Comparison of ARIMA and GM(1,1) models for prediction of hepatitis B in China

Temporal Trends in the Burden of Disease for Male Infertility from 1990 to 2021 in the BRICS

Background

Over the past three decades, male infertility has become a significant burden on global public health. As an international organization with nearly half of the world's population, BRICS plays a crucial role in global health. This study investigates the trend of male infertility burden in BRICS countries from 1990 to 2021, providing valuable information for prevention and treatment strategies.

Methods

Data on male infertility in BRICS countries were obtained from the Global Burden of Disease database. Joinpoint regression, decomposition analysis, and prediction models were applied to analyze the data and assess the disease burden trends.

Results

The global prevalence of male infertility has worsened significantly between 1990 and 2021, with projections indicating this trend will continue for the next 15 years. While this global trend is based on data from a range of countries, the results of this study specifically focus on the BRICS countries. In these countries, while China and the Russian Federation have had high prevalence rates, improvements were observed over the past 30 years. India and Brazil, though unable to control male infertility in this period, have managed to halt its worsening in recent years. South Africa experienced substantial fluctuations from 2001 to 2015, with further significant changes projected in the next 15 years.

Conclusion

This study provides valuable insights into the evolving burden of male infertility in BRICS countries. It underscores the importance of targeted prevention and treatment strategies for these countries based on national and global trends.

Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model

BACKGROUND

There may be evidence that COVID-19 affects illness patterns. This study aimed to assess the effects of COVID-19 epidemic on the declines in hepatitis B (HB) case notifications and to estimate the epidemiological trends of HB in Henan.

METHODS

The Bayesian structured time series (BSTS) method was used to investigate the causal effect of COVID-19 on the decline in HB cases based on the monthly incidence of HB from January 2013 to September 2022. To assess how well the BSTS algorithm performs predictions, we split the observations into various training and testing ranges.

RESULTS

The incidence of HB in Henan was generally declining with periodicity and seasonality. The seasonal index in September and February was the smallest (0.91 and 0.93), and that in March was the largest (1.19). Due to the COVID-19 pandemic, the monthly average number of notifications of HB cases decreased by 38% (95% credible intervals [CI]: -44% to -31%) from January to March 2020, by 24% (95% CI: -29% to -17%) from January to June 2020, by 15% (95% CI: -19% to -9.2%) from January to December 2020, by 11% (95% CI: -15% to -6.7%) from January 2020 to June 2021, and by 11% (95% CI: -15% to -7.3%) from January 2020 to December 2021. From January 2020 to September 2022, it decreased by 12% (95% CI: -16% to -8.1%). From 2021 to 2022, the impact of COVID-19 on HB was attenuated. In both training and test sets, the average absolute percentage error (10.03%) generated by the BSTS model was smaller than that generated by the ARIMA model (14.4%). It was also found that the average absolute error, root mean square error, and root mean square percentage error generated by the BSTS model were smaller than ones generated by the ARIMA model. The trend of HB cases in Henan from October 2022 to December 2023 predicted by the BSTS model remained stable, with a total number of 81,650 cases (95% CI: 47,372 to 115,391).

CONCLUSIONS

During the COVID-19 pandemic, the incidence of HB in Henan decreased and exhibited clear seasonal and cyclical trends. The BSTS model outperformed the ARIMA model in predicting the HB incidence trend in Henan. This information may serve as a reference and provide technical assistance for developing strategies and actions to prevent and control HB. Take additional measures to accelerate the progress of eliminating HB.

Comparison of ARIMA and Bayesian Structural Time Series Models for Predicting the Trend of Syphilis Epidemic in Jiangsu Province

Purpose

This study sets out to explore the forecasting value in syphilis incidence of the Bayesian structural time series (BSTS) model in Jiangsu Province.

Methods

The seasonal autoregressive integrated moving average (ARIMA) and BSTS models were constructed using the series from January 2017 to December 2021, and the prediction accuracy of both models was tested using the series from January 2022 to November 2022.

Results

From January 2017 to November 2022, the total number of syphilis cases in Jiangsu Province was 170629, with an average monthly notification cases of 2403. The optimal model was ARIMA (1,0,0) (0,1,1) 12 (AIC = 663.12, AICc = 664.05, and BIC = 670.60). The model coefficients were further tested: AR1 = 0.48 (t = 3.46, P < 0.001), and SMA1 =-0.48 (t =-2.32, P = 0.01). The mean absolute deviation, mean absolute percentage error, root mean square error, and root mean square percentage error from the BSTS model were smaller than those from the ARIMA model. The total number of syphilis cases predicted by the BSTS model from December 2022 to December 2023 in Jiangsu Province was 29902 (95% CI: 16553 ~ 42,401), with a monthly average of 2300 (95% CI: 1273 ~ 3262) cases.

Conclusion

Syphilis is a seasonal disease in Jiangsu Province, and its incidence is still at a high level. The BSTS model is superior to the ARIMA model in dynamically predicting the incidence trend of syphilis in Jiangsu Province and has better application value.

Temporal disruption in tuberculosis incidence patterns during COVID-19: a time series analysis in China

Background

Despite extensive knowledge of tuberculosis (TB) and its control, there remains a significant gap in understanding the comprehensive impact of the COVID-19 pandemic on TB incidence patterns. This study aims to explore the impact of COVID-19 on the pattern of pulmonary tuberculosis in China and examine the application of time series models in the analysis of these patterns, providing valuable insights for TB prevention and control.

Methods

We used pre-COVID-19 pulmonary tuberculosis (PTB) data (2007-2018) to fit SARIMA, Prophet, and LSTM models, assessing their ability to predict PTB incidence trends. These models were then applied to compare the predicted PTB incidence patterns with actual reported cases during the COVID-19 pandemic (2020-2023), using deviations between predicted and actual values to reflect the impact of COVID-19 countermeasures on PTB incidence.

Results

Prior to the COVID-19 outbreak, PTB incidence in China exhibited a steady decline with strong seasonal fluctuations, characterized by two annual peaks-one in March and another in December. These seasonal trends persisted until 2019. During the COVID-19 pandemic, there was a significant reduction in PTB cases, with actual reported cases falling below the predicted values. The disruption in PTB incidence appears to be temporary, as 2023 data indicate a gradual return to pre-pandemic trends, though the incidence rate remains slightly lower than pre-COVID levels. Additionally, we compared the fitting and forecasting performance of the SARIMA, Prophet, and LSTM models using RMSE (root mean squared error), MAE (mean absolute error), and MAPE (mean absolute percentage error) indexes prior to the COVID-19 outbreak. We found that the Prophet model had the lowest values for all three indexes, demonstrating the best fitting and prediction performance.

