Development and comparison of forecast models of hand-foot-mouth disease with meteorological factors

Impact of the COVID-19 outbreak and interventions on hand, foot and mouth disease in Zhengzhou, China, 2014-2022: a retrospective study

BACKGROUND

Since December 2019, COVID-19 has spread rapidly around the world, and studies have shown that measures to prevent COVID-19 can largely reduce the spread of other infectious diseases. This study explored the impact of the COVID-19 outbreak and interventions on the incidence of HFMD.

METHODS

We gathered data on the prevalence of HFMD from the Children's Hospital Affiliated to Zhengzhou University. An autoregressive integrated moving average model was constructed using HFMD incidence data from 2014 to 2019, the number of cases predicted from 2020 to 2022 was predicted, and the predicted values were compared with the actual measurements.

RESULTS

From January 2014 to October 2022, the Children's Hospital of Zhengzhou University admitted 103,995 children with HFMD. The average number of cases of HFMD from 2020 to 2022 was 4,946, a significant decrease from 14,859 cases from 2014 to 2019. We confirmed the best ARIMA (2,0,0) (1,1,0)12 model. From 2020 to 2022, the yearly number of cases decreased by 46.58%, 75.54%, and 66.16%, respectively, compared with the forecasted incidence. Trends in incidence across sexes and ages displayed patterns similar to those overall.

CONCLUSIONS

The COVID-19 outbreak and interventions reduced the incidence of HFMD compared to that before the outbreak. Strengthening public health interventions remains a priority in the prevention of HFMD.

Comparison of Three Prediction Models for Predicting Chronic Obstructive Pulmonary Disease in China

Purpose

To predict the future number of patients with chronic obstructive pulmonary disease (COPD) in China and compare the three prediction models.

Methods

A generalized additive model (GAM), autoregressive integrated moving average (ARIMA) model, and curve-fitting method were used to fit and predict the number of patients with COPD in China. Data on the number of patients with COPD in China from 1990 to 2019 were obtained from the Global Burden of Disease (GBD) database. The coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), relative error of prediction, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to evaluate and compare the fitting effect, prediction effect, and reliability of the three models.

Results

The GAM, ARIMA, and curve-fitting methods could predict future trends in COPD in China. The performance of the GAM is the best among the three models, whereas the curve fitting method is the worst, and the ARIMA (0,1,2) model is in between. The prediction results of the three models showed that the number of patients with COPD in China is expected to increase from 2020 to 2025.

Conclusion

GAM and AIRMA models are recommended for predicting the future prevalence of COPD in China. The number of patients with COPD in China is expected to increase in the next few years. The prevention and control of COPD in China still needs to be strengthened. Using appropriate models to predict future trends in COPD will provide support for health policymakers.

Analysis of the epidemiological trends of enterovirus A in Asia and Europe

BACKGROUND

Enteroviruses have been in massive, cyclical epidemics worldwide. An in-depth understanding of the international epidemiological characteristics of Enterovirus A (EVA) is critical to determining its clinical significance and total disease burden. Although much research has been conducted on EVA epidemiology, there is still a lack of a comprehensive overview of EVA epidemiological characteristics and trends.

OBJECTIVE

EVA nucleic acid sequences from the NCBI virus database were used to summarize the epidemic time (based on the time of specimen collection), spatial and serotype distribution of EVA, and to analyze EVA isolated from cerebrospinal fluid specimens.

METHODS

EVA sequences were searched in NCBI Virus by keyword ("Enterovirus A″ or "EVA") to screen sequences released before December 2021 and sort them to analyze EVA by year, geographic region and serotype prevalence.

RESULTS

The results found 23,041 retrieved nucleic acid sequences with precise collection dates and geographical regions as of December 2021, with Asia accounting for 87%, Europe for 11% and Africa and the Americas for only 2%. Overall, EV-A71, CVA6 and CVA16 are a few of the main prevalent serotypes; and the prevalence characteristics of the different serotypes change over time from place to place.

