Comparison of simulation and predictive efficacy for hemorrhagic fever with renal syndrome incidence in mainland China based on five time series models.

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. By comparing the simulated performance of these models on training and testing sets, we determined the most suitable model. Results: Overall, the CNN-LSTM model demonstrated optimal fitting performance (with Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) of 93.77/270.66, 7.59%/38.96%, and 64.37/189.73 for the training and testing sets, respectively, lower than those of individual CNN or LSTM models). Conclusion: The hybrid CNN-LSTM model seamlessly integrates CNN's data feature extraction and LSTM's recurrent prediction capabilities, rendering it theoretically applicable for simulating diverse distributed time series data. We recommend that the CNN-LSTM model be considered as a valuable time series analysis tool for disease prediction by policy-makers.

Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model.

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. RESULTS: The Seasonal components after data decomposition showed an approximate bimodal distribution with a period of 12 months. The three models identified were SARIMA(1,1,1) (2,1,2)12, LSTM with 150 hidden units, and SARIMA-LSTM with 150 hidden units, the SARIMA-LSTM model fitted best in the training and validation sets, for the smallest MAPE, RMSE, and MPE. CONCLUSIONS: The overall incidence trend of gonorrhea in mainland China has been on the decline since 2004, with some periods exhibiting an upward trend. The incidence of gonorrhea displays a seasonal distribution, typically peaking in July and December each year. The SARIMA model, LSTM model, and SARIMA-LSTM model can all fit the monthly incidence time series data of gonorrhea in mainland China. However, in terms of predictive performance, the SARIMA-LSTM model outperforms the SARIMA and LSTM models, with the LSTM model surpassing the SARIMA model. This suggests that the SARIMA-LSTM model can serve as a preferred tool for time series analysis, providing evidence for the government to predict trends in gonorrhea incidence. The model's predictions indicate that the incidence of gonorrhea in mainland China will remain at a high level in 2024, necessitating that policymakers implement public health measures in advance to prevent the spread of the disease.

Association of air pollutants and meteorological factors with tuberculosis: a national multicenter ecological study in China.

The impact of weather variability and air pollutants on tuberculosis (TB) has been a research hotspot. Previous studies have mostly been limited to a certain area or with a small sample size of cases, and multi-scale systematic studies are lacking. In this study, 14,816,329 TB cases were collected from 31 provinces in China between 2004 and 2018 to estimate the association between TB risk and meteorological factors and air pollutants using a two-stage time-series analysis. The impact and lagged time of meteorological factors and air pollutants on TB risk varied greatly in different provinces and regions. Overall cumulative exposure-response summary associations across 31 provinces suggested that high monthly mean relative humidity (RH) (66.8-82.4%, percentile56-100 (P56-100)), rainfall (316.5-331.1 mm, P96-100), PM2.5 exposure concentration (93.3-145.0 µg/m3, P58-100), and low monthly mean wind speed (1.6-2.1 m/s, P0-38) increased the risk of TB incidence, with a relative risk (RR) of 1.10 (95% CI: 1.04-1.16), 1.10 (95% CI: 1.03-1.16), 2.08 (95% CI: 1.18-3.65), and 2.06 (95% CI: 1.27-3.33), and attributable risk percent (AR%) of 9%, 9%, 52%, and 51%, respectively. Conversely, high monthly average wind speed (2.3-2.9 m/s, P54-100) and mean temperature (20.2-25.3 °C, P79-96), and low monthly average rainfall (2.4-25.2 mm, P0-7) and concentration of SO2 (8.1-21.2 µg/m3, P0-16) exposure decreased the risk of TB incidence, with an overall cumulative RR of 0.92 (95% CI: 0.87-0.98), 0.74 (95% CI: 0.59-0.94), 0.87 (95% CI: 0.79-0.95), and 0.72 (95% CI: 0.56-0.93), respectively. Our study provided insights into future planning of public health interventions for TB.

Association of rotating night shift work, CLOCK, MTNR1A, MTNR1B genes polymorphisms and their interactions with type 2 diabetes among steelworkers: a case-control study.