Conclusions

The COVID-19 pandemic has had a temporary but significant impact on PTB incidence in China, leading to a reduction in reported cases during the pandemic. However, as pandemic control measures relax and the healthcare system stabilizes, PTB incidence patterns are expected to return to pre-COVID-19 levels. The Prophet model demonstrated the best predictive performance and proves to be a valuable tool for analyzing PTB trends and guiding public health planning in the post-pandemic era.

Evaluating the effectiveness of self-attention mechanism in tuberculosis time series forecasting

BACKGROUND

With the increasing impact of tuberculosis on public health, accurately predicting future tuberculosis cases is crucial for optimizing of health resources and medical service allocation. This study applies a self-attention mechanism to predict the number of tuberculosis cases, aiming to evaluate its effectiveness in forecasting.

METHODS

Monthly tuberculosis case data from Changde City between 2010 and 2021 were used to construct a self-attention model, a long short-term memory (LSTM) model, and an autoregressive integrated moving average (ARIMA) model. The performance of these models was evaluated using three metrics: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).

RESULTS

The self-attention model outperformed the other models in terms of prediction accuracy. On the test set, the RMSE of the self-attention model was approximately 7.41% lower than that of the LSTM model, MAE was reduced by about 10.99%, and MAPE was reduced by approximately 9.87%. Compared to the ARIMA model, RMSE was reduced by about 28.86%, MAE by about 32.22%, and MAPE by approximately 29.89%.

CONCLUSION

The self-attention model can effectively improve the prediction accuracy of tuberculosis cases, providing guidance for health departments optimizing of health resources and medical service allocation.

Spatial and temporal analysis and forecasting of TB reported incidence in western China

OBJECTIVE

Tuberculosis (TB) remains an important public health concern in western China. This study aimed to explore and analyze the spatial and temporal distribution characteristics of TB reported incidence in 12 provinces and municipalities in western China and to construct the optimal models for prediction, which would provide a reference for the prevention and control of TB and the optimization of related health policies.

METHODS

We collected monthly data on TB reported incidence in 12 provinces and municipalities in western China and used ArcGIS software to analyze the spatial and temporal distribution characteristics of TB reported incidence. We applied the seasonal index method for the seasonal analysis of TB reported incidence and then established the SARIMA and Holt-Winters models for TB reported incidence in 12 provinces and municipalities in western China.

RESULTS

The reported incidence of TB in 12 provinces and municipalities in western China showed apparent spatial clustering characteristics, and Moran's I was greater than 0 (p < 0.05) over 8 years during the reporting period. Among them, Tibet was the hotspot for TB incidence in 12 provinces and municipalities in western China. The reported incidence of TB in 12 provinces and municipalities in western China from 2004 to 2018 showed clear seasonal characteristics, with seasonal indices greater than 100% in both the first and second quarters. The optimal models constructed for TB reported incidence in 12 provinces and municipalities in western China all passed white noise test (p > 0.05).

CONCLUSIONS

As a hotspot of reported TB incidence, Tibet should continue to strengthen government leadership and policy support, explore TB intervention strategies and causes. The optimal prediction models we developed for reported TB incidence in 12 provinces and municipalities in western China were different.

Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time-series model

This study aims to explore the application value of the Bayesian Time Structure Sequence (BSTS) model in estimating the acute hemorrhagic conjunctivitis (AHC) epidemics. The reported AHC cases spanning from January 2011 to October 2022 in China were collated. Utilizing R software, the BSTS and Autoregressive Integrated Moving Average (ARIMA) models were constructed using the data from January 2011 to December 2021. The prediction effect of both models was compared using the data from January to October 2022, and finally the AHC incidence from November 2022 to December 2023 was predicted. The results indicated that forecast errors under the BSTS model were lower than those under the ARIMA model. The actual AHC incidence in July 2022 from the ARIMA model deviated from the 95% confidence interval (CI) of the predicted value. However, the observed AHC incidence from the BSTS model fell within the 95% CI of the predicted value. Notably, the BSTS model predicted 26,474 new AHC cases in China from November 2022 to December 2023, exhibiting better prediction performance compared to the ARIMA model. This indicates that the BSTS model possesses a high application value for forecasting the epidemic trends of AHC, making it a valuable tool for disease surveillance and prevention strategies.

Temporal trend analysis of acute hepatitis B virus infection in China, 1990-2019

China faces challenges in meeting the World Health Organization (WHO)'s target of reducing hepatitis B virus (HBV) infections by 95% using 2015 as the baseline. Using Global Burden of Disease (GBD) 2019 data, joinpoint regression models were used to analyse the temporal trends in the crude incidence rates (CIRs) and age-standardized incidence rates (ASIRs) of acute HBV (AHBV) infections in China from 1990 to 2019. The age-period-cohort model was used to estimate the effects of age, period, and birth cohort on AHBV infection risk, while the Bayesian age-period-cohort (BAPC) model was applied to predict the annual number and ASIRs of AHBV infections in China through 2030. The joinpoint regression model revealed that CIRs and ASIRs decreased from 1990 to 2019, with a faster decline occurring among males and females younger than 20 years. According to the age-period-cohort model, age effects showed a steep increase followed by a gradual decline, whereas period effects showed a linear decline, and cohort effects showed a gradual rise followed by a rapid decline. The number of cases of AHBV infections in China was predicted to decline until 2030, but it is unlikely to meet the WHO's target. These findings provide scientific support and guidance for hepatitis B prevention and control.

Evaluating the impact of COVID-19 outbreak on hepatitis B and forecasting the epidemiological trend in mainland China: a causal analysis

BACKGROUND

It is uncertain how COVID-19 outbreak influences the hepatitis B epidemics. This study aims to evaluate the effects on hepatitis B owing to the COVID-19 outbreak and forecast the hepatitis B epidemiological trend in mainland China to speed up the course of the "End viral hepatitis Strategy".

METHODS

We estimated the causal impacts and created a forecast through adopting monthly notifications of hepatitis B each year from 2005 to 2020 in mainland China using the Bayesian structural time series (BSTS) method.

RESULTS

The hepatitis B epidemics fluctuates irregularly during the period 2005-2007(APC = 8.7, P = 0.246) and 2015-2020(APC = 1.7, P = 0.290), and there is a downturn (APC=-3.2, 95% CI -5.2 to -1.2, P = 0.006) from 2007 to 2015 in mainland China. The COVID-19 outbreak was found to have a monthly average reduction on the hepatitis B epidemics of 26% (95% CI 18-35%) within the first three months in 2020,17% (95% CI 7.7-26%) within the first six months in 2020, and 10% (95% CI19-22%) all year as a result of the COVID-19 outbreak, (probability of causal effect = 96.591%, P = 0.034) and the forecasts showed an upward trend from 2021 to 2025 (annual percentage change = 4.18, 95% CI 4.0 to 4.3, P < 0.001).

