. To alleviate this information gap, this study uses a meta learning framework and combines publicly Internet-based information (Baidu search queries) for real-time estimation of HFMD cases.

METHODS

We incorporate Baidu index into modeling to nowcast the monthly HFMD incidences in Guangxi, Zhejiang, Henan provinces and the whole China. We develop a meta learning framework to select appropriate predictive model based on the statistical and time series meta features. Our proposed approach is assessed for the HFMD cases within the time period from July 2015 to June 2016 using multiple evaluation metrics including root mean squared error (RMSE) and correlation coefficient (Corr).

RESULTS

For the four areas: whole China, Guangxi, Zhejiang, and Henan, our approach is superior to the best competing models, reducing the RMSE by 37, 20, 20, and 30% respectively. Compared with all the alternative predictive methods, our estimates show the strongest correlation with the observations.

CONCLUSIONS

In this study, the proposed meta learning method significantly improves the HFMD prediction accuracy, demonstrating that: (1) the Internet-based information offers the possibility for effective HFMD nowcasts; (2) the meta learning approach is capable of adapting to a wide variety of data, and enables selecting appropriate method for improving the nowcasting accuracy.

Predicting Infectious Disease Using Deep Learning and Big Data

Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study's models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society.

Spatial-temporal characteristics and the epidemiology of haemorrhagic fever with renal syndrome from 2007 to 2016 in Zhejiang Province, China

Zhejiang Province is one of the six provinces in China that has the highest incidence of haemorrhagic fever with renal syndrome (HFRS). Data on HFRS cases in Zhejiang Province from January 2007 to July 2017 were obtained from the China Information Network System of Disease Prevention and Control. Joinpoint regression analysis was used to observe the trend of the incidence rate of HFRS. The monthly incidence rate was predicted by autoregressive integrated moving average(ARIMA) models. Spatial autocorrelation analysis was performed to detect geographic clusters. A multivariate time series model was employed to analyze heterogeneous transmission of HFRS. There were a total of 4,836 HFRS cases, with 15 fatal cases reported in Zhejiang Province, China in the last decade. Results show that the mean absolute percentage error (MAPE) of the modelling performance and the forecasting performance of the ARIMA model were 27.53% and 16.29%, respectively. Male farmers and middle-aged patients account for the majority of the patient population. There were 54 high-high clusters and 1 high-low cluster identified at the county level. The random effect variance of the autoregressive component is 0.33; the spatio-temporal component is 1.30; and the endemic component is 2.45. According to the results, there was obvious spatial heterogeneity in the endemic component and spatio-temporal component but little spatial heterogeneity in the autoregressive component. A significant decreasing trend in the incidence rate was identified, and obvious clusters were discovered. Spatial heterogeneity in the factors driving HFRS transmission was discovered, which suggested that a targeted preventive effort should be considered in different districts based on their own main factors that contribute to the epidemics.

Floods Increase the Risks of Hand-Foot-Mouth Disease in Qingdao, China, 2009-2013: A Quantitative Analysis

BACKGROUND

We aimed to quantify the impact of few times floods on hand-foot-mouth disease (HFMD) in Qingdao during 2009-2013.

METHODS

The Spearman correlation test was applied to examine the lagged effects of floods on monthly morbidity of HFMD during study period in Qingdao. We further quantified the effects of 5 flood events on the morbidity of HFMD using the time-series Poisson regression controlling for climatic factors, seasonality, and lagged effects among different populations.

RESULTS

A total of 55,920 cases of HFMD were reported in the study region over the study period. The relative risks of floods on the morbidity of HFMD among the total population, males, females, under 1-2 years old, and 3-5 years old were 1.178, 1.165, 1.198, 1.338, and 1.245, respectively.

CONCLUSIONS

This study has, for the first time, provided the positive evidence of the impact of floods on HFMD. It demonstrates that floods can significantly increase the risk of HFMD during study period. Additionally, among the different populations, the risks were higher among children under 1-5 years old. (Disaster Med Public Health Preparedness. 2018;12:723-729).

Spatio-temporal analysis of the relationship between meteorological factors and hand-foot-mouth disease in Beijing, China

Background

Hand-foot-mouth disease (HFMD) is a common infectious disease in China and occurs mostly in infants and children. Beijing is a densely populated megacity, in which HFMD has been increasing in the last decade. The aim of this study was to quantify spatio-temporal characteristics of HFMD and the relationship between meteorological factors and HFMD incidence in Beijing, China.

