Trend analysis and forecast of daily reported incidence of hand, foot and mouth disease in Hubei, China by Prophet model

Trends and multi-model prediction of hepatitis B incidence in Xiamen

Background

This study aims to analyze the trend of Hepatitis B incidence in Xiamen City from 2004 to 2022, and to select the best-performing model for predicting the number of Hepatitis B cases from 2023 to 2027.

Methods

Data were obtained from the China Information System for Disease Control and Prevention (CISDCP). The Joinpoint Regression Model analyzed temporal trends, while the Age-Period-Cohort (APC) model assessed the effects of age, period, and cohort on hepatitis B incidence rates. We also compared the predictive performance of the Neural Network Autoregressive (NNAR) Model, Bayesian Structural Time Series (BSTS) Model, Prophet, Exponential Smoothing (ETS) Model, Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Hybrid Model, selecting the model with the highest performance to forecast the number of hepatitis B cases for the next five years.

Results

Hepatitis B incidence rates in Xiamen from 2004 to 2022 showed an overall declining trend, with rates higher in men than in women. Higher incidence rates were observed in adults, particularly in the 30-39 age group. Moreover, the period and cohort effects on incidence showed a declining trend. Furthermore, in the best-performing NNAR(10, 1, 6)[12] model, the number of new cases is predicted to be 4271 in 2023, increasing to 5314 by 2027.

Conclusions

Hepatitis B remains a significant issue in Xiamen, necessitating further optimization of hepatitis B prevention and control measures. Moreover, targeted interventions are essential for adults with higher incidence rates.

Development and Comparison of Time Series Models in Predicting Severe Fever with Thrombocytopenia Syndrome Cases - Hubei Province, China, 2013-2020

Introduction

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus, which has a high mortality rate. Predicting the number of SFTS cases is essential for early outbreak warning and can offer valuable insights for establishing prevention and control measures.

Methods

In this study, data on monthly SFTS cases in Hubei Province, China, from 2013 to 2020 were collected. Various time series models based on seasonal auto-regressive integrated moving average (SARIMA), Prophet, eXtreme Gradient Boosting (XGBoost), and long short-term memory (LSTM) were developed using these historical data to predict SFTS cases. The established models were evaluated and compared using mean absolute error (MAE) and root mean squared error (RMSE).

Results

Four models were developed and performed well in predicting the trend of SFTS cases. The XGBoost model outperformed the others, yielding the closest fit to the actual case numbers and exhibiting the smallest MAE (2.54) and RMSE (2.89) in capturing the seasonal trend and predicting the monthly number of SFTS cases in Hubei Province.

Conclusion

The developed XGBoost model represents a promising and valuable tool for SFTS prediction and early warning in Hubei Province, China.

Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models

BACKGROUND

Influenza is a highly contagious respiratory disease that presents a significant challenge to public health globally. Therefore, effective influenza prediction and prevention are crucial for the timely allocation of resources, the development of vaccine strategies, and the implementation of targeted public health interventions.

METHOD

In this study, we utilized historical influenza case data from January 2013 to December 2021 in Fuzhou to develop four regression prediction models: SARIMA, Prophet, Holt-Winters, and XGBoost models. Their predicted performance was assessed by using influenza data from the period from January 2022 to December 2022 in Fuzhou. These models were used for fitting and prediction analysis. The evaluation metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were employed to compare the performance of these models.

RESULTS

The results indicate that the epidemic of influenza in Fuzhou exhibits a distinct seasonal and cyclical pattern. The influenza cases data displayed a noticeable upward trend and significant fluctuations. In our study, we employed SARIMA, Prophet, Holt-Winters, and XGBoost models to predict influenza outbreaks in Fuzhou. Among these models, the XGBoost model demonstrated the best performance on both the training and test sets, yielding the lowest values for MSE, RMSE, and MAE among the four models.

CONCLUSION

The utilization of the XGBoost model significantly enhances the prediction accuracy of influenza in Fuzhou. This study makes a valuable contribution to the field of influenza prediction and provides substantial support for future influenza response efforts.