CONCLUSION

The COVID-19 has a positive effect on the decline of hepatitis B cases. And the potential of BSTS model to forecast the epidemiological trend of the hepatitis B can be applied in automatic public health policymaking in mainland China.

Comparison of SARIMA model, Holt-winters model and ETS model in predicting the incidence of foodborne disease

BACKGROUND

According to the World Health Organization, foodborne disease is a significant public health issue. We will choose the best model to predict foodborne disease by comparison, to provide evidence for government policies to prevent foodborne illness.

METHODS

The foodborne disease monthly incidence data from June 2017 to April 2022 were obtained from the Chongqing Nan'an District Center for Disease Prevention and Control. Data from June 2017 to June 2021 were used to train the model, and the last 10 months of incidence were used for prediction and validation The incidence was fitted using the seasonal autoregressive integrated moving average (SARIMA) model, Holt-Winters model and Exponential Smoothing (ETS) model. Besides, we used MSE, MAE, RMSE to determine which model fits better.

RESULTS

During June 2017 to April 2022, the incidence of foodborne disease showed seasonal changes, the months with the highest incidence are June to November. The optimal model of SARIMA is SARIMA (1,0,0) (1,1,0)12. The MSE, MAE, RMSE of the Holt-Winters model are 8.78, 2.33 and 2.96 respectively, which less than those of the SARIMA and ETS model, and its prediction curve is closer to the true value. The optimal model has good predictive performance.

CONCLUSION

Based on the results, Holt-Winters model produces better prediction accuracy of the model.

Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China

BACKGROUND

Hepatitis B (HB) and hepatitis C (HC) place the largest burden in China, and a goal of eliminating them as a major public health threat by 2030 has been set. Making more informed and accurate forecasts of their spread is essential for developing effective strategies, heightening the requirement for early warning to deal with such a major public health threat.

AIM

To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average (SARFIMA) for projections into 2030, and to compare the effectiveness with the seasonal autoregressive integrated moving average (SARIMA).

METHODS

Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023. Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality. Two periods (from January 2004 to June 2022 and from January 2004 to December 2015, respectively) were used as the training sets to develop both models, while the remaining periods served as the test sets to evaluate the forecasting accuracy.

RESULTS

There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023. Overall, HB remained steady [average annual percentage change (AAPC) = 0.44, 95% confidence interval (95%CI): -0.94-1.84] while HC was increasing (AAPC = 8.91, 95%CI: 6.98-10.88), and both had a peak in March and a trough in February. In the 12-step-ahead HB forecast, the mean absolute deviation (15211.94), root mean square error (18762.94), mean absolute percentage error (0.17), mean error rate (0.15), and root mean square percentage error (0.25) under the best SARFIMA (3, 0, 0) (0, 0.449, 2)12 were smaller than those under the best SARIMA (3, 0, 0) (0, 1, 2)12 (16867.71, 20775.12, 0.19, 0.17, and 0.27, respectively). Similar results were also observed for the 90-step-ahead HB, 12-step-ahead HC, and 90-step-ahead HC forecasts. The predicted HB incidents totaled 9865400 (95%CI: 7508093-12222709) cases and HC totaled 1659485 (95%CI: 856681-2462290) cases during 2023-2030.

CONCLUSION

Under current interventions, China faces enormous challenges to eliminate HB and HC epidemics by 2030, and effective strategies must be reinforced. The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions, surpassing the capabilities of SARIMA.

Retrospective evaluation of short-term forecast performance of ensemble sub-epidemic frameworks and other time-series models: The 2022-2023 mpox outbreak across multiple geographical scales, July 14th, 2022, through February 26th, 2023

In May 2022, public health officials noted an unprecedented surge in mpox cases in non-endemic countries worldwide. As the epidemic accelerated, multi-model forecasts of the epidemic's trajectory were critical in guiding the implementation of public health interventions and determining policy. As the case levels have significantly decreased as of early September 2022, evaluating model performance is essential to advance the growing field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention (CDC) and Our World in Data (OWID) teams through the week of January 26th, 2023, we generated retrospective sequential weekly forecasts (e.g., 1-4-weeks) for Brazil, Canada, France, Germany, Spain, the United Kingdom, the USA, and at the global scale using models that require minimal input data including the auto-regressive integrated moving average (ARIMA), general additive model (GAM), simple linear regression (SLR), Facebook's Prophet model, as well as the sub-epidemic wave (spatial-wave) and n -sub-epidemic modeling frameworks. We assess forecast performance using average mean squared error (MSE), mean absolute error (MAE), weighted interval score (WIS), 95% prediction interval coverage (95% PI coverage), and skill scores. Average Winkler scores were used to calculate skill scores for 95% PI coverage. Overall, the n -sub-epidemic modeling framework outcompeted other models across most locations and forecasting horizons, with the unweighted ensemble model performing best across all forecasting horizons for most locations regarding average MSE, MAE, WIS, and 95% PI coverage. However, many locations had multiple models performing equally well for the average 95% PI coverage. The n -sub-epidemic and spatial-wave frameworks improved considerably in average MSE, MAE, and WIS, and Winkler scores (95% PI coverage) relative to the ARIMA model. Findings lend further support to sub-epidemic frameworks for short-term forecasting epidemics of emerging and re-emerging infectious diseases.

Prediction of reported monthly incidence of hepatitis B in Hainan Province of China based on SARIMA-BPNN model

In recent years, the incidence of hepatitis B has been serious in Hainan Province of China. To construct a statistical model of the monthly incidence of hepatitis B in Hainan Province of China and predict the monthly incidence of hepatitis B in 2022. Simple central moving average method and seasonal index were used to analyze the trend and seasonal effects of monthly incidence of hepatitis B. Based on the time series of reported monthly incidence of hepatitis B in Hainan Province from 2017 to 2020, a multiplicative seasonal model (SARIMA), multiplicative seasonal model combined with error back propagation neural network model (SARIMA-BPNN), and a gray prediction model were constructed to fit the incidence, and the time series of monthly incidence of hepatitis B in 2021 was used to verify the accuracy of models. The lowest and highest monthly incidence of hepatitis B in Hainan Province were in February and August, respectively, and MAPE of SARIMA, SARIMA-BPNN, and gray prediction models were 0.089, 0.087, and 0.316, respectively. The best fitting model is the SARIMA-BPNN model. The predicted monthly incidence of hepatitis B in 2022 showed a downward trend, with the steepest decline in March, which indicates that the prevention and control of hepatitis B in Hainan Province is effective, and the study can provide scientific and reasonable suggestions for the prevention and control of hepatitis B in Hainan.