Methods

Daily counts of HFMD cases from January 2010 to December 2012 were obtained from the Beijing Center for Disease Prevention and Control (CDC). Seasonal trend decomposition with Loess smoothing was used to explore seasonal patterns and temporal trends of HFMD. Bayesian spatiotemporal Poisson regression models were used to quantify spatiotemporal patterns of HFMD incidence and associations with meteorological factors.

Results

There were 114,777 HFMD cases reported to Beijing CDC from 1 January 2010 to 31 December 2012 and the raw incidence was 568.6 per 100,000 people. May to July was the peak period of HFMD incidence each year. Low-incidence townships were clustered in central, northeast and southwest regions of Beijing. Mean temperature, relative humidity, wind velocity and sunshine hours were all positively associated with HFMD. The effect of wind velocity was significant with a RR of 3.30 (95%CI: 2.37, 4.60) per meter per second increase, as was sunshine hours with a RR of 1.20 (95%CI: 1.02, 1.40) per 1 hour increase.

Conclusions

The distribution of HFMD in Beijing was spatiotemporally heterogeneous, and was associated with meteorological factors. Meteorological monitoring could be incorporated into prediction and surveillance of HFMD in Beijing.

Electronic supplementary material

The online version of this article (10.1186/s12879-018-3071-3) contains supplementary material, which is available to authorized users.

Epidemiological Characteristics and Spatial-Temporal Distribution of Hand, Foot, and Mouth Disease in Chongqing, China, 2009-2016

(1) Objective: Even with licensed vaccine for enterovirus 71 (EV71) put into market in 2016 in China, hand, foot, and mouth disease (HFMD) is still a threat for children’s health in Chongqing. We described the epidemiological characteristics and spatial–temporal patterns of HFMD in Chongqing from 2009 to 2016, in order to provide information and evidence for guiding public health response and intervention. (2) Methods: We retrieved the HFMD surveillance data from January 2009 to December 2016 from “National Disease Reporting Information System”, and then analyzed demographic and geographical information integrally. Descriptive analysis was conducted to evaluate the epidemic features of HFMD in Chongqing. The spatial–temporal methods were performed to explore the clusters at district/county level. (3) Results: A total of 276,207 HFMD cases were reported during the study period (total population incidence: 114.8 per 100,000 per year), including 641 severe cases (129 deaths). The annual incidence of HFMD sharply increased in even-numbered years, but remained stable or decreased in odd-numbered years. A semiannual seasonality was observed during April to July, and October to December in each year. The male-to-female ratios of the mild and severe cases were 1.4:1 and 1.5:1, with the median age of 2.3 years and 1.9 years, respectively. More than 90% of the cases were children equal to and less than 5 years old. High-incidence clustered regions included the main urban districts and northeast regions according to incidence rates comparison or space–time cluster analysis. A total of 19,482 specimen were collected from the reported cases and 13,277 (68.2%) were positive for enterovirus. EV71 was the major causative agent for severe cases, while other enteroviruses were the predominant serotype for mild cases. (4) Conclusions: The characteristics of HFMD in Chongqing exhibited a phenomenon of increasing incidence in two-year cycles and semiannual seasonality in time distribution. Children ≤5 years old, especially boys, were more affected by HFMD. EV71 was the major causative agent for severe cases. We suggest initiating mass EV71 vaccination campaigns among children aged 6 months to 5 years in Chongqing, especially in the main urban districts and northern regions, in order to reduce case fatality, and take integrated measurements for controlling and preventing HFMD attributed to other enteroviruses.

Different responses of weather factors on hand, foot and mouth disease in three different climate areas of Gansu, China

Background

To determine the linear and non-linear interacting relationships between weather factors and hand, foot and mouth disease (HFMD) in children in Gansu, China, and gain further traction as an early warning signal based on weather variability for HFMD transmission.

Method

Weekly HFMD cases aged less than 15 and meteorological information from 2010 to 2014 in Jiuquan, Lanzhou and Tianshu, Gansu, China were collected. Generalized linear regression models (GLM) with Poisson link and classification and regression trees (CART) were employed to determine the combined and interactive relationship of weather factors and HFMD in both linear and non-linear ways.

