Publications by authors named "Zhende Wang"

Background: Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic infectious disease commonly found in Asia and Europe, characterized by fever, hemorrhage, shock, and renal failure. China is the most severely affected region, necessitating an analysis of the temporal incidence patterns in the country.

Methods: We employed Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Nonlinear AutoRegressive with eXogenous inputs (NARX), and a hybrid CNN-LSTM model to model and forecast time series data spanning from January 2009 to November 2023 in the mainland China.

View Article and Find Full Text PDF

Background: Gonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions.

Methods: Time series (TS) data of the gonorrhea incidence in China from January 2004 to August 2022 were collected, with the incidence data from September 2021 to August 2022 as the validation. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, and hybrid SARIMA-LSTM model were used to simulate the data respectively, the model performance were evaluated by calculating the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the training and validation sets of the models.

View Article and Find Full Text PDF
Article Synopsis
  • * It found that high humidity, rainfall, and particulate matter (PM) exposure increased TB incidence, while higher wind speed and temperatures reduced risk, showing varying effects across different regions.
  • * The results suggest targeted public health interventions can be developed, considering these environmental factors to better manage TB risks.
View Article and Find Full Text PDF

Background: The purpose of this study is to investigate the association of rotating night shift work, CLOCK, MTNR1A, MTNR1B genes polymorphisms and their interactions with type 2 diabetes among steelworkers.

Methods: A case-control study was conducted in the Tangsteel company in Tangshan, China. The sample sizes of the case group and control group were 251 and 451, respectively.

View Article and Find Full Text PDF

Background: The COVID-19 pandemic has lasted more than 2 years, and the global epidemic prevention and control situation remains challenging. Scientific decision-making is of great significance to people's production and life as well as the effectiveness of epidemic prevention and control. Therefore, it is all the more important to explore its patterns and put forward countermeasures for the pandemic of respiratory infections.

View Article and Find Full Text PDF

Objective: The association between shift schedules and liver enzymes is unclear. This study aims to explore the effect of rotating night shift work on increased liver enzymes.

Methods: The in-service workers of Tangsteel Company who participated in occupational health examination in Tangshan in 2017 were selected as the research objects.

View Article and Find Full Text PDF

Objective: To examine the associations of rotating night shift work with hyperhomocysteinaemia (HHcy) odds by different exposure metrics.

Design: Cross-sectional study.

Setting: Occupational physical examination centre for steel production workers, Tangshan, China.

View Article and Find Full Text PDF

Objectives: In a 24/7 society, the negative metabolic effects of rotating night shift work have been increasingly explored. This study aimed to examine the association between rotating night shift work and non-alcoholic fatty liver disease (NAFLD) in steelworkers.

Methods: A total of 6881 subjects was included in this study.

View Article and Find Full Text PDF

Objective: Tuberculosis (TB) remains a major deadly threat in mainland China. Early warning and advanced response systems play a central role in addressing such a wide-ranging threat. The purpose of this study is to establish a new hybrid model combining a seasonal autoregressive integrated moving average (SARIMA) model and a non-linear autoregressive neural network with exogenous input (NARNNX) model to understand the future epidemiological patterns of TB morbidity.

View Article and Find Full Text PDF

The high incidence, seasonal pattern and frequent outbreaks of hand, foot, and mouth disease (HFMD) represent a threat for millions of children in mainland China. And advanced response is being used to address this. Here, we aimed to model time series with a long short-term memory (LSTM) based on the HFMD notified data from June 2008 to June 2018 and the ultimate performance was compared with the autoregressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NAR).

View Article and Find Full Text PDF

Background: Scarlet fever is recognized as being a major public health issue owing to its increase in notifications in mainland China, and an advanced response based on forecasting techniques is being adopted to tackle this. Here, we construct a new hybrid method incorporating seasonal autoregressive integrated moving average (SARIMA) with a nonlinear autoregressive with external input(NARX) to analyze its seasonality and trend in order to efficiently prevent and control this re-emerging disease.

Methods: Four statistical models, including a basic SARIMA, basic nonlinear autoregressive (NAR) method, traditional SARIMA-NAR and new SARIMA-NARX hybrid approaches, were developed based on scarlet fever incidence data between January 2004 and July 2018 to evaluate its temporal patterns, and their mimic and predictive capacities were compared to discover the optimal using the mean absolute percentage error, root mean square error, mean error rate, and root mean square percentage error.

View Article and Find Full Text PDF

Background: It is a daunting task to discontinue pertussis completely in China owing to its growing increase in the incidence. While basic to any formulation of prevention and control measures is early response for future epidemic trends. Discrete wavelet transform(DWT) has been emerged as a powerful tool in decomposing time series into different constituents, which facilitates better improvement in prediction accuracy.

View Article and Find Full Text PDF

With the re-emergence of brucellosis in mainland China since the mid-1990s, an increasing threat to public health tends to become even more violent, advanced warning plays a pivotal role in the control of brucellosis. However, a model integrating the autoregressive integrated moving average (ARIMA) with Error-Trend-Seasonal (ETS) methods remains unexplored in the epidemiological prediction. The hybrid ARIMA-ETS model based on discrete wavelet transform was hence constructed to assess the epidemics of human brucellosis from January 2004 to February 2018 in mainland China.

View Article and Find Full Text PDF