Forecasting the Spread of COVID-19 Using Deep Learning and Big Data Analytics Methods

To contain the spread of the COVID-19 pandemic, there is a need for cutting-edge approaches that make use of existing technology capabilities. Forecasting its spread in a single or multiple countries ahead of time is a common strategy in most research. There is, however, a need for all-inclusive studies that capitalize on the entire regions on the African continent. This study closes this gap by conducting a wide-ranging investigation and analysis to forecast COVID-19 cases and identify the most critical countries in terms of the COVID-19 pandemic in all five major African regions. The proposed approach leveraged both statistical and deep learning models that included the autoregressive integrated moving average (ARIMA) model with a seasonal perspective, the long-term memory (LSTM), and Prophet models. In this approach, the forecasting problem was considered as a univariate time series problem using confirmed cumulative COVID-19 cases. The model performance was evaluated using seven performance metrics that included the mean-squared error, root mean-square error, mean absolute percentage error, symmetric mean absolute percentage error, peak signal-to-noise ratio, normalized root mean-square error, and the R2 score. The best-performing model was selected and used to make future predictions for the next 61 days. In this study, the long short-term memory model performed the best. Mali, Angola, Egypt, Somalia, and Gabon from the Western, Southern, Northern, Eastern, and Central African regions, with an expected increase of 22.77%, 18.97%, 11.83%, 10.72%, and 2.81%, respectively, were the most vulnerable countries with the highest expected increase in the number of cumulative positive cases.

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.

Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models

BACKGROUND

The public health sectors can use the forecasting applications to determine vaccine stock requirements to avoid excess or shortage stock. This prediction will ensure that immunization protection for COVID- 19 is well-distributed among African citizens.

OBJECTIVE

The aim of this study is to forecast vaccination rate for COVID-19 in Africa.

METHODS

The method used to estimate predictions is the hybrid forecasting models which predicts the COVID-19 vaccination rate (CVR). HARIMA is a hybrid of ARIMA and the Linear Regression model and HGRNN is a hybrid of Generalized Regression Neural Network (GRNN) and the Gaussian Process Regression (GPR) model which are used to improve predictive accuracy.

RESULTS

In this study, standard and hybrid forecasting models are used to evaluate new COVID-19 vaccine cases daily in May and June 2021. To evaluate the effectiveness of the models, the COVID-19 vaccine dataset for Africa was used, which included new vaccine cases daily from 13 January 2021 to 16 May 2021. Root Mean Squared Error (RMSE) and Error Percentage (EP) are used as evaluation measures in this process. The results obtained showed that the hybrid GRNN model performed better than the hybrid ARIMA model.

CONCLUSION

HGRNN model provides accurate daily vaccinated case forecast, which helps to maintain optimal vaccine stock to avoid vaccine wastage and save many lives.

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

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

Autoregressive models in environmental forecasting time series: a theoretical and application review

Though globalization, industrialization, and urbanization have escalated the economic growth of nations, these activities have played foul on the environment. Better understanding of ill effects of these activities on environment and human health and taking appropriate control measures in advance are the need of the hour. Time series analysis can be a great tool in this direction. ARIMA model is the most popular accepted time series model. It has numerous applications in various domains due its high mathematical precision, flexible nature, and greater reliable results. ARIMA and environment are highly correlated. Though there are many research papers on application of ARIMA in various fields including environment, there is no substantial work that reviews the building stages of ARIMA. In this regard, the present work attempts to present three different stages through which ARIMA was evolved. More than 100 papers are reviewed in this study to discuss the application part based on pure ARIMA and its hybrid modeling with special focus in the field of environment/health/air quality. Forecasting in this field can be a great contributor to governments and public at large in taking all the required precautionary steps in advance. After such a massive review of ARIMA and hybrid modeling involving ARIMA in the fields including or excluding environment/health/atmosphere, it can be concluded that the combined models are more robust and have higher ability to capture all the patterns of the series uniformly. Thus, combining several models or using hybrid model has emerged as a routinized custom.

ARIMA model for predicting chronic kidney disease and estimating its economic burden in China

BACKGROUND

Chronic kidney disease (CKD) is an important global public health issue. In China, CKD affects a large number of patients and causes a huge economic burden. This study provided a new way to predict the number of patients with CKD and estimate its economic burden in China based on the autoregressive integrated moving average (ARIMA) model.

METHODS

Data of the number of patients with CKD in China from 2000 to 2019 were obtained from the Global Burden of Disease. The ARIMA model was used to fit and predict the number of patients with CKD. The direct and indirect economic burden of CKD were estimated by the bottom-up approach and the human capital approach respectively.

RESULTS

The results of coefficient of determination (0.99), mean absolute percentage error (0.26%), mean absolute error (343,193.8) and root mean squared error (628,230.3) showed that the ARIMA (1,1,1) model fitted well. Akaike information criterion (543.13) and Bayesian information criterion (546.69) indicated the ARIMA (1,1,1) model was reliable when analyzing our data. The result of relative error of prediction (0.23%) also suggested that the model predicted well. The number of patients with CKD in 2020 to 2025 was predicted to be about 153 million, 155 million, 157 million, 160 million, 163 million and 165 million respectively, accounting for more than 10% of the Chinese population. The total economic burden of CKD from 2019 to 2025 was estimated to be $179 billion, $182 billion, $185 billion, $188 billion, $191 billion, $194 billion and $198 billion respectively.

CONCLUSION

The number of patients with CKD and the economic burden of CKD will continue to rise in China. The number of patients with CKD in China would increase by 2.6 million (1.6%) per year on average from 2020 to 2025. Meanwhile, the total economic burden of CKD in China would increase by an average of $3.1 billion per year. The ARIMA model is applicable to predict the number of patients with CKD. This study provides a new perspective for more comprehensive understanding of the future risk of CKD.

Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014-2017

Humans are susceptible to influenza. The influenza virus spreads quickly and behave seasonally. The seasonality and spread of influenza are often associated with meteorological factors and have spatio-temporal differences. Based on the influenza cases and daily average meteorological factors in Lanzhou from 2014 to 2017, this study firstly aimed to analyze the characteristics of influenza incidence in Lanzhou and the impact of meteorological factors on influenza activities. Then, SARIMA(X) models for the prediction were established. The influenza cases in Lanzhou from 2014 to 2017 was more male than female, and the younger the age, the higher the susceptibility; the epidemic characteristics showed that there is a peak in winter, a secondary peak in spring, and a trough in summer and autumn. The influenza cases in Lanzhou increased with increasing daily pressure, decreasing precipitation, average relative humidity, hours of sunshine, average daily temperature and average daily wind speed. Low temperature was a significant driving factor for the increase of transmission intensity of seasonal influenza. The SARIMAX (1,0,0)(1,0,1)[12] multivariable model with average temperature has better prediction performance than the university model. This model is helpful to establish an early warning system, and provide important evidence for the development of influenza control policies and public health interventions.

Incidence of pulmonary tuberculosis under the regular COVID-19 epidemic prevention and control in China

Background

The COVID-19 pandemic has driven public health intervention strategies, including keeping social distance, wearing masks in crowded places, and having good health habits, to prevent the transmission of the novel coronavirus (SARS-CoV-2). However, it is unknown whether the use of these intervention strategies influences morbidity in other human infectious diseases, such as tuberculosis.