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. The logistic regression, log-linear model and generalized multifactor dimensionality (GMDR) method were used to investigate the interaction between circadian clock gene, melatonin receptor genes and rotating night shift work on type 2 diabetes among steelworkers. Relative excess risk due to interaction (RERI) and attributable proportions (AP) were used to evaluate additive interactions. RESULTS: Rotating night shift work, current shift status, duration of night shifts, and average frequency of night shifts were associated with an increased risk of type 2 diabetes after adjustment for confounders. Rs1387153 variants in MTNR1B was found to be associated with an increased risk of type 2 diabetes, which was not found between MTNR1A gene rs2119882 locus, CLOCK gene rs1801260 locus and the risk of type 2 diabetes. The association between rotating night shift work and risk of type 2 diabetes appeared to be modified by MTNR1B gene rs1387153 locus (RERI = 0.98, (95% CI, 0.40-1.55); AP = 0.60, (95% CI, 0.07-1.12)). The interaction between MTNR1A gene rs2119882 locus and CLOCK gene rs1801260 locus was associated with the risk of type 2 diabetes (RERI = 1.07, (95% CI, 0.23-1.91); AP = 0.77, (95% CI, 0.36-1.17)). The complex interaction of the MTNR1A-MTNR1B-CLOCK-rotating night shift work model based on the GMDR methods may increase the risk of type 2 diabetes (P = 0.011). CONCLUSIONS: Rotating night shift work and rs1387153 variants in MTNR1B were associated with an increased risk of type 2 diabetes among steelworkers. The complex interaction of MTNR1A-MTNR1B-CLOCK-rotating night shift work may increase the risk of type 2 diabetes.

Comparison of COVID-19 and seasonal influenza under different intensities of non-pharmaceutical interventions and vaccine effectiveness.

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. Methods: Modeling of epidemiological characteristics was conducted based on COVID-19 and influenza characteristics using improved transmission dynamics models to simulate the number of COVID-19 and influenza infections in different scenarios in a hypothetical city of 100,000 people. By comparing the infections of COVID-19 and influenza in different scenarios, the impact of the effectiveness of vaccination and non-pharmaceutical interventions (NPIs) on disease trends can be calculated. We have divided the NPIs into three levels according to the degree of restriction on social activities (including entertainment venues, conventions, offices, restaurants, public transport, etc.), with social controls becoming progressively stricter from level 1 to level 3. Results: In the simulated scenario where susceptible individuals were vaccinated with three doses of COVID-19 coronaVac vaccine, the peak number of severe cases was 26.57% lower than that in the unvaccinated scenario, and the peak number of infection cases was reduced by 10.16%. In the scenario with level three NPIs, the peak number of severe cases was reduced by 7.79% and 15.43%, and the peak number of infection cases was reduced by 12.67% and 28.28%, respectively, compared with the scenarios with NPIs intensity of level 2 and level 1. For the influenza, the peak number of severe cases in the scenario where the entire population were vaccinated was 89.85%, lower than that in the unvaccinated scenario, and the peak number of infections dropped by 79.89%. Conclusion: The effectiveness of COVID-19 coronaVac vaccine for preventing severe outcomes is better than preventing infection; for the prevention and control of influenza, we recommend influenza vaccination as a priority over strict NPIs in the long term.

Rotating night shift work and liver enzymes-associated abnormalities among steelworkers: a cross-sectional study from a Chinese cohort.

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. Multifaceted exposure metrics of night shift work and comprehensive liver enzymes were used to evaluate rotating night shift work and liver enzymes-associated abnormalities, respectively. RESULTS: There were positive associations between the odds of all liver enzymes-associated abnormalities and duration of night shifts. Different exposure metrics of night shift work were significantly associated with higher odds of elevated alanine aminotransferase (ALT), elevated gamma-glutamyl transaminase (GGT) and increased liver enzymes. Compared with those who never worked night shift, the groups of current night shift, duration of night shifts ≤ 18 years, duration of night shifts > 18 years, cumulative number of night shifts ≤ 1643 nights, cumulative number of night shifts > 1643 nights and average frequency of night shifts > 7 nights/month had an OR of increased liver enzymes of 1.31 (95% CI 1.08-1.58), 1.28 (95% CI 1.05-1.58), 1.27 (95% CI 1.04-1.55), 1.28 (95% CI 1.04-1.58), 1.27 (95% CI 1.04-1.55), 1.32 (95% CI 1.08-1.60) after adjusting for all confounders, respectively. No significant association was found between rotating night shift work and liver enzymes-associated abnormalities among female steelworkers. CONCLUSIONS: Rotating night shift work is associated with elevated ALT, elevated GGT and increased liver enzymes in male steelworkers.