CONCLUSION

The prevalence of different serotypes of EVA varies considerably over time and space, and we focused on analysing the epidemiological characteristics of EVAs in Asia and Europe and EVAs that invade the nervous system. This study will likely provide important clues for prevention, control and future research in virological surveillance, disease management and vaccine development.

Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model

Hand, foot, and mouth disease (HFMD) is an important public health problem and has received concern worldwide. Moreover, the coronavirus disease 2019 (COVID-19) epidemic also increases the difficulty of understanding and predicting the prevalence of HFMD. The purpose is to prove the usability and applicability of the automatic machine learning (Auto-ML) algorithm in predicting the epidemic trend of HFMD and to explore the influence of COVID-19 on the spread of HFMD. The AutoML algorithm and the autoregressive integrated moving average (ARIMA) model were applied to construct and validate models, based on the monthly incidence numbers of HFMD and meteorological factors from May 2008 to December 2019 in Henan province, China. A total of four models were established, among which the Auto-ML model with meteorological factors had minimum RMSE and MAE in both the model constructing phase and forecasting phase (training set: RMSE = 1424.40 and MAE = 812.55; test set: RMSE = 2107.83, MAE = 1494.41), so this model has the best performance. The optimal model was used to further predict the incidence numbers of HFMD in 2020 and then compared with the reported cases. And, for analysis, 2020 was divided into two periods. The predicted incidence numbers followed the same trend as the reported cases of HFMD before the COVID-19 outbreak; while after the COVID-19 outbreak, the reported cases have been greatly reduced than expected, with an average of only about 103 cases per month, and the incidence peak has also been delayed, which has led to significant changes in the seasonality of HFMD. Overall, the AutoML algorithm is an applicable and ideal method to predict the epidemic trend of the HFMD. Furthermore, it was found that the countermeasures of COVID-19 have a certain influence on suppressing the spread of HFMD during the period of COVID-19. The findings are helpful to health administrative departments.

Application of logistic differential equation models for early warning of infectious diseases in Jilin Province

Background

There is still a relatively serious disease burden of infectious diseases and the warning time for different infectious diseases before implementation of interventions is important. The logistic differential equation models can be used for predicting early warning of infectious diseases. The aim of this study is to compare the disease fitting effects of the logistic differential equation (LDE) model and the generalized logistic differential equation (GLDE) model for the first time using data on multiple infectious diseases in Jilin Province and to calculate the early warning signals for different types of infectious diseases using these two models in Jilin Province to solve the disease early warning schedule for Jilin Province throughout the year.

Methods

Collecting the incidence of 22 infectious diseases in Jilin Province, China. The LDE and GLDE models were used to calculate the recommended warning week (RWW), the epidemic acceleration week (EAW) and warning removed week (WRW) for acute infectious diseases with seasonality, respectively.

Results

Five diseases were selected for analysis based on screening principles: hemorrhagic fever with renal syndrome (HFRS), shigellosis, mumps, Hand, foot and mouth disease (HFMD), and scarlet fever. The GLDE model fitted the above diseases better (0.80 ≤ R2 ≤ 0.94, P <  0. 005) than the LDE model. The estimated warning durations (per year) of the LDE model for the above diseases were: weeks 12–23 and 40–50; weeks 20–36; weeks 15–24 and 43–52; weeks 26–34; and weeks 16–25 and 41–50. While the durations of early warning (per year) estimated by the GLDE model were: weeks 7–24 and 36–51; weeks 13–37; weeks 11–26 and 39–54; weeks 23–35; and weeks 12–26 and 40–50.

Conclusions

Compared to the LDE model, the GLDE model provides a better fit to the actual disease incidence data. The RWW appeared to be earlier when estimated with the GLDE model than the LDE model. In addition, the WRW estimated with the GLDE model were more lagged and had a longer warning time.