Methods

In this study, three prediction models were constructed to compare variations in PTB incidences after January 2020 without or with intervention includes strict and regular interventions, when the COVID-19 outbreak began in China. The non-interventional model was developed with an autoregressive integrated moving average (ARIMA) model that was trained with the monthly incidence of PTB in China from January 2005 to December 2019. The interventional model was established using an ARIMA model with a continuing intervention function that was trained with the monthly PTB incidence in China from January 2020 to December 2020.

Results

Starting with the assumption that no COVID-19 outbreak had occurred in China, PTB incidence was predicted, and then the actual incidence was compared with the predicted incidence. A remarkable overall decline in PTB incidence from January 2020 to December 2020 was observed, which was likely due to the potential influence of intervention policies for COVID-19. If the same intervention strategy is applied for the next 2 years, the monthly PTB incidence would reduce on average by about 1.03 per 100,000 people each month compared with the incidence predicted by the non-interventional model. The annual incidence estimated 59.15 under regular intervention per 100,000 in 2021, and the value would decline to 50.65 with strict interventions.

Conclusions

Our models quantified the potential knock-on effect on PTB incidence of the intervention strategy used to control the transmission of COVID-19 in China. Combined with the feasibility of the strategies, these results suggested that continuous regular interventions would play important roles in the future prevention and control of PTB.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07620-y.

Modeling and Forecasting Monkeypox Cases Using Stochastic Models

BACKGROUND

Monkeypox virus is gaining attention due to its severity and spread among people. This study sheds light on the modeling and forecasting of new monkeypox cases. Knowledge about the future situation of the virus using a more accurate time series and stochastic models is required for future actions and plans to cope with the challenge.

METHODS

We conduct a side-by-side comparison of the machine learning approach with the traditional time series model. The multilayer perceptron model (MLP), a machine learning technique, and the Box-Jenkins methodology, also known as the ARIMA model, are used for classical modeling. Both methods are applied to the Monkeypox cumulative data set and compared using different model selection criteria such as root mean square error, mean square error, mean absolute error, and mean absolute percentage error.

RESULTS

With a root mean square error of 150.78, the monkeypox series follows the ARIMA (7,1,7) model among the other potential models. Comparatively, we use the multilayer perceptron (MLP) model, which employs the sigmoid activation function and has a different number of hidden neurons in a single hidden layer. The root mean square error of the MLP model, which uses a single input and ten hidden neurons, is 54.40, significantly lower than that of the ARIMA model. The actual confirmed cases versus estimated or fitted plots also demonstrate that the multilayer perceptron model has a better fit for the monkeypox data than the ARIMA model.

CONCLUSIONS AND RECOMMENDATION

When it comes to predicting monkeypox, the machine learning method outperforms the traditional time series. A better match can be achieved in future studies by applying the extreme learning machine model (ELM), support vector machine (SVM), and some other methods with various activation functions. It is thus concluded that the selected data provide a real picture of the virus. If the situations remain the same, governments and other stockholders should ensure the follow-up of Standard Operating Procedures (SOPs) among the masses, as the trends will continue rising in the upcoming 10 days. However, governments should take some serious interventions to cope with the virus.

LIMITATION

In the ARIMA models selected for forecasting, we did not incorporate the effect of covariates such as the effect of net migration of monkeypox virus patients, government interventions, etc.

The research of SARIMA model for prediction of hepatitis B in mainland China

Hepatitis B virus infection is a major global public health concern. This study explored the epidemic characteristics and tendency of hepatitis B in 31 provinces of mainland China, constructed a SARIMA model for prediction, and provided corresponding preventive measures.Monthly hepatitis B case data from mainland China from 2013 to 2020 were obtained from the website of the National Health Commission of the People's Republic of China. Monthly data from 2013 to 2020 were used to build the SARIMA model and data from 2021 were used to test the model.Between 2013 and 2020, 9,177,313 hepatitis B cases were reported in mainland China. SARIMA(1,0,0)(0,1,1)12 was the optimal model and its residual was white noise. It was used to predict the number of hepatitis B cases from January to December 2021, and the predicted values for 2021 were within the 95% confidence interval.This study suggests that the SARIMA model simulated well based on epidemiological trends of hepatitis B in mainland China. The SARIMA model is a feasible tool for monitoring hepatitis B virus infections in mainland China.

Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries

The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world’s economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths from COVID-19 in 12 hard-hit big countries around the world as of 20 August 2021. The data used in the analysis were extracted from the Our World in Data COVID-19 dataset. Both non-seasonal and seasonal autoregressive integrated moving averages (ARIMA and SARIMA) were estimated. The analysis showed that: (i) ARIMA/SARIMA forecasts were sufficiently accurate in both the training and test set by always outperforming the simple alternative forecasting techniques chosen as benchmarks (Mean, Naïve, and Seasonal Naïve); (ii) SARIMA models outperformed ARIMA models in 47 out 48 metrics (in forecasting future values), i.e., on 97.9% of all the considered forecast accuracy measures (mean absolute error [MAE], mean absolute percentage error [MAPE], mean absolute scaled error [MASE], and the root mean squared error [RMSE]), suggesting a clear seasonal pattern in the data; and (iii) the forecasted values from SARIMA models fitted very well the observed (real-time) data for the period 21 August 2021–19 September 2021 for almost all the countries analyzed. This article shows that SARIMA can be safely used for both the short- and medium-term predictions of COVID-19 deaths. Thus, this approach can help government authorities to monitor and manage the huge pressure that COVID-19 is exerting on national healthcare systems.

Epidemiological analysis of varicella in Dalian from 2009 to 2019 and application of three kinds of model in prediction prevalence of varicella

Objective

This study described the epidemic characteristics of varicella in Dalian from 2009 to 2019, explored the fitting effect of Grey model first-order one variable( GM(1,1)), Markov model, and GM(1,1)-Markov model on varicella data, and found the best fitting method for this type of data, to better predict the incidence trend.

Methods

For this Cross-sectional study, this article was completed in 2020, and the data collection is up to 2019. Due to the global epidemic, the infectious disease data of Dalian in 2020 itself does not conform to the normal changes of varicella and is not included. The epidemiological characteristics of varicella from 2009 to 2019 were analyzed by epidemiological descriptive methods. Using the varicella prevalence data from 2009 to 2018, predicted 2019 and compared with actual value. First made GM (1,1) prediction and Markov prediction. Then according to the relative error of the GM (1,1), made GM (1,1)-Markov prediction.

