ARIMA and ARIMA-ERNN models for prediction of pertussis incidence in mainland China from 2004 to 2021

Factors associated with pregnant women's willingness to receive maternal pertussis vaccination in Guizhou Province, China: An exploratory cross-sectional study

The rise in pertussis incidence among infants in Guizhou, China underscores the need for maternal acellular pertussis vaccine (aP) immunization, a key strategy in protecting infants from severe health consequences. However, the willingness of pregnant women in Guizhou to receive this vaccine is not well-understood. This study aimed to explore pregnant women's intentions toward maternal pertussis vaccination in Guizhou and identify the associated factors. A questionnaire based on the health belief model, was administered in an exploratory cross-sectional study from January to February 2022. Data from 564 participants were collected and analyzed. The chi-square test, Mann-Whitney U test, and Poisson regression were used to identify potential factors associated with vaccination intentions. Participants' median age was 27 y (interquartile range (IQR): 24-31), and the median number of children per participant was one. The study found that only 36.0% of the participants intended to receive the aP vaccine while 64.0% were uncertain or negative in this regard. Significant factors associated with intentions to vaccinate included perceived barriers and cues for action and perceived benefits. The major barriers for low vaccination intentions were safety concerns for both the fetus and the mother, and family members' negative attitudes. Free vaccines, perceiving preventive benefits, observing other pregnant women getting vaccinated, and healthcare provider recommendations may facilitate vaccination intentions. Multiple immune strategies should be developed or optimized to cope with the resurgence of pertussis.

Global, regional, and national burden and trends of air pollution-related neoplasms from 1990 to 2019: An observational trend study from the Global Burden of Disease Study 2019

BACKGROUND

Air pollution-related neoplasms are a major global public health issue and are one of the leading causes of death worldwide. Air pollution is one of the important risk factors of air pollution-related neoplasms and is associated with a variety of air pollution-related neoplasms.The primary objective of this study was to estimate the epidemiological patterns of death rates and disability-adjusted life years (DALYs) associated with air pollution-related neoplasms on a global scale, covering the period from 1990 to 2019. Furthermore, we aimed to predict the trends in these epidemiological patterns up to 2050. By achieving these goals, our study seeks to provide a comprehensive understanding of the potential causes underlying the observed disparities in neoplasm-related health outcomes, ultimately contributing to the development of effective strategies for addressing this major public health issue.

METHODS

Based on data from the 2019 Global Burden of Disease (GBD) study, the indicators of the air pollution-related neoplasms disease burden was the numbers and age-standardized rates (ASR) of deaths and disability-adjusted life years (DALYs) from 1990 to 2019. First, we compared the burden of air pollution-related neoplasms and temporal trends by gender, age, socio-demographic index (SDI), region, and country. Furthermore, driving factors and improvement potential were evaluated using decomposition and frontier analysis. Finally, forecasting analyses of the changing trend in the burden of air pollution-related neoplasm up to 2050 was conducted based on time series forecasting models.

RESULTS

In 2019, air pollution-related neoplasms accounted for 387.45 million (95 % UI 288.04-490.06 million) deaths and 8951.97 million (95 % UI 6680.89-11342.60 million) DALYs globally. Deaths and DALYs demonstrated an upward trend from 1990 to 2019, while their ASR showed a downward trend. The disease burden and the decline degree of males were both significantly higher than that of females, and the high burden was mainly in the elderly groups. The middle SDI region possessed the highest burden with the most significant upward trend, while the high SDI region had the lowest burden with the most significant downward trend. Decomposition analyses represented that the increase in the overall deaths and DALYs of air pollution-related neoplasms was mainly driven by population growth. The predictive analyses expected that the deaths and DALYs of air pollution-related neoplasms will continue to rise, while their corresponding ASR will decrease by 2050.

CONCLUSION

The global burden of air pollution-related neoplasms remained high, and deaths and DALYs will be on upward trends up to 2050, with differences among genders, ages, SDI levels, GBD regions, and countries. It is essential to understand the air pollution-related neoplasm burden and contributing epidemiological factors for implementing effective and factor-tailored interventions to reduce the global burden.