Prediction of hand, foot, and mouth disease epidemics in Japan using a long short-term memory approach

Hand, foot, and mouth disease (HFMD) is a common febrile illness caused by enteroviruses in the Picornaviridae family. The major symptoms of HFMD are fever and a vesicular rash on the hand, foot, or oral mucosa. Acute meningitis and encephalitis are observed in rare cases. HFMD epidemics occur annually in Japan, usually in the summer season. Relatively large-scale outbreaks have occurred every two years since 2011. In this study, the epidemic patterns of HFMD in Japan are predicted four weeks in advance using a deep learning method. The time-series data were analyzed by a long short-term memory (LSTM) approach called a Recurrent Neural Network. The LSTM model was trained on the numbers of weekly HFMD cases in each prefecture. These data are reported in the Infectious Diseases Weekly Report, which compiles the national surveillance data from web sites at the National Institute of Infectious Diseases, Japan, under the Infectious Diseases Control Law. Consequently, our trained LSTM model distinguishes between relatively large-scale and small-scale epidemics. The trained model predicted the HFMD epidemics in 2018 and 2019, indicating that the LSTM approach can estimate the future epidemic patterns of HFMD in Japan.

Seasonality and Meteorological Factors Associated With Different Hand, Foot, and Mouth Disease: Serotype-Specific Analysis From 2010 to 2018 in Zhejiang Province, China

Background

Hand-foot-mouth disease (HFMD) is caused by a group of enteroviruses (EVs) and has a high incidence in children; some subtypes had high mortalities in children. The subtypes of HFMD had a different incidence across seasons. Thereby, we suspect that the infection of HFMD is varied by meteorological factors. However, studies examining serotype-specific associations between meteorological factors and HFMD incidence were rare.

Methods

We obtained all HFMD cases that occurred from 1 January 2010 to 31 December 2018 in Zhejiang province from the China Information System for Disease Control and Prevention (CISDCP). Daily meteorological data for Zhejiang province were provided by the China Methodological Data Sharing Service System and linked to HFMD cases based on residential addresses and dates of onset. The associations between meteorological factors and HFMDs were examined using distributed lag non-linear models (DLNMs) for each serotype.

Results

Overall, the incidences of all HFMD cases were increasing in study years, while the number of severe and fatality cases were decreasing. The dominant serotypes varied by study year. The association between temperature and incidence of both CVA16 and EV71 serotypes showed an inverted U shape. The risk ratio for CVA16 was increasing when temperature is 11–25°C, reaching the maximum RR at 18°C and humidity above 77% can promote the occurrence with CVA16, and temperature between 11 and 32°C with the maximum RR at 21°C and relative humidity above 77% are risk conditions of the occurrence of HFMD associated with EV71. For other enteroviruses causing HFMD, temperature above 11°C and humidity above 76% have a risk effect. CVA16, EV71, and all enteroviruses of HFMD have a maximum effect on lag day 0, and temperature is 35, 34, and 33°C respectively, while the enteroviruses of HFMD other than EV71 and CVA16 has a maximum effect when the temperature is 33°C and the lag time is 7 days.

Conclusion

This study shows that meteorological factors have an effect on the occurrence of different HFMD serotypes. Local control strategies for public health should be taken in time to prevent and reduce the risk of HFMD while the weather is getting warmer and wetter.

Spatial and Temporal Characteristics of Hand-Foot-and-Mouth Disease and Their Influencing Factors in Urumqi, China

Hand, foot, and mouth disease (HFMD) remains a serious health threat to young children. Urumqi is one of the most severely affected cities in northwestern China. This study aims to identify the spatiotemporal distribution characteristics of HFMD, and explore the relationships between driving factors and HFMD in Urumqi, Xinjiang.

METHODS

HFMD surveillance data from 2014 to 2018 were obtained from the China Center for Disease Control and Prevention. The center of gravity and geographical detector model were used to analyze the spatiotemporal distribution characteristics of HFMD and identify the association between these characteristics and socioeconomic and meteorological factors.