Epidemiological characteristics and prediction model construction of hemorrhagic fever with renal syndrome in Quzhou City, China, 2005-2022

Background

Hemorrhagic fever with renal syndrome (HFRS) is one of the 10 major infectious diseases that jeopardize human health and is distributed in more than 30 countries around the world. China is the country with the highest number of reported HFRS cases worldwide, accounting for 90% of global cases. The incidence level of HFRS in Quzhou is at the forefront of Zhejiang Province, and there is no specific treatment for it yet. Therefore, it is crucial to grasp the epidemiological characteristics of HFRS in Quzhou and establish a prediction model for HFRS to lay the foundation for early warning of HFRS.

Methods

Descriptive epidemiological methods were used to analyze the epidemic characteristics of HFRS, the incidence map was drawn by ArcGIS software, the Seasonal AutoRegressive Integrated Moving Average (SARIMA) and Prophet model were established by R software. Then, root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the fitting and prediction performances of the model.

Results

A total of 843 HFRS cases were reported in Quzhou City from 2005 to 2022, with the highest annual incidence rate in 2007 (3.93/100,000) and the lowest in 2022 (1.05/100,000) (P trend<0.001). The incidence is distributed in a seasonal double-peak distribution, with the first peak from October to January and the second peak from May to July. The incidence rate in males (2.87/100,000) was significantly higher than in females (1.32/100,000). Farmers had the highest number of cases, accounting for 79.95% of the total number of cases. The incidence is high in the northwest of Quzhou City, with cases concentrated on cultivated land and artificial land. The RMSE and MAE values of the Prophet model are smaller than those of the SARIMA (1,0,1) (2,1,0)12 model.

Conclusion

From 2005 to 2022, the incidence of HFRS in Quzhou City showed an overall downward trend, but the epidemic in high-incidence areas was still serious. In the future, the dynamics of HFRS outbreaks and host animal surveillance should be continuously strengthened in combination with the Prophet model. During the peak season, HFRS vaccination and health education are promoted with farmers as the key groups.

Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models

BACKGROUND

Seasonal influenza places a substantial burden annually on healthcare services. Policies during the COVID-19 pandemic limited the transmission of seasonal influenza, making the timing and magnitude of a potential resurgence difficult to ascertain and its impact important to forecast.

METHODS

We have developed a hierarchical generalised additive model (GAM) for the short-term forecasting of hospital admissions with a positive test for the influenza virus sub-regionally across England. The model incorporates a multi-level structure of spatio-temporal splines, weekly cycles in admissions, and spatial correlation. Using multiple performance metrics including interval score, coverage, bias, and median absolute error, the predictive performance is evaluated for the 2022-2023 seasonal wave. Performance is measured against autoregressive integrated moving average (ARIMA) and Prophet time series models.

RESULTS

Across the epidemic phases the hierarchical GAM shows improved performance, at all geographic scales relative to the ARIMA and Prophet models. Temporally, the hierarchical GAM has overall an improved performance at 7 and 14 day time horizons. The performance of the GAM is most sensitive to the flexibility of the smoothing function that measures the national epidemic trend.

CONCLUSIONS

This study introduces an approach to short-term forecasting of hospital admissions for the influenza virus using hierarchical, spatial, and temporal components. The methodology was designed for the real time forecasting of epidemics. This modelling framework was used across the 2022-2023 winter for healthcare operational planning by the UK Health Security Agency and the National Health Service in England.

Trend of the Tuberculous Pleurisy Notification Rate in Eastern China During 2017-2021: Spatiotemporal Analysis

BACKGROUND

Tuberculous pleurisy (TP) presents a serious allergic reaction in the pleura caused by Mycobacterium tuberculosis; however, few studies have described its spatial epidemiological characteristics in eastern China.

OBJECTIVE

This study aimed to determine the epidemiological distribution of TP and predict its further development in Zhejiang Province.

METHODS

Data on all notified cases of TP in Zhejiang Province, China, from 2017 to 2021 were collected from the existing tuberculosis information management system. Analyses, including spatial autocorrelation and spatial-temporal scan analysis, were performed to identify hot spots and clusters, respectively. The prediction of TP prevalence was performed using the seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing, and Prophet models using R (The R Foundation) and Python (Python Software Foundation).

RESULTS

The average notification rate of TP in Zhejiang Province was 7.06 cases per 100,000 population, peaking in the summer. The male-to-female ratio was 2.18:1. In terms of geographical distribution, clusters of cases were observed in the western part of Zhejiang Province, including parts of Hangzhou, Quzhou, Jinhua, Lishui, Wenzhou, and Taizhou city. Spatial-temporal analysis identified 1 most likely cluster and 4 secondary clusters. The Holt-Winters model outperformed the SARIMA and Prophet models in predicting the trend in TP prevalence.