Results

This study collected 37,223 cases from China Information System for Disease Control and Prevention's “Disease Prevention and Control Information System” and the cumulative population was 73,618,235 from 2009 to 2019. The average annual prevalence was 50.56/100000. Varicella occurred all year round, it had a bimodal distribution. The number of cases had two peaks from April to June and November to January of the following year. The ratio of males to females was 1.17:1. The 4 to 25 accounted for 60.36% of the total population. The age of varicella appeared to shift backward. Students, kindergarten children, scattered children accounted for about 64% of all cases. The GM(1,1) model prediction result of 2019 would be 53.64, the relative error would be 14.42%, the Markov prediction result would be 56.21, the relative error would be 10.33%, and the Gray(1,1)-Markov prediction result would be 59.51. The relative error would be 5.06%.

Conclusions

Varicella data had its unique development characteristics. The accuracy of GM (1,1)—Markov model is higher than GM(1.1) model and Markov model. The model can be used for prediction and decision guidance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-12898-3.

The evaluation of preventive and control measures on congenital syphilis in Guangdong Province, China: a time series modeling study

Purpose

To evaluate the effectiveness of preventive and control measures for congenital syphilis (CS) implemented since 2012 in Guangdong Province, China, and assess the epidemic trend in the near future.

Methods

The interrupted time series analysis was conducted to compare changes in slope and level of CS notification rate from 2005 to 2020 in Guangdong Province and its three regions with different economic developmental levels. The ARIMA model was established to predict the new CS case number of Guangdong Province in 2021.

Results

A total of 12,687 CS cases were reported from 2005 to 2020. The CS notification rate of the province had been increasing until 2012 (128.55 cases per 100,000 live births) and then been decreasing constantly, hitting the lowest point in 2020 (5.76 cases per 100,000 live births). The severe epidemic cluster shifted from the developed region to underdeveloped ones over time. The effectiveness of the measures was proved by the significant change in the slope of the notification rate which was found in both of the provinces (− 18.18, 95% CI − 25.63 to − 10.75) and two less-developed regions (− 10.49, 95% CI − 13.13 to − 7.86 and − 32.89, 95% CI − 41.67 to − 24.10, respectively). In the developed region where the notification rate had already been decreasing in the pre-implementation period, implementing these measures also aided in hastening the rate of descent. The CS case number in 2021 was predicted to be 48, indicating a low-level epidemic.

Conclusions

The preventive and control measures have assisted Guangdong Province to control CS effectively, of which the supportive ones ensured a successful implementation. For resource-limited countries where CS is still endemic, especially guaranteeing the support in financial subsidy, professional training, supervision and so on might trigger the effectiveness of other measures and eventually make significant and sustainable progress.

Supplementary Information

The online version contains supplementary material available at 10.1007/s15010-022-01791-1.

The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China

Background and objective

Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population’s health but also affects economic and social development. It requires an accurate prediction analysis to help to make policymakers with early warning and provide effective precautionary measures. In this study, ARIMA, GM(1,1), and LSTM models were constructed and compared, respectively. The results showed that the LSTM was the optimal model, which can be achieved satisfactory performance for TB cases predictions in mainland China.

Methods

The data of tuberculosis cases in mainland China were extracted from the National Health Commission of the People’s Republic of China website. According to the TB data characteristics and the sample requirements, we created the ARIMA, GM(1,1), and LSTM models, which can make predictions for the prevalence trend of TB. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were applied to evaluate the effects of model fitting predicting accuracy.

Results

There were 3,021,995 tuberculosis cases in mainland China from January 2018 to December 2020. And the overall TB cases in mainland China take on a downtrend trend. We established ARIMA, GM(1,1), and LSTM models, respectively. The optimal ARIMA model is the ARIMA (0,1,0) × (0,1,0)12. The equation for GM(1,1) model was X(k+1) = -10057053.55e^(-0.01k) + 10153178.55 the Mean square deviation ratio C value was 0.49, and the Small probability of error P was 0.94. LSTM model consists of an input layer, a hidden layer and an output layer, the parameters of epochs, learning rating are 60, 0.01, respectively. The MAE, RMSE, and MAPE values of LSTM model were smaller than that of GM(1,1) and ARIMA models.

Conclusions

Our findings showed that the LSTM model was the optimal model, which has a higher accuracy performance than that of ARIMA and GM (1,1) models. Its prediction results can act as a predictive tool for TB prevention measures in mainland China.

Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China

Objectives

This study intends to build and compare two kinds of forecasting models at different time scales for hemorrhagic fever incidence in China.

Methods

Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) were adopted to fit monthly, weekly and daily incidence of hemorrhagic fever in China from 2013 to 2018. The two models, combined and uncombined with rolling forecasts, were used to predict the incidence in 2019 to examine their stability and applicability.

Results

ARIMA (2, 1, 1) (0, 1, 1)₁₂, ARIMA (1, 1, 3) (1, 1, 1)₅₂ and ARIMA (5, 0, 1) were selected as the best fitting ARIMA model for monthly, weekly and daily incidence series, respectively. The LSTM model with 64 neurons and Stochastic Gradient Descent (SGDM) for monthly incidence, 8 neurons and Adaptive Moment Estimation (Adam) for weekly incidence, and 64 neurons and Root Mean Square Prop (RMSprop) for daily incidence were selected as the best fitting LSTM models. The values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the models combined with rolling forecasts in 2019 were lower than those of the direct forecasting models for both ARIMA and LSTM. It was shown from the forecasting performance in 2019 that ARIMA was better than LSTM for monthly and weekly forecasting while the LSTM was better than ARIMA for daily forecasting in rolling forecasting models.

Conclusions

Both ARIMA and LSTM could be used to build a prediction model for the incidence of hemorrhagic fever. Different models might be more suitable for the incidence prediction at different time scales. The findings can provide a good reference for future selection of prediction models and establishments of early warning systems for hemorrhagic fever.

Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast milk production in the northern region of Thailand using time-series forecast methods. A single-technique model, including seasonal autoregressive integrated moving average (SARIMA) and error trend seasonality (ETS), and a hybrid model of SARIMA-ETS were applied to milk production data to develop forecast models. The performance of the models developed was compared using several error matrices. Results showed that milk production was forecasted to raise by 3.2 to 3.6% annually. The SARIMA-ETS hybrid model had the highest forecast performances compared with other models, and the ETS outperformed the SARIMA in predictive ability. Furthermore, the forecast models highlighted a continuously increasing trend with evidence of a seasonal fluctuation for future milk production. The results from this study emphasizes the need for an effective plan and strategy to manage milk production to alleviate a possible oversupply. Policymakers and stakeholders can use our forecasts to develop short- and long-term strategies for managing milk production.