Epidemiology and SARIMA model of deaths in a tertiary comprehensive hospital in Hangzhou from 2015 to 2022

BACKGROUND

By analysing the deaths of inpatients in a tertiary hospital in Hangzhou, this study aimed to understand the epidemiological distribution characteristics and the composition of the causes of death. Additionally, this study aimed to predict the changing trend in the number of deaths, providing valuable insights for hospitals to formulate relevant strategies and measures aimed at reducing mortality rates.

METHODS

In this study, data on inpatient mortality at a tertiary hospital in Hangzhou from 2015 to 2022 were obtained via the population information registration system of the Chinese Center for Disease Control and Prevention. The death data of inpatients were described and analysed through a retrospective study. Excel 2016 was utilized for data sorting, and SPSS 22.0 software was employed for data analysis. The statistical inference of single factor differences was conducted via χ2 tests. The SARIMA model was established via the forecast, aTSA, and tseries software packages (version 4.3.0) to forecast future changes in the number of deaths.

RESULTS

A total of 1938 inpatients died at the tertiary hospital in Hangzhou, with the greatest number of deaths occurring in 2022 (262, 13.52%). The sex ratio was 2.22:1, and there were significant differences between sexes in terms of age, marital status, educational level, and place of residence (P < 0.05). The percentage of males in the groups aged of 20 to 29 and 30 to 39 years was significantly greater than that of females (χ2 = 46.905, P < 0.001). More females than males died in the widowed group, and divorced and married males experienced a greater number of deaths than divorced and married females did (χ2 = 61.130, P < 0.001). The proportions of male students with a junior college and senior high school education were significantly greater than that of female students (χ2 = 12.310, P < 0.05). The primary causes of mortality within the hospital setting included circulatory system diseases, injury, poisoning, tumours, and respiratory system diseases. These leading factors accounted for 86.12% of all recorded deaths. Finally, the SARIMA (2, 1, 1) (1, 1, 1)12 model was determined to be the optimal model, with an AIC of 380.23, a BIC of 392.79, and an AICc of 381.81. The MAPE was 14.99%, indicating a satisfactory overall fit of this model. The relative error between the predicted and actual number of deaths in 2022 was 8.02%. Therefore, the SARIMA (2, 1, 1) (1, 1, 1)12 model demonstrates good predictive performance.

CONCLUSIONS

Hospitals should enhance the management of sudden cardiac death, acute myocardial infarction, severe craniocerebral injury, lung cancer, and lung infection to reduce the mortality rate. The SARIMA model can be employed for predicting the number of deaths.

Assessing the impact of COVID-19 non-pharmaceutical interventions and relaxation policies on Class B respiratory infectious diseases transmission in China

This study investigates the incidence of Class B respiratory infectious diseases (RIDs) in China under the Coronavirus disease 2019 (COVID-19) epidemic and examines variations post-epidemic, following the relaxation of non-pharmaceutical interventions (NPIs). Two-stage evaluation was used in our study. In the first stage evaluation, we established counterfactual models for the pre-COVID-19 period to estimate expected incidences of Class B RIDs without the onset of the epidemic. In the second stage evaluation, we constructed seasonal autoregressive integrated moving average intervention (SARIMA-Intervention) models to evaluate the impact on the Class B RIDs after NPIs aimed at COVID-19 pandemic were relaxed. The counterfactual model in the first stage evaluation suggested average annual increases of 10.015%, 78.019%, 70.439%, and 67.799% for tuberculosis, scarlet fever, measles, and pertussis respectively, had the epidemic not occurred. In the second stage evaluation, the total relative reduction in 2023 of tuberculosis, scarlet fever, measles and pertussis were - 35.209%, - 59.184%, - 4.481%, and - 9.943% respectively. The actual incidence declined significantly in the first stage evaluation. However, the results of the second stage evaluation indicated that a rebound occurred in four Class B RIDs after the relaxation of NPIs; all of these showed a negative total relative reduction rate.