RESULTS

A total of 10,725 HFMD cases were reported in Urumqi during the study period. Spatially, the morbidity number of HFMD differed regionally and the density was higher in urban districts than in rural districts. Overall, the development of HFMD in Urumqi expanded toward the southeast. Temporally, we observed that the risk of HFMD peaked from June to July. Furthermore, socioeconomic and meteorological factors, including population density, road density, GDP, temperature and precipitation were significantly associated with the occurrence of HFMD.

CONCLUSIONS

HFMD cases occurred in spatiotemporal clusters. Our findings showed strong associations between HFMD and socioeconomic and meteorological factors. We comprehensively considered the spatiotemporal distribution characteristics and influencing factors of HFMD, and proposed some intervention strategies that may assist in predicting the morbidity number of HFMD.

Effect of Emodin on Coxsackievirus B3m-Mediated Encephalitis in Hand, Foot, and Mouth Disease by Inhibiting Toll-Like Receptor 3 Pathway In Vitro and In Vivo

BACKGROUND

Encephalitis in hand, foot, and mouth disease (HFMD) is a serious threat to children's health and life. Toll-like receptor 3 (TLR3) is an innate immune-recognition receptor that can recognize virus and initiate innate immune responses. Emodin has the effects of anti-inflammatory and regulating immune function, but the mechanism is not very clear.

METHODS

Cells and mice were pretreated with coxsackievirus B3m (CVB3) and treated with emodin. The messenger ribonucleic acid (mRNA) and protein levels of TLR3 and downstream molecules were detected by quantitative real-time polymearse chain reaction and western blotting analysis, respectively. TLR3 expression was also downregulated by anti-TLR3 antibody (TLR3Ab) or small interfering RNA (siRNA). Pathological changes were assessed with hematoxylin and eosin staining. Immunohistochemistry was used to examine the expression of TLR3 in brain tissues. The expression of interleukin (IL)-6, nuclear factor (NF)-κB, and interferon (IFN)-β in serum were tested with enzyme-linked immunosorbent assay.

RESULTS

Emodin decreased the mRNA and protein levels of TLR3 and downstream molecules in vitro and in vivo. After downregulating TLR3 using anti-TLR3Ab or siRNA, emodin could still decrease the mRNA and protein levels of TLR3 and downstream molecules. Emodin also displayed notable effects on pathology, TLR3 protein in brain tissues, and expression of IL-6, NF-κB, IFN-β, in serum.

CONCLUSIONS

Emodin exerts a protective effect in CVB3-mediated encephalitis in HFMD by inhibiting the TLR3 pathway.

Forecast and early warning of hand, foot, and mouth disease based on meteorological factors: Evidence from a multicity study of 11 meteorological geographical divisions in mainland China

BACKGROUND

Hand, foot, and mouth disease (HFMD) is a significant public health issue in China. Early warning and forecasting are one of the most cost-effective ways for HFMD control and prevention. However, relevant research is limited, especially in China with a large population and diverse climatic characteristics. This study aims to identify local specific HFMD epidemic thresholds and construct a weather-based early warning model for HFMD control and prevention across China.

METHODS

Monthly notified HFMD cases and meteorological data for 22 cities selected from different climate zones from 2014 to 2018 were extracted from the National Notifiable Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. A generalized additive model (GAM) based on meteorological factors was conducted to forecast HFMD epidemics. The receiver operator characteristic curve (ROC) was generated to determine the value of optimal warning threshold.

RESULTS

The developed model was solid in forecasting the epidemic of HFMD with all R square (R²) in the 22 cities above 85%, and mean absolute percentage error (MAPE) less than 1%. The warning thresholds varied by cities with the highest threshold observed in Shenzhen (n = 7195) and the lowest threshold in Liaoyang (n = 12). The areas under the curve (AUC) was greater than 0.9 for all regions, indicating a satisfied discriminating ability in epidemics detection.

CONCLUSIONS

The weather-based HFMD forecasting and early warning model we developed for different climate zones provides needed information on occurrence time and size of HFMD epidemics. An effective early warning system for HFMD could provide sufficient time for local authorities to implement timely interventions to minimize the HFMD morbidity and mortality.

Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models

Typhoid and paratyphoid fevers are common enteric diseases causing disability and death in China. Incidence data of typhoid and paratyphoid between 2004 and 2016 in China were analyzed descriptively to explore the epidemiological features such as age-specific and geographical distribution. Cumulative incidence of both fevers displayed significant decrease nationally, displaying a drop of 73.9% for typhoid and 86.6% for paratyphoid in 2016 compared to 2004. Cumulative incidence fell in all age subgroups and the 0–4 years-old children were the most susceptible ones in recent years. A cluster of three southwestern provinces (Yunnan, Guizhou, and Guangxi) were the top high-incidence regions. Grey model GM (1,1) and seasonal autoregressive integrated moving average (SARIMA) model were employed to extract the long-term trends of the diseases. Annual cumulative incidence for typhoid and paratyphoid were formulated by GM (1,1) as x^(t)=−14.98(e−0.10(t−2004)−e−0.10(t−2005)) and x^(t)=−4.96(e−0.19(t−2004)−e−0.19(t−2005)) respectively. SARIMA (0,1,7) × (1,0,1)₁₂ was selected among a collection of constructed models for high R² and low errors. The predictive models for both fevers forecasted cumulative incidence to continue the slightly downward trend and maintain the cyclical seasonality in near future years. Such data-driven insights are informative and actionable for the prevention and control of typhoid and paratyphoid fevers as serious infectious diseases.

Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018

Hand-foot-and-month disease (HFMD), especially the enterovirus A71 (EV-A71) subtype, is a major health problem in Beijing, China. Previous studies mainly used regressive models to forecast the prevalence of HFMD, ignoring its intrinsic age groups. This study aims to predict HFMD of EV-A71 subtype in three age groups (0–3, 3–6 and > 6 years old) from 2011 to 2018 using residual-convolutional-recurrent neural network (CNNRNN-Res), convolutional-recurrent neural network (CNNRNN) and recurrent neural network (RNN). They were compared with auto-regressio, global auto-regression and vector auto-regression on both short-term and long-term prediction. Results showed that CNNRNN-Res and RNN had higher accuracies on point forecast tasks, as well as robust performances in long-term prediction. Three deep learning models also had better skills in peak intensity forecast, and CNNRNN-Res achieved the best results in the peak month forecast. We also found that three age groups had consistent outbreak trends and similar patterns of prediction errors. These results highlight the superior performance of deep learning models in HFMD prediction and can assist the decision-makers to refine the HFMD control measures according to age groups.

Time series analysis of temporal trends in hemorrhagic fever with renal syndrome morbidity rate in China from 2005 to 2019

Hemorrhagic fever with renal syndrome (HFRS) is seriously endemic in China with 70%~90% of the notified cases worldwide and showing an epidemic tendency of upturn in recent years. Early detection for its future epidemic trends plays a pivotal role in combating this threat. In this scenario, our study investigates the suitability for application in analyzing and forecasting the epidemic tendencies based on the monthly HFRS morbidity data from 2005 through 2019 using the nonlinear model-based self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) methods. The experimental results manifested that the SETAR and LSTAR approaches presented smaller values among the performance measures in both two forecasting subsamples, when compared with the most extensively used seasonal autoregressive integrated moving average (SARIMA) method, and the former slightly outperformed the latter. Descriptive statistics showed an epidemic tendency of downturn with average annual percent change (AAPC) of −5.640% in overall HFRS, however, an upward trend with an AAPC = 1.213% was observed since 2016 and according to the forecasts using the SETAR, it would seemingly experience an outbreak of HFRS in China in December 2019. Remarkably, there were dual-peak patterns in HFRS incidence with a strong one occurring in November until January of the following year, additionally, a weak one in May and June annually. Therefore, the SETAR and LSTAR approaches may be a potential useful tool in analyzing the temporal behaviors of HFRS in China.