Comparison of ARIMA, ES, GRNN and ARIMA–GRNN hybrid models to forecast the second wave of COVID-19 in India and the United States

As acute infectious pneumonia, the coronavirus disease-2019 (COVID-19) has created unique challenges for each nation and region. Both India and the United States (US) have experienced a second outbreak, resulting in a severe disease burden. The study aimed to develop optimal models to predict the daily new cases, in order to help to develop public health strategies. The autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models, ARIMA–GRNN hybrid model and exponential smoothing (ES) model were used to fit the daily new cases. The performances were evaluated by minimum mean absolute per cent error (MAPE). The predictive value with ARIMA (3, 1, 3) (1, 1, 1)₁₄ model was closest to the actual value in India, while the ARIMA–GRNN presented a better performance in the US. According to the models, the number of daily new COVID-19 cases in India continued to decrease after 27 May 2021. In conclusion, the ARIMA model presented to be the best-fit model in forecasting daily COVID-19 new cases in India, and the ARIMA–GRNN hybrid model had the best prediction performance in the US. The appropriate model should be selected for different regions in predicting daily new cases. The results can shed light on understanding the trends of the outbreak and giving ideas of the epidemiological stage of these regions.

Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models

BACKGROUND AND OBJECTIVES

Auto regressive integrated moving average (ARIMA) model is a popular model to forecast future values of a time series using the past values of the same series. However, if the variance of the time series varies with time, the 95% confidence interval estimated by the ARIMA will not be accurate. This study proposes a method to revise the ARIMA model to suit time series with heteroscedasticity.

METHODS

Multiple historical ARIMA models were constructed with publicly available COVID-19 data in Alberta, Canada. The time series between different time periods were applied for these models. The means and their 95% confidence intervals of the differences between the forecasted values and the corresponding actual values were computed. The forecasted values of the general ARIMA models were modified by adding these differences.

RESULTS

The average incident cases forecasted with the proposed method are lower than those with a general ARIMA model during the forecasted period. The 95% confidence intervals of the forecasted incidence with the proposed method are narrower. During the forecasted period (13 weeks) the average incidence was predicted to increase first and then decrease exponentially.

CONCLUSION

The proposed method can be used to automatically specify the best ARIMA model, to fit time series with heteroscedasticity and to forecast longer period of the trends in the future. In the next 13 weeks, the Covid-19 incidence may decrease but not eliminate. To stop the transmission of infections eventually, persistent effects complying with accurate forecasts are necessary.

Estimating the Long-Term Epidemiological Trends and Seasonality of Hemorrhagic Fever with Renal Syndrome in China

OBJECTIVE

We aim to examine the adequacy of an innovation state-space modeling framework (called TBATS) in forecasting the long-term epidemic seasonality and trends of hemorrhagic fever with renal syndrome (HFRS).

METHODS

The HFRS morbidity data from January 1995 to December 2020 were taken, and subsequently, the data were split into six different training and testing segments (including 12, 24, 36, 60, 84, and 108 holdout monthly data) to investigate its predictive ability of the TBATS method, and its forecasting performance was compared with the seasonal autoregressive integrated moving average (SARIMA).

RESULTS

The TBATS (0.27, {0,0}, -, {<12,4>}) and SARIMA (0,1,(1,3))(0,1,1)12 were selected as the best TBATS and SARIMA methods, respectively, for the 12-step ahead prediction. The mean absolute deviation, root mean square error, mean absolute percentage error, mean error rate, and root mean square percentage error were 91.799, 14.772, 123.653, 0.129, and 0.193, respectively, for the preferred TBATS method and were 144.734, 25.049, 161.671, 0.203, and 0.296, respectively, for the preferred SARIMA method. Likewise, for the 24-, 36-, 60-, 84-, and 108-step ahead predictions, the preferred TBATS methods produced smaller forecasting errors over the best SARIMA methods. Further validations also suggested that the TBATS model outperformed the Error-Trend-Seasonal framework, with little exception. HFRS had dual seasonal behaviors, peaking in May-June and November-December. Overall a notable decrease in the HFRS morbidity was seen during the study period (average annual percentage change=-6.767, 95% confidence intervals: -10.592 to -2.778), and yet different stages had different variation trends. Besides, the TBATS model predicted a plateau in the HFRS morbidity in the next ten years.

CONCLUSION

The TBATS approach outperforms the SARIMA approach in estimating the long-term epidemic seasonality and trends of HFRS, which is capable of being deemed as a promising alternative to help stakeholders to inform future preventive policy or practical solutions to tackle the evolving scenarios.

Hybrid Model for Unemployment Impact on Social Life

In this paper, we want to examine how unemployment impacts social life, and, by using datasets from six European countries, we analyze the effect of unemployment on two of the main aspects of social life: social exclusion and life satisfaction. First, we predict unemployment rates using the Auto Regressive Integrated Moving Average (ARIMA) model and the results are further used in a linear regression model alongside social exclusion and life satisfaction data, thus obtaining the hybrid model. With the help of the point prediction method, we use the hybrid model to predict new values for the two aspects of social life for the upcoming three years and we analyze the results obtained in order to better understand their interconnection. The results suggest that unemployment has particularly adverse effects on the subjective perception of life satisfaction, furthermore increasing the social exclusion percentage.

Machine learning-based diffusion model for prediction of coronavirus-19 outbreak

The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model.

On the accuracy of ARIMA based prediction of COVID-19 spread

COVID-19 was declared a global pandemic by the World Health Organization in March 2020, and has infected more than 4 million people worldwide with over 300,000 deaths by early May 2020. Many researchers around the world incorporated various prediction techniques such as Susceptible-Infected-Recovered model, Susceptible-Exposed-Infected-Recovered model, and Auto Regressive Integrated Moving Average model (ARIMA) to forecast the spread of this pandemic. The ARIMA technique was not heavily used in forecasting COVID-19 by researchers due to the claim that it is not suitable for use in complex and dynamic contexts. The aim of this study is to test how accurate the ARIMA best-fit model predictions were with the actual values reported after the entire time of the prediction had elapsed. We investigate and validate the accuracy of an ARIMA model over a relatively long period of time using Kuwait as a case study. We started by optimizing the parameters of our model to find a best-fit through examining auto-correlation function and partial auto correlation function charts, as well as different accuracy measures. We then used the best-fit model to forecast confirmed and recovered cases of COVID-19 throughout the different phases of Kuwait's gradual preventive plan. The results show that despite the dynamic nature of the disease and constant revisions made by the Kuwaiti government, the actual values for most of the time period observed were well within bounds of our selected ARIMA model prediction at 95% confidence interval. Pearson's correlation coefficient for the forecast points with the actual recorded data was found to be 0.996. This indicates that the two sets are highly correlated. The accuracy of the prediction provided by our ARIMA model is both appropriate and satisfactory.

Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model

Objective

The high morbidity, complex seasonality, and recurring risk of hand-foot-and-mouth disease (HFMD) exert a major burden in China. Forecasting its epidemic trends is greatly instrumental in informing vaccine and targeted interventions. This study sets out to investigate the usefulness of an advanced exponential smoothing state space framework by combining Box-Cox transformations, Fourier representations with time-varying coefficients and autoregressive moving average (ARMA) error correction (TBATS) method to assess the temporal trends of HFMD in China.