Analysis and forecasting of syphilis trends in mainland China based on hybrid time series models

Syphilis remains a serious public health problem in mainland China that requires attention, modelling to describe and predict its prevalence patterns can help the government to develop more scientific interventions. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, hybrid SARIMA-LSTM model, and hybrid SARIMA-nonlinear auto-regressive models with exogenous inputs (SARIMA-NARX) model were used to simulate the time series data of the syphilis incidence from January 2004 to November 2023 respectively. Compared to the SARIMA, LSTM, and SARIMA-LSTM models, the median absolute deviation (MAD) value of the SARIMA-NARX model decreases by 352.69%, 4.98%, and 3.73%, respectively. The mean absolute percentage error (MAPE) value decreases by 73.7%, 23.46%, and 13.06%, respectively. The root mean square error (RMSE) value decreases by 68.02%, 26.68%, and 23.78%, respectively. The mean absolute error (MAE) value decreases by 70.90%, 23.00%, and 21.80%, respectively. The hybrid SARIMA-NARX and SARIMA-LSTM methods predict syphilis cases more accurately than the basic SARIMA and LSTM methods, so that can be used for governments to develop long-term syphilis prevention and control programs. In addition, the predicted cases still maintain a fairly high level of incidence, so there is an urgent need to develop more comprehensive prevention strategies.

Trajectories tracking of maternal and neonatal health in eastern China from 2010 to 2021: A multicentre cross-sectional study

Background

China's fertility policy has dramatically changed in the past decade with the successive promulgation of the partial two-child policy, universal two-child policy and three-child policy. The trajectories of maternal and neonatal health accompanied the changes in fertility policy are unknown.

Methods

We obtained data of 280 203 deliveries with six common pregnancy complications and thirteen perinatal outcomes between 2010 and 2021 in eastern China. The average annual percent change (AAPC) was calculated to evaluated the temporal trajectories of obstetric characteristics and adverse outcomes during this period. Then, the autoregressive integrated moving average (ARIMA) models were constructed to project future trend of obstetric characteristics and outcomes until 2027.

Results

The proportion of advanced maternal age (AMA), assisted reproduction technology (ART) treatment, gestational diabetes mellitus (GDM), anaemia, thrombocytopenia, thyroid dysfunction, oligohydramnios, placental abruption, small for gestational age (SGA) infants, and congenital malformation significantly increased from 2010 to 2021. However, the placenta previa, large for gestational age (LGA) infants and stillbirth significantly decreased during the same period. The AMA and ART treatment were identified as independent risk factors for the uptrends of pregnancy complications and adverse perinatal outcomes. The overall caesarean section rate remained above 40%. Importantly, among multiparas, a previous caesarean section was found to be associated with a significantly reduced risk of hypertensive disorders of pregnancy (HDP), premature rupture of membranes (PROM), placenta previa, placental abruption, perinatal asphyxia, LGA infants, stillbirths, and preterm births. In addition, the ARIMA time series models predicted increasing trends in the ART treatment, GDM, anaemia, thrombocytopenia, postpartum haemorrhage, congenital malformation, and caesarean section until 2027. Conversely, a decreasing trend was predicted for HDP, PROM, and placental abruption premature, LGA infants, SGA infants, perinatal asphyxia, and stillbirth.

Conclusions

Maternal and neonatal adverse outcomes became more prevalent from 2010 to 2021 in China. Maternal age and ART treatment were independent risk factors for adverse obstetric outcomes. The findings offered comprehensive trajectories for monitoring pregnancy complications and perinatal outcomes in China, and provided robust intervention targets in obstetric safety. The development of early prediction models and the implementation of prevention efforts for adverse obstetric events are necessary to enhance obstetric safety.

Correlation between intraosseous thermal change and drilling impulse data during osteotomy within autonomous dental implant robotic system: An in vitro study

OBJECTIVES

This study aims at examining the correlation of intraosseous temperature change with drilling impulse data during osteotomy and establishing real-time temperature prediction models.