CONCLUSIONS

The western region of Zhejiang Province had the highest risk of TP. Comprehensive interventions, such as chest x-ray screening and symptom screening, should be reinforced to improve early identification. Additionally, a more systematic assessment of the prevalence trend of TP should include more predictors.

Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China

BACKGROUND

Hand, foot and mouth disease (HFMD) is a public health concern that threatens the health of children. Accurately forecasting of HFMD cases multiple days ahead and early detection of peaks in the number of cases followed by timely response are essential for HFMD prevention and control. However, many studies mainly predict future one-day incidence, which reduces the flexibility of prevention and control.

METHODS

We collected the daily number of HFMD cases among children aged 0-14 years in Chengdu from 2011 to 2017, as well as meteorological and air pollutant data for the same period. The LSTM, Seq2Seq, Seq2Seq-Luong and Seq2Seq-Shih models were used to perform multi-step prediction of HFMD through multi-input multi-output. We evaluated the models in terms of overall prediction performance, the time delay and intensity of detection peaks.

RESULTS

From 2011 to 2017, HFMD in Chengdu showed seasonal trends that were consistent with temperature, air pressure, rainfall, relative humidity, and PM10. The Seq2Seq-Shih model achieved the best performance, with RMSE, sMAPE and PCC values of 13.943~22.192, 17.880~27.937, and 0.887~0.705 for the 2-day to 15-day predictions, respectively. Meanwhile, the Seq2Seq-Shih model is able to detect peaks in the next 15 days with a smaller time delay.

CONCLUSIONS

The deep learning Seq2Seq-Shih model achieves the best performance in overall and peak prediction, and is applicable to HFMD multi-step prediction based on environmental factors.

Suicidality and mood: the impact of trends, seasons, day of the week, and time of day on explicit and implicit cognitions among an online community sample

Decades of research have established seasonality effects on completed and attempted suicides, with rates increasing in spring. Little advancements have been made to explain this phenomenon, with most studies focusing almost exclusively on the number of suicide attempts and deaths. Using more than six years of data collected among a US, UK, and Canadian online community sample (N > 10,000), we used newly developed Prophet forecasting and autoregressive-integrated moving average time-series models to examine the temporal dynamics of explicit and implicit self-harm cognitions. We created three groups (past suicide attempters; suicide ideation and/or non-suicidal self-injury; no previous self-harm, suicidal thoughts, or behaviors). We found a general increase of negative self-harm cognitions across the six years and seasonality effects for mood and desire to die, particularly among those who previously made a suicide attempt. Negative explicit self-harm cognitions peaked in winter (December), with implicit self-harm showing a lagged peak of two months (February). Moreover, daily negative self-harm cognitions consistently peaked around 4-5 am, with implicit cognitions again showing a lagged effect (1-hour). Limitations include the volunteer sample not being representative and the cross-sectional nature of the data being unable to separate between-subject and within-subject structural trends in the time series. Our findings show that negative explicit and implicit cognitions precede the rise in suicidal behaviors in spring. We proposed a conceptual model of seasonal suicide risk that may offer fertile ground for theoretical advancements, including implications for clinical risk assessment and public policies regarding the availability of health services.

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.

Current status of hand-foot-and-mouth disease

Hand-foot-and-mouth disease (HFMD) is a viral illness commonly seen in young children under 5 years of age, characterized by typical manifestations such as oral herpes and rashes on the hands and feet. These symptoms typically resolve spontaneously within a few days without complications. Over the past two decades, our understanding of HFMD has greatly improved and it has received significant attention. A variety of research studies, including epidemiological, animal, and in vitro studies, suggest that the disease may be associated with potentially fatal neurological complications. These findings reveal clinical, epidemiological, pathological, and etiological characteristics that are quite different from initial understandings of the illness. It is important to note that HFMD has been linked to severe cardiopulmonary complications, as well as severe neurological sequelae that can be observed during follow-up. At present, there is no specific pharmaceutical intervention for HFMD. An inactivated Enterovirus A71 (EV-A71) vaccine that has been approved by the China Food and Drug Administration (CFDA) has been shown to provide a high level of protection against EV-A71-related HFMD. However, the simultaneous circulation of multiple pathogens and the evolution of the molecular epidemiology of infectious agents make interventions based solely on a single agent comparatively inadequate. Enteroviruses are highly contagious and have a predilection for the nervous system, particularly in child populations, which contributes to the ongoing outbreak. Given the substantial impact of HFMD around the world, this Review synthesizes the current knowledge of the virology, epidemiology, pathogenesis, therapy, sequelae, and vaccine development of HFMD to improve clinical practices and public health efforts.