Methods

Data from January 2009 to December 2019 were drawn, and then they were split into two segments comprising the in-sample training data and out-of-sample testing data to develop and validate the TBATS model, and its fitting and forecasting abilities were compared with the most frequently used seasonal autoregressive integrated moving average (SARIMA) method.

Results

Following the modelling procedures of the SARIMA and TBATS methods, the SARIMA (1,0,1)(0,1,1)₁₂ and TBATS (0.024, {1,1}, 0.855, {<12,4>}) specifications were recognized as being the optimal models, respectively, for the 12-step ahead forecasting, along with the SARIMA (1,0,1)(0,1,1)₁₂ and TBATS (0.062, {1,3}, 0.86, {<12,4>}) models as being the optimal models, respectively, for the 24-step ahead forecasting. Among them, the optimal TBATS models produced lower error rates in both 12-step and 24-step ahead forecasting aspects compared to the preferred SARIMA models. Descriptive analysis of the data showed a significantly high level and a marked dual seasonal pattern in the HFMD morbidity.

Conclusion

The TBATS model has the capacity to outperform the most frequently used SARIMA model in forecasting the HFMD incidence in China, and it can be recommended as a flexible and useful tool in the decision-making process of HFMD prevention and control in China.

Operative Times in Primary Total Knee Arthroplasty: Can We Predict the Future Based on Contemporary Nationwide Data

Recently, the Centers for Medicare & Medicaid Services announced its decision to review "potentially misvalued" Current Procedural Terminology (CPT) codes, including those for primary total knee arthroplasty (TKA). CPT 27447 is being reevaluated to determine contemporary relative value units for work value, with operative time considered a primary factor in this revaluation. Despite broader indications for TKA, including extension of the procedure to more complex patient populations, it is unknown whether operative times may remain stable in the future. Therefore, the purpose of this study was to specifically evaluate future trends in TKA operative times across a large sample from a national database. The American College of Surgeons National Surgical Quality Improvement Project database was queried from January 1, 2008 to December 31, 2017 to identify 286,816 TKAs using the CPT code 27447. Our final analysis included 140,890 TKAs. Autoregressive integrated moving average forecasting models were built to predict 2- and 10-year operative times. While operative times were significantly different between American Society of Anesthesiologists (ASA) classes 1 and 2 (p = 0.035), there were not enough patients in ASA class 1 to perform rigorous inference. Additionally, operative times were not significantly different between ASA classes 3 and the combined ASA classes 4 and 5 cohort (p = 0.95). Therefore, we were only able to perform forecasts for ASA classes 2 and 3. Operative time was found to be nonstationary for both ASA class 2 (p = 0.08269) and class 3 (p = 0.2385). As a whole, the projection models indicated that operative time will remain within 2 minutes of the present operative time, up to the year 2027. Our projections indicate that operative times will remain stable over the next decade. This suggests that there is a lack of evidence for reducing the valuation of CPT code 27477 based on intraservice time for TKA. Further study should examine operative time trends in the setting of evolving alternative payment models, increasing patient complexity, and governmental restrictions.

Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China

Background: This study intends to identify the best model for predicting the incidence of hand, foot and mouth disease (HFMD) in Ningbo by comparing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) models combined and uncombined with exogenous meteorological variables. Methods: The data of daily HFMD incidence in Ningbo from January 2014 to November 2017 were set as the training set, and the data of December 2017 were set as the test set. ARIMA and LSTM models combined and uncombined with exogenous meteorological variables were adopted to fit the daily incidence of HFMD by using the data of the training set. The forecasting performances of the four fitted models were verified by using the data of the test set. Root mean square error (RMSE) was selected as the main measure to evaluate the performance of the models. Results: The RMSE for multivariate LSTM, univariate LSTM, ARIMA and ARIMAX (Autoregressive Integrated Moving Average Model with Exogenous Input Variables) was 10.78, 11.20, 12.43 and 14.73, respectively. The LSTM model with exogenous meteorological variables has the best performance among the four models and meteorological variables can increase the prediction accuracy of LSTM model. For the ARIMA model, exogenous meteorological variables did not increase the prediction accuracy but became the interference factor of the model. Conclusions: Multivariate LSTM is the best among the four models to fit the daily incidence of HFMD in Ningbo. It can provide a scientific method to build the HFMD early warning system and the methodology can also be applied to other communicable diseases.

Auto-Regressive Integrated Moving Average Model (ARIMA): conceptual and methodological aspects and applicability in infant mortality

Abstract This reflective theoretical article, aims to discuss conceptual and methodological aspects about the applications of time series modeling, in particular, the Integrated Auto-regressive Moving Average model and its applicability in infant mortality. This modeling makes it possible to predict future values using past data, outlining and estimating possible scenarios of the health event, highlighting its magnitude. Due to the persistence of infant mortality as a public health problem, the applicability of this method is useful in the timely and systematic management of child health indicators, in addition to being a method with low operating cost, which in contexts of cost reduction in public healthcare services, becomes a potential management tool. However, there are still gaps in the use of statistical methods in the decision-making and policy-making process in public healthcare, such as the modeling in question. These are methodological (robust statistics), institutional (outdated information systems) and cultural obstacles (devaluation of the data produced, mainly at the local level).

Prognosticating the Spread of Covid-19 Pandemic Based on Optimal Arima Estimators

COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19 from the explicit data based on optimal ARIMA model estimators. Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and the Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to the number of autoregressive terms, d refers to the number of times the series has to be differenced before it becomes stationary, and q refers to the number of moving average terms. Results obtained from the ARIMA model showed a significant decrease in cases in Australia; a stable case for China and rising cases have been observed in other countries. This study predicted the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

Operative times in primary total hip arthroplasty will remain stable up to the year 2027: prediction models based on 85,808 cases

PURPOSE

Recently, the Centers for Medicare and Medicaid have announced the decision to review "potentially misvalued" Current Procedural Terminology codes, including those for primary total hip arthroplasty (THA). While recent studies have suggested that THA operative times have remained stable in recent years, there is an absence of information regarding how operative times are expected to change in the future. Therefore, the purpose of our analysis was to produce 2- and 10-year prediction models developed from contemporary operative time data.

METHODS

Utilizing the American College of Surgeons National Surgical Quality Improvement patient database, all primary THA procedures performed between January 1st, 2008 and December 31st, 2017 were identified (n = 85,808 THA patients). Autocorrelation fit significance was determined through Box-Ljung lack of fit tests. Time series stationarity was evaluated using augmented Dickey-Fuller tests. After adjusting non-stationary time series for seasonality-dependent changes, 2-year and 10-year operative times were predicted using Autoregressive integrated moving