Results

GLM suggested an increase in weekly HFMD of 5.9% [95% confidence interval (CI): 5.4%, 6.5%] in Tianshui, 2.8% [2.5%, 3.1%] in Lanzhou and 1.8% [1.4%, 2.2%] in Jiuquan in association with a 1 °C increase in average temperature, respectively. And 1% increase of relative humidity could increase weekly HFMD of 2.47% [2.23%, 2.71%] in Lanzhou and 1.11% [0.72%, 1.51%] in Tianshui. CART revealed that average temperature and relative humidity were the first two important determinants, and their threshold values for average temperature deceased from 20 °C of Jiuquan to 16 °C in Tianshui; and for relative humidity, threshold values increased from 38% of Jiuquan to 65% of Tianshui.

Conclusion

Average temperature was the primary weather factor in three areas, more sensitive in southeast Tianshui, compared with northwest Jiuquan; Relative humidity’s effect on HFMD showed a non-linear interacting relationship with average temperature.

Association between meteorological factors and reported cases of hand, foot, and mouth disease from 2000 to 2015 in Japan

The purpose of this study was to clarify the association between hand, foot, and mouth disease (HFMD) epidemics and meteorological conditions. We used HFMD surveillance data of all 47 prefectures in Japan from January 2000 to December 2015. Spectral analysis was performed using the maximum entropy method (MEM) for temperature-, relative humidity-, and total rainfall-dependent incidence data. Using MEM-estimated periods, long-term oscillatory trends were calculated using the least squares fitting (LSF) method. The temperature and relative humidity thresholds of HFMD data were estimated from the LSF curves. The average temperature data indicated a lower threshold at 12 °C and a higher threshold at 30 °C for risk of HFMD infection. Maximum and minimum temperature data indicated a lower threshold at 6 °C and a higher threshold at 35 °C, suggesting a need for HFMD control measures at temperatures between 6 and 35 °C. Based on our findings, we recommend the use of maximum and minimum temperatures rather than the average temperature, to estimate the temperature threshold of HFMD infections. The results obtained might aid in the prediction of epidemics and preparation for the effect of climatic changes on HFMD epidemiology.

Epidemiological characteristics of hand, foot, and mouth disease in Shandong, China, 2009-2016

In the past decade, hand, foot, and mouth disease (HFMD) has posed a serious threat to childhood health in China; however, no epidemiological data from large HFMD epidemics have been described since 2013. In the present study, we described the epidemiological patterns of HFMD in Shandong province during 2009–2016 from a large number of symptomatic cases (n = 839,483), including >370,000 HFMD cases since 2013. Our results revealed that HFMD activity has remained at a high level and continued to cause annual epidemics in Shandong province from 2013 onwards. Although the incidence rate was significantly higher in urban areas than in rural areas, no significantly higher case-severity and case-fatality rates were found in urban areas. Furthermore, the seventeen cities of Shandong province could be classified into three distinct epidemiological groups according to the different peak times from southwest (inland) to northeast (coastal) regions. Notably, a replacement of the predominant HFMD circulating agent was seen and non-EVA71/Coxsackievirus A16 enteroviruses became dominant in 2013 and 2015, causing approximately 30% of the severe cases. Our study sheds light on the latest epidemiological characteristics of HFMD in Shandong province and should prove helpful for the prevention and control of the disease in Shandong and elsewhere.

Seasonal modeling of hand, foot, and mouth disease as a function of meteorological variations in Chongqing, China

Hand, foot, and mouth disease (HFMD) is an enterovirus-induced infectious disease, mainly affecting children under 5 years old. Outbreaks of HFMD in recent years indicate the disease interacts with both the weather and season. This study aimed to investigate the seasonal association between HFMD and weather variation in Chongqing, China. Generalized additive models and distributed lag non-linear models based on a maximum lag of 14 days, with negative binomial distribution assumed to account for overdispersion, were constructed to model the association between reporting HFMD cases from 2009 to 2014 and daily mean temperature, relative humidity, total rainfall and sun duration, adjusting for trend, season, and day of the week. The year-round temperature and relative humidity, rainfall in summer, and sun duration in winter were all significantly associated with HFMD. An inverted-U relationship was found between mean temperature and HFMD above 19 °C in summer, with a maximum morbidity at 27 °C, while the risk increased linearly with the temperature in winter. A hockey-stick association was found for relative humidity in summer with increasing risks over 60%. Heavy rainfall, relative to no rain, was found to be associated with reduced HFMD risk in summer and 2 h of sunshine could decrease the risk by 21% in winter. The present study showed meteorological variables were differentially associated with HFMD incidence in two seasons. Short-term weather variation surveillance and forecasting could be employed as an early indicator for potential HFMD outbreaks.