Different exposure metrics of rotating night shift work and hyperhomocysteinaemia among Chinese steelworkers: a cross-sectional study.

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. PARTICIPANTS: A total of 6846 steelworkers, aged 22-60 years, from the baseline survey of a Chinese occupational cohort. PRIMARY AND SECONDARY OUTCOME MEASURES: Different exposure metrics of night shift work, including current shift status, duration of night shifts (years), cumulative number of night shifts (nights), cumulative length of night shifts (hours), average frequency of night shifts (nights/month), average length of night shifts (hours/night) and percentage of hours on night shifts, were used to examine the effects of past and current night shift work on HHcy odds. The total homocysteine concentration in the plasma above 15 µmol/L was defined as HHcy. RESULTS: Compared with those who never worked night shifts, current night shift workers had elevated odds of HHcy (OR 1.23, 95% CI 1.06 to 1.44). Considering a person's lifetime work schedule and compared with individuals who never worked night shifts, duration of night shifts >28 years (OR 1.35, 95% CI 1.12 to 1.61), average frequency of night shifts >7 nights/month (OR 1.25, 95% CI 1.07 to 1.47) and percentage of hours on night shifts >30% (OR 1.23, 95% CI 1.05 to 1.43) were associated with higher HHcy odds. The duration of night shifts >20 years and the average frequency of night shifts >7 nights/month could significantly increase the odds of HHcy regardless of whether the average length of night shifts was greater than 8 hours/night. After stratification by sex, no significant association was found in female workers between different exposure metrics of night shift work and HHcy. CONCLUSIONS: Long duration and high frequency of night shift work are associated with higher HHcy odds among male steelworkers.

Rotating night shift work and non-alcoholic fatty liver disease among steelworkers in China: a cross-sectional survey.

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. Different exposure metrics of night shift work including current shift status, duration of night shifts (years), cumulative number of night shifts (nights), cumulative length of night shifts (hours), average frequency of night shifts (nights/month) and average length of night shifts (hours/night) were used to examine the relationship between night shift work and NAFLD. RESULTS: Current night shift workers had elevated odds of NAFLD (OR, 1.23, 95% CI 1.02 to 1.48) compared with those who never worked night shifts after adjustment for potential confounders. Duration of night shifts, cumulative number of night shifts and cumulative length of night shifts were positively associated with NAFLD. Both the average frequency of night shifts (>7 nights/month vs ≤7 nights/month: OR, 1.24, 95% CI 1.06 to 1.45) and average length of night shifts (>8 hours/night vs ≤8 hours/night: OR, 1.27, 95% CI 1.08 to 1.51) were independently associated with overall NAFLD after mutually adjusting for the duration of night shifts and other potential confounders among night shift workers. No significant association was found in female workers between different exposure metrics of night shift work and NAFLD. CONCLUSIONS: Rotating night shift work is associated with elevated odds of NAFLD in male steelworkers.

Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model.

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. METHODS: We develop a SARIMA-NARNNX hybrid model for forecasting future levels of TB incidence based on data containing 255 observations from January 1997 to March 2018 in mainland China, and the ultimate simulating and forecasting performances were compared with the basic SARIMA, non-linear autoregressive neural network (NARNN) and error-trend-seasonal (ETS) approaches, as well as the SARIMA-generalised regression neural network (GRNN) and SARIMA-NARNN hybrid techniques. RESULTS: In terms of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error, the identified best-fitting SARIMA-NARNNX combined model with 17 hidden neurons and 4 feedback delays had smaller values in both in-sample simulating scheme and the out-of-sample forecasting scheme than the preferred single SARIMA(2,1,3)(0,1,1)12 model, a NARNN with 19 hidden neurons and 6 feedback delays and ETS(M,A,A), and the best-performing SARIMA-GRNN and SARIMA-NARNN models with 32 hidden neurons and 6 feedback delays. Every year, there was an obvious high-risk season for the notified TB cases in March and April. Importantly, the epidemic levels of TB from 2006 to 2017 trended slightly downward. According to the projection results from 2018 to 2025, TB incidence will continue to drop by 3.002% annually but will remain high. CONCLUSIONS: The new SARIMA-NARNNX combined model visibly outperforms the other methods. This hybrid model should be used for forecasting the long-term epidemic patterns of TB, and it may serve as a beneficial and effective tool for controlling this disease.

Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China.

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). The results indicated that the identified best-fitting LSTM with the better superiority, be it in modeling dataset or two robustness tests dataset, than the best-conducting NAR and seasonal ARIMA (SARIMA) methods in forecasting performances, including the minimum indices of root mean square error, mean absolute error and mean absolute percentage error. The epidemic trends of HFMD remained stable during the study period, but the reported cases were even at significantly high levels with a notable high-risk seasonality in summer, and the incident cases projected by the LSTM would still be fairly high with a slightly upward trend in the future. In this regard, the LSTM approach should be highlighted in forecasting the epidemics of HFMD, and therefore assisting decision makers in making efficient decisions derived from the early detection of the disease incidents.

Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model.

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. RESULTS: The four preferred models identified were comprised of the SARIMA(0,1,0)(0,1,1)12, NAR with 14 hidden neurons and five delays, SARIMA-NAR with 33 hidden neurons and five delays, and SARIMA-NARX with 16 hidden neurons and 4 delays. Among which presenting the lowest values of the aforementioned indices in both simulation and prediction horizons is the SARIMA-NARX method. Analyses from the data suggested that scarlet fever was a seasonal disease with predominant peaks of summer and winter and a substantial rising trend in the scarlet fever notifications was observed with an acceleration of 9.641% annually, particularly since 2011 with 12.869%, and moreover such a trend will be projected to continue in the coming year. CONCLUSIONS: The SARIMA-NARX technique has the promising ability to better consider both linearity and non-linearity behind scarlet fever data than the others, which significantly facilitates its prevention and intervention of scarlet fever. Besides, under current trend of ongoing resurgence, specific strategies and countermeasures should be formulated to target scarlet fever.

Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model.

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. Thus we aim to integrate modeling approaches as a decision-making supportive tool for formulating health resources. METHODS: We constructed a novel hybrid method based on the pertussis morbidity cases from January 2004 to May 2018 in China, where the approximations and details decomposed by DWT were forecasted by a seasonal autoregressive integrated moving average (SARIMA) and nonlinear autoregressive network (NAR), respectively. Then, the obtained values were aggregated as the final results predicted by the combined model. Finally, the performance was compared with the SARIMA, NAR and traditional SARIMA-NAR techniques. RESULTS: The hybrid technique at level 2 of db2 wavelet including a SARIMA(0,1,3)(1,0,0)12modelfor the approximation-forecasting and NAR model with 12 hidden units and 4 delays for the detail d1-forecasting, along with another NAR model with 11 hidden units and 5 delays for the detail d2-forecasting notably outperformed other wavelets, SARIMA, NAR and traditional SARIMA-NAR techniques in terms of the mean square error, root mean square error, mean absolute error and mean absolute percentage error. Descriptive statistics exhibited that a substantial rise was observed in the notifications from 2013 to 2018, and there was an apparent seasonality with summer peak. Moreover, the trend was projected to continue upwards in the near future. CONCLUSIONS: This hybrid approach has an outstanding ability to improve the prediction accuracy relative to the others, which can be of great help in the prevention of pertussis. Besides, under current trend of pertussis morbidity, it is required to urgently address strategically within the proper policy adopted.

Temporal trends analysis of human brucellosis incidence in mainland China from 2004 to 2018.

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. The preferred hybrid model including the best-performing ARIMA method for approximation-forecasting and the best-fitting ETS approach for detail-forecasting is evidently superior to the standard ARIMA and ETS techniques in both three in-sample simulating and out-of-sample forecasting horizons in terms of the minimum performance indices of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error. Whereafter, an ahead prediction from March to December in 2018 displays a dropping trend compared to the preceding years. But being still present, in various trends, in the present or future. This hybrid model can be highlighted in predicting the temporal trends of human brucellosis, which may act as the potential for far-reaching implications for prevention and control of this disease.