Emergency department presentations during the COVID-19 pandemic in Queensland (to June 2021): interrupted time series analysis

OBJECTIVES

To assess emergency department (ED) presentation numbers in Queensland during the coronavirus disease 2019 (COVID-19) pandemic to mid-2021, a period of relatively low COVID-19 case numbers.

DESIGN

Interrupted time series analysis.

SETTING

All 105 Queensland public hospital EDs.

MAIN OUTCOME MEASURES

Numbers of ED presentations during the COVID-19 lockdown period (11 March 2020 - 30 June 2020) and the period of easing restrictions (1 July 2020 - 30 June 2021), compared with pre-pandemic period (1 January 2018 - 10 March 2020), overall (daily numbers) and by Australasian Triage Scale (ATS; daily numbers) and selected diagnostic categories (cardiac, respiratory, mental health, injury-related conditions) and conditions (stroke, sepsis) (weekly numbers).

RESULTS

During the lockdown period, the mean number of ED presentations was 19.4% lower (95% confidence interval, -20.9% to -17.9%) than during the pre-pandemic period (predicted mean number: 5935; actual number: 4786 presentations). The magnitudes of the decline and the time to return to predicted levels varied by ATS category and diagnostic group; changes in presentation numbers were least marked for ATS 1 and 2 (most urgent) presentations, and for presentations with cardiac conditions or stroke. Numbers remained below predicted levels during the 12-month post-lockdown period for ATS 5 (least urgent) presentations and presentations with mental health problems, respiratory conditions, or sepsis.

CONCLUSIONS

The COVID-19 pandemic and related public restrictions were associated with profound changes in health care use. Pandemic plans should include advice about continuing to seek care for serious health conditions and health emergencies, and support alternative sources of care for less urgent health care needs.

Spatial-temporal dynamics and time series prediction of HFRS in mainland China: A long-term retrospective study

Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China. The current study aims to characterize the spatial-temporal dynamics of HFRS in mainland China during a long-term period (1950-2018). A total of 1 665 431 cases of HFRS were reported with an average annual incidence of 54.22 cases/100 000 individuals during 1950-2018. The joint regression model was used to define the global trend of the HFRS cases with an increasing-decreasing-slightly increasing-decreasing-slightly increasing trend during the 68 years. Then spatial correlation analysis and wavelet cluster analysis were used to identify four types of clusters of HFRS cases located in central and northeastern China. Lastly, the prophet model outperforms auto-regressive integrated moving average model in the HFRS modeling. Our findings will help reduce the knowledge gap on the transmission dynamics and distribution patterns of the HFRS in mainland China and facilitate to take effective preventive and control measures for the high-risk epidemic area.

Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models

Globally, since the outbreak of the Omicron variant in November 2021, the number of confirmed cases of COVID-19 has continued to increase, posing a tremendous challenge to the prevention and control of this infectious disease in many countries. The global daily confirmed cases of COVID-19 between November 1, 2021, and February 17, 2022, were used as a database for modeling, and the ARIMA, MLR, and Prophet models were developed and compared. The prediction performance was evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The study showed that ARIMA (7, 1, 0) was the optimum model, and the MAE, MAPE, and RMSE values were lower than those of the MLR and Prophet models in terms of fitting performance and forecasting performance. The ARIMA model had superior prediction performance compared to the MLR and Prophet models. In real-world research, an appropriate prediction model should be selected based on the characteristics of the data and the sample size, which is essential for obtaining more accurate predictions of infectious disease incidence.

The comparative analysis of SARIMA, Facebook Prophet, and LSTM for road traffic injury prediction in Northeast China

Objective

This cross-sectional research aims to develop reliable predictive short-term prediction models to predict the number of RTIs in Northeast China through comparative studies.