Epidemiological Features of Hand, Foot and Mouth Disease during the Period of 2008-14 in Wenzhou, China

This study aimed to analyze the epidemiological characteristics of hand, foot and mouth disease (HFMD) during 2008-14 in Wenzhou, China. The epidemiological data of HFMD retrieved from the Wenzhou Center for Disease Control and Prevention were retrospectively analyzed. HFMD infections with enterovirus 71 (EV71), Cox A16 or other pathogens were further verified by polymerase chain reaction (PCR) and real-time PCR. A total of 213 617 cases of HFMD were reported between 2008 and 2014 in Wenzhou. The average incidence was 384.31 of 100 000, and the fatality rate was 0.14‰. The incidence of HFMD peaked between April and July, and it occurred more frequently in males than in females. Approximately 92.68% of the HFMD patients were children aged <5 years. Nearly 80% of the cases were diagnosed within 2 days after onset. The major HFMD pathogen was EV71. This study suggested that appropriate comprehensive prevention and control measures should be taken to avoid the spread of HFMD.

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

Hand, foot and mouth disease (HFMD) in children under the age of five is a major public health issue in China. Beijing–Tianjin–Hebei is the largest urban agglomeration in northern China. The present study aimed to analyze the epidemiological features of HFMD, reveal spatial clusters, and detect risk factors in this region. Reports of HFMD cases in Beijing–Tianjin–Hebei from 1 January 2013 to 31 December 2013 were collected from 211 counties or municipal districts. First, the epidemiological features were explored, and then SaTScan analysis was carried out to detect spatial clusters of HFMD. Finally, GeoDetector and spatial paneled model were used to identify potential risk factors among the socioeconomic and meteorological variables. There were a total of 90,527 HFMD cases in the year 2013. The highest rate was in individuals aged one year, with an incidence of 24.76/10³. Boys (55,168) outnumbered girls (35,359). Temporally, the incidence rose rapidly from April, peaking in June (4.08/10³). Temperature, relative humidity and wind speed were positively associated with the incidence rate, while precipitation and sunshine hours had a negative association. The explanatory powers of these factors were 57%, 13%, 2%, 21% and 12%, respectively. Spatially, the highest-risk regions were located in Beijing and neighboring areas, with a relative risk (RR) value of 3.04. The proportion of primary industry was negatively associated with HFMD transmission, with an explanatory power of 32%. Gross domestic product (GDP) per capita, proportion of tertiary industry, and population density were positively associated with disease incidence, with explanatory powers of 22%, 17% and 15%, respectively. These findings may be helpful in the risk assessment of HFMD transmission and for implementing effective interventions to reduce the burden of this disease.

Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China

Exact prediction of Hemorrhagic fever with renal syndrome (HFRS) epidemics must improve to establish effective preventive measures in China. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish a highly predictive model of HFRS. Meteorological factors were considered external variables through a cross correlation analysis. Then, these factors were included in the SARIMA model to determine if they could improve the predictive ability of HFRS epidemics in the region. The optimal univariate SARIMA model was identified as (0,0,2)(1,1,1)₁₂. The R² of the prediction of HFRS cases from January 2014 to December 2014 was 0.857, and the Root mean square error (RMSE) was 2.708. However, the inclusion of meteorological variables as external regressors did not significantly improve the SARIMA model. This result is likely because seasonal variations in meteorological variables were included in the seasonal characteristics of the HFRS itself.

The Association between Ambient Temperature and Childhood Hand, Foot, and Mouth Disease in Chengdu, China: A Distributed Lag Non-linear Analysis

Hand, foot and mouth disease (HFMD) has recently been recognized as a critical challenge to disease control and public health response in China. This study aimed to quantify the association between temperature and HFMD in Chengdu. Daily HFMD cases and meteorological variables in Chengdu between January 2010 and December 2013 were obtained to construct the time series. A distributed lag non-linear model was performed to investigate the temporal lagged association of daily temperature with age- and gender-specific HFMD. A total of 76,403 HFMD cases aged 0–14 years were reported in Chengdu during the study period, and a bimodal seasonal pattern was observed. The temperature-HFMD relationships were non-linear in all age and gender groups, with the first peak at 14.0–14.1 °C and the second peak at 23.1–23.2 °C. The high temperatures had acute and short-term effects and declined quickly over time, while the effects in low temperature ranges were persistent over longer lag periods. Males and children aged <1 year were more vulnerable to temperature variations. Temperature played an important role in HFMD incidence with non-linear and delayed effects. The success of HFMD intervention strategies could benefit from giving more consideration to local climatic conditions.