MATERIALS AND METHODS

A combination of in vitro bovine rib model and Autonomous Dental Implant Robotic System (ADIR) was set up, in which intraosseous temperature and drilling impulse data were measured using an infrared camera and a six-axis force/torque sensor respectively. A total of 800 drills with different parameters (e.g., drill diameter, drill wear, drilling speed, and thickness of cortical bone) were experimented, along with an independent test set of 200 drills. Pearson correlation analysis was done for linear relationship. Four machining learning (ML) algorithms (e.g., support vector regression [SVR], ridge regression [RR], extreme gradient boosting [XGboost], and artificial neural network [ANN]) were run for building prediction models.

RESULTS

By incorporating different parameters, it was found that lower drilling speed, smaller drill diameter, more severe wear, and thicker cortical bone were associated with higher intraosseous temperature changes and longer time exposure and were accompanied with alterations in drilling impulse data. Pearson correlation analysis further identified highly linear correlation between drilling impulse data and thermal changes. Finally, four ML prediction models were established, among which XGboost model showed the best performance with the minimum error measurements in test set.

CONCLUSION

The proof-of-concept study highlighted close correlation of drilling impulse data with intraosseous temperature change during osteotomy. The ML prediction models may inspire future improvement on prevention of thermal bone injury and intelligent design of robot-assisted implant surgery.

Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings

BACKGROUND

Application of accumulated experience and management measures in the prevention and control of coronavirus disease 2019 (COVID-19) has generally depended on the subjective judgment of epidemic intensity, with the quality of prevention and control management being uneven. The present study was designed to develop a novel risk management system for COVID-19 infection in outpatients, with the ability to provide accurate and hierarchical control based on estimated risk of infection.

METHODS

Infection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results.

RESULTS

The ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results.

CONCLUSION

These innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections.

A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM

BACKGROUND

Hand, foot, and mouth disease (HFMD) is a common infectious disease that poses a serious threat to children all over the world. However, the current prediction models for HFMD still require improvement in accuracy. In this study, we proposed a hybrid model based on autoregressive integrated moving average (ARIMA), ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) to predict the trend of HFMD.

METHODS

The data used in this study was sourced from the National Clinical Research Center for Child Health and Disorders, Chongqing, China. The daily reported incidence of HFMD from 1 January 2015 to 27 July 2023 was collected to develop an ARIMA-EEMD-LSTM hybrid model. ARIMA, LSTM, ARIMA-LSTM and EEMD-LSTM models were developed to compare with the proposed hybrid model. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were adopted to evaluate the performances of the prediction models.

RESULTS

Overall, ARIMA-EEMD-LSTM model achieved the most accurate prediction for HFMD, with RMSE, MAPE and R2 of 4.37, 2.94 and 0.996, respectively. Performing EEMD on the residual sequence yields 11 intrinsic mode functions. EEMD-LSTM model is the second best, with RMSE, MAPE and R2 of 6.20, 3.98 and 0.996.

CONCLUSION

Results showed the advantage of ARIMA-EEMD-LSTM model over the ARIMA model, the LSTM model, the ARIMA-LSTM model and the EEMD-LSTM model. For the prevention and control of epidemics, the proposed hybrid model may provide a more powerful help. Compared with other three models, the two integrated with EEMD method showed significant improvement in predictive capability, offering novel insights for modeling of disease time series.

Evaluation of the impact of the COVID-19 pandemic on health service utilization in China: A study using auto-regressive integrated moving average model

Background

The outbreak of COVID-19 in early 2020 presented a major challenge to the healthcare system in China. This study aimed to quantitatively evaluate the impact of COVID-19 on health services utilization in China in 2020.

Methods

Health service-related data for this study were extracted from the China Health Statistical Yearbook. The Auto-Regressive Integrated Moving Average model (ARIMA) was used to forecast the data for the year 2020 based on trends observed between 2010 and 2019. The differences between the actual 2020 values reported in the statistical yearbook and the forecast values from the ARIMA model were used to assess the impact of COVID-19 on health services utilization.