Methodology

Seasonal auto-regressive integrated moving average (SARIMA), Long Short-Term Memory (LSTM), and Facebook Prophet (Prophet) models were used for time series prediction of the number of RTIs inpatients. The three models were trained using data from 2015 to 2019, and their prediction accuracy was compared using data from 2020 as a test set. The parameters of the SARIMA model were determined using the autocorrelation function (ACF) and the partial autocorrelation function (PACF). The LSTM uses linear as the activation function, the mean square error (MSE) as the loss function and the Adam optimizer to construct the model, while the Prophet model is built on the Python platform. The root mean squared error (RMSE), mean absolute error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the predictive performance of the model.

Findings

In this research, the LSTM model had the highest prediction accuracy, followed by the Prophet model, and the SARIMA model had the lowest prediction accuracy. The trend in medical expenditure of RTIs inpatients overlapped highly with the number of RTIs inpatients.

Conclusion

By adjusting the activation function and optimizer, the LSTM predicts the number of RTIs inpatients more accurately and robustly than other models. Compared with other models, LSTM models still show excellent prediction performance in the face of data with seasonal and drastic changes. The LSTM can provide a better basis for planning and management in healthcare administration.

Implication

The results of this research show that it is feasible to accurately forecast the demand for healthcare resources with seasonal distribution using a suitable forecasting model. The prediction of specific medical service volumes will be an important basis for medical management to allocate medical and health resources.

Query-based-learning mortality-related decoders for the developed island economy

Search volumes from Google Trends over clear-defined temporal and spatial scales were reported beneficial in predicting influenza or disease outbreak. Recent studies showed Wiener Model shares merits of interpretability, implementation, and adaptation to nonlinear fluctuation in terms of real-time decoding. Previous work reported Google Trends effectively predicts death-related trends for the continent economy, yet whether it applies to the island economy is unclear. To this end, a framework of the mortality-related model for a developed island economy Taiwan was built based on potential death causes from Google Trends, aiming to provide new insights into death-related online search behavior at a population level. Our results showed estimated trends based on the Wiener model significantly correlated to actual trends, outperformed those with multiple linear regression and seasonal autoregressive integrated moving average. Meanwhile, apart from that involved all possible features, two other sets of feature selecting strategies were proposed to optimize pre-trained models, either by weights or waveform periodicity of features, resulting in estimated death-related dynamics along with spectrums of risk factors. In general, high-weight features were beneficial to both "die" and "death", whereas features that possessed clear periodic patterns contributed more to "death". Of note, normalization before modeling improved decoding performances.

[Epidemiological and etiological characteristics of hand, foot and mouth disease among children aged 5 years and younger in Ningbo (2016 to 2019)]

OBJECTIVE

To describe the epidemiological and etiological characteristics of hand, foot and mouth disease (HFMD) among children aged 5 years and younger in Ningbo after the access of entero-virus-A71 vaccine (2016 to 2019).

METHODS

A retrospective cohort study were performed in children aged 5 years and younger in Ningbo from 2016 to 2019. Data for incidence of HFMD was collected from the National Notifiable Disease Surveillance Reporting System and the Electronic Medical Records (EMRs) System, while the demographic information was derived from the Immunization Information System. Speci-mens were detected by real-time fluorescence quantitative PCR and the Wilson method was used to estimate the incidence rate and 95% confidence interval.

RESULTS

From 2016 to 2019, a total of 1 044 800 residential children were observed in this population-based cohort. In the study, 102 471 cases of HFMD were diagnosed in 2 651 081 person-years, revealing an overall incidence density of 3 865.25/100 000 person-years. There was no significant decline in the number of the cases after the vaccine was available. The number of the patients of hand foot mouth disease during the four years was 93 421, of whom 84 875 (90.85%) had only one incident record, while 8 946 (9.15%) had 2 or more cases in this period; there were 69 771 (66.06%) patients who only needed to see a doctor once for each disease, 19.92% of the patients needed to be treated twice, and 14 801 (14.02%) patients needed to go to the hospital or clinic three times or more. The incidence of HFMD showed obvious seasonality and periodicity, which mainly concentrated in April to July each year, and the epidemic cycle was 2 years; most of the cases were 1 to 3-year old children, with more cases in male. The incidence density varied across the region, with the highest density observed in Ninghai (4 524.76/100 000 person-years), followed by Xiangshan (3 984.22/100 000 person-years). In 3 748 library-conformed cases, 2 834(75.61%) were detected positive, among which enterovirus-A71, Cox-A16 and other enteroviruses accounted for 9.03%, 31.55% and 59.42%, respectively. During the study period, the cumulative coverage of enterovirus-A71 vaccine increased year by year, with the proportion of enterovirus-A71 and severe cases both gradually decreasing.