Boosted regression tree model-based assessment of the impacts of meteorological drivers of hand, foot and mouth disease in Guangdong, China

BACKGROUND

Hand, foot and mouth disease (HFMD) is a common childhood infection and has become a major public health issue in China. Considerable research has focused on the role of meteorological factors in HFMD development. Nonlinear relationship, delayed effects and collinearity problems are key issues for achieving robust and accurate estimations in this kind of weather-health relationship explorations. The current study was designed to address these issues and assess the impact of meteorological factors on HFMD in Guangdong, China.

METHODS

Case-based HFMD surveillance data and daily meteorological data collected between 2010 and 2012 was obtained from China CDC and the National Meteorological Information Center, respectively. After a preliminary variable selection, for each dataset boosted regression tree (BRT) models were applied to determine the optimal lag for meteorological factors at which the variance of HFMD cases was most explained, and to assess the impacts of these meteorological factors at the optimal lag.

RESULTS

Variance of HFMD cases was explained most by meteorological factors about 1 week ago. Younger children and those from the Pearl-River Delta Region were more sensitive to weather changes. Temperature had the largest contribution to HFMD epidemics (28.99-71.93%), followed by precipitation (6.52-16.11%), humidity (3.92-17.66%), wind speed (3.84-11.37%) and sunshine (6.21-10.36%). Temperature between 10°C and 25°C, as well as humidity between 70% and 90%, had a facilitating effect on the epidemic of HFMD. Sunshine duration above 9h and wind speed below 2.5m/s also contributed to an elevated risk of HFMD. The positive relationship between HFMD and precipitation reversed when the daily amount of rainfall exceeded 25 mm.

CONCLUSIONS

This study indicated significantly facilitating effects of five meteorological factors within some range on the epidemic of HFMD. Results from the current study were particularly important for developing early warning and response system on HFMD in the context of global climate change.

Time series analysis of reported cases of hand, foot, and mouth disease from 2010 to 2013 in Wuhan, China

Background

Hand, foot, and mouth disease (HFMD) is an infectious disease caused by a group of enteroviruses, including Coxsackievirus A16 (CVA16) and Enterovirus A71 (EV-A71). In recent decades, Asian countries have experienced frequent and widespread HFMD outbreaks, with deaths predominantly among children. In several Asian countries, epidemics usually peak in the late spring/early summer, with a second small peak in late autumn/early winter. We investigated the possible underlying association between the seasonality of HFMD epidemics and meteorological variables, which could improve our ability to predict HFMD epidemics.

Methods

We used a time series analysis composed of a spectral analysis based on the maximum entropy method (MEM) in the frequency domain and the nonlinear least squares method in the time domain. The time series analysis was applied to three kinds of monthly time series data collected in Wuhan, China, where high-quality surveillance data for HFMD have been collected: (i) reported cases of HFMD, (ii) reported cases of EV-A71 and CVA16 detected in HFMD patients, and (iii) meteorological variables.

Results

In the power spectral densities for HFMD and EV-A71, the dominant spectral lines were observed at frequency positions corresponding to 1-year and 6-month cycles. The optimum least squares fitting (LSF) curves calculated for the 1-year and 6-month cycles reproduced the bimodal cycles that were clearly observed in the HFMD and EV-A71 data. The peak months on the LSF curves for the HFMD data were consistent with those for the EV-A71 data. The risk of infection was relatively high at 10 °C ≤ t < 15 °C (t, temperature [°C]) and 15 °C ≤ t < 20 °C, and peaked at 20 °C ≤ t < 25 °C.

Conclusion

In this study, the HFMD infections occurring in Wuhan showed two seasonal peaks, in summer (June) and winter (November or December). The results obtained with a time series analysis suggest that the bimodal seasonal peaks in HFMD epidemics are attributable to EV-A71 epidemics. Our results suggest that controlling the spread of EV-A71 infections when the temperature is approximately 20–25 °C should be considered to prevent HFMD infections in Wuhan, China.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-015-1233-0) contains supplementary material, which is available to authorized users.