Results

In 2020, the number of admissions and outpatient visits in China declined by 17.74 and 14.37%, respectively, compared to the ARIMA model’s forecast values. Notably, public hospitals experienced the largest decrease in outpatient visits and admissions, of 18.55 and 19.64%, respectively. Among all departments, the pediatrics department had the greatest decrease in outpatient visits (35.15%). Regarding geographical distribution, Beijing and Heilongjiang were the regions most affected by the decline in outpatient visits (29.96%) and admissions (43.20%) respectively.

Conclusion

The study’s findings suggest that during the first year of the COVID-19 pandemic, one in seven outpatient services and one in six admissions were affected in China. Therefore, there is an urgent need to establish a green channel for seeking medical treatment without spatial and institutional barriers during epidemic prevention and control periods.

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.

Intention and Attitude to Accept a Pertussis Cocooning Vaccination among Chinese Children's Guardians: A Cross-Sectional Survey

OBJECTIVE

to assess Chinese children's guardians' intentions and attitudes toward accepting a pertussis cocooning vaccination and its determinants.

METHODS

a self-administered questionnaire was designed based on a theoretical framework that originated mainly from the reasoned action approach. Associations between questionnaire variables and outcomes were assessed using univariate and multivariate analyses with odds ratios (OR), regression coefficients (β), and their 95% confidence intervals (CIs).

RESULTS

among 762 eligible participants, most (80.71%) reported a positive intention to accept a pertussis cocooning vaccination. The guardians' positive intention was related to the children's pertussis vaccination experience (OR = 2.483, 95% CI: 1.340-4.600). Guardians who had a positive attitude towards pertussis vaccination (OR = 1.554, 95% CI: 1.053-2.296), higher subjective norms (OR = 1.960, 95% CI: 1.371-2.802) and better perceived behavioral control (OR = 7.482, 95% CI: 4.829-11.591) stated a higher intention to receive a pertussis cocooning vaccination. The mean attitude score was 3.88 ± 0.863. Greater risk perception about pertussis (β = 0.390, 95% CI: 0.298-0.483), stronger obligation from moral norms (β = 0.355, 95% CI: 0.279-0.430), and good knowledge (β = 0.108, 95% CI: 0.070-0.146) were significantly related to positive attitude toward pertussis cocooning vaccination among guardians.

CONCLUSIONS

Chinese children's guardians held positive intentions and attitudes toward accepting a pertussis cocooning vaccination. The current findings described the determinants of such intention and attitude and provided knowledge based on improving guardians' intentions for policymakers if cocooning vaccinations or related immunization strategies are implemented in China in the future.

An investigation of the efficacy of different statistical models in malaria forecasting in the semi-arid regions of Gujarat, India

BACKGROUND & OBJECTIVES

Robust forecasting of malaria cases is desirable as we are approaching towards malaria elimination in India. Methods enabling robust forecasting and timely case detection in unstable transmission areas are the need of the hour.

METHODS

Forecasting efficacy of the eight most prominent statistical models that are based on three statistical methods: Generalized linear model (Model A and Model B), Smoothing method (Model C), and SARIMA (Model D to model H) were compared using last twelve years (2008-19) monthly malaria data of two districts (Kheda and Anand) of Gujarat state of India.

RESULTS

The SARIMA Model F was found the most appropriate when forecasted for 2017 and 2018 using model-building data sets 1 and 2, respectively, for both the districts: Kheda and Anand. Model H followed by model C were the two models found appropriate in terms of point estimates for 2019. Still, we regretted these two because confidence intervals from these models are wider that they do not have any forecasting utility. Model F is the third one in terms of point prediction but gives a relatively better confidence interval. Therefore, model F was considered the most appropriate for the year 2019 for both districts.

INTERPRETATION & CONCLUSION

Model F was found relatively more appropriate than others and can be used to forecast malaria cases in both districts.