CONCLUSION

The current status of hand, foot and mouth disease in Ningbo is still serious. Children under 3-year old (especially male children aged 1 year) were the key population for prevention and control. Vaccination might lead to changes in major pathogenic virus type, of which more attention should be paid to the potential impact on disease surveillance, prevention and control.

[Epidemiological and etiological characteristics of hand, foot and mouth disease among children aged 5 years and younger in Ningbo (2016 to 2019)]

OBJECTIVE

To describe the epidemiological and etiological characteristics of hand, foot and mouth disease (HFMD) among children aged 5 years and younger in Ningbo after the access of entero-virus-A71 vaccine (2016 to 2019).

METHODS

A retrospective cohort study were performed in children aged 5 years and younger in Ningbo from 2016 to 2019. Data for incidence of HFMD was collected from the National Notifiable Disease Surveillance Reporting System and the Electronic Medical Records (EMRs) System, while the demographic information was derived from the Immunization Information System. Speci-mens were detected by real-time fluorescence quantitative PCR and the Wilson method was used to estimate the incidence rate and 95% confidence interval.

RESULTS

From 2016 to 2019, a total of 1 044 800 residential children were observed in this population-based cohort. In the study, 102 471 cases of HFMD were diagnosed in 2 651 081 person-years, revealing an overall incidence density of 3 865.25/100 000 person-years. There was no significant decline in the number of the cases after the vaccine was available. The number of the patients of hand foot mouth disease during the four years was 93 421, of whom 84 875 (90.85%) had only one incident record, while 8 946 (9.15%) had 2 or more cases in this period; there were 69 771 (66.06%) patients who only needed to see a doctor once for each disease, 19.92% of the patients needed to be treated twice, and 14 801 (14.02%) patients needed to go to the hospital or clinic three times or more. The incidence of HFMD showed obvious seasonality and periodicity, which mainly concentrated in April to July each year, and the epidemic cycle was 2 years; most of the cases were 1 to 3-year old children, with more cases in male. The incidence density varied across the region, with the highest density observed in Ninghai (4 524.76/100 000 person-years), followed by Xiangshan (3 984.22/100 000 person-years). In 3 748 library-conformed cases, 2 834(75.61%) were detected positive, among which enterovirus-A71, Cox-A16 and other enteroviruses accounted for 9.03%, 31.55% and 59.42%, respectively. During the study period, the cumulative coverage of enterovirus-A71 vaccine increased year by year, with the proportion of enterovirus-A71 and severe cases both gradually decreasing.

CONCLUSION

The current status of hand, foot and mouth disease in Ningbo is still serious. Children under 3-year old (especially male children aged 1 year) were the key population for prevention and control. Vaccination might lead to changes in major pathogenic virus type, of which more attention should be paid to the potential impact on disease surveillance, prevention and control.

Predictive analysis of the number of human brucellosis cases in Xinjiang, China

Brucellosis is one of the major public health problems in China, and human brucellosis represents a serious public health concern in Xinjiang and requires a prediction analysis to help making early planning and putting forward science preventive and control countermeasures. According to the characteristics of the time series of monthly reported cases of human brucellosis in Xinjiang from January 2008 to June 2020, we used seasonal autoregressive integrated moving average (SARIMA) method and nonlinear autoregressive regression neural network (NARNN) method, which are widely prevalent and have high prediction accuracy, to construct prediction models and make prediction analysis. Finally, we established the SARIMA((1,4,5,7),0,0)(0,1,2)12 model and the NARNN model with a time lag of 5 and a hidden layer neuron of 10. Both models have high fitting performance. After comparing the accuracies of two established models, we found that the SARIMA((1,4,5,7),0,0)(0,1,2)12 model was better than the NARNN model. We used the SARIMA((1,4,5,7),0,0)(0,1,2)12 model to predict the number of monthly reported cases of human brucellosis in Xinjiang from July 2020 to December 2021, and the results showed that the fluctuation of the time series from July 2020 to December 2021 was similar to that of the last year and a half while maintaining the current prevention and control ability. The methodology applied here and its prediction values of this study could be useful to give a scientific reference for prevention and control human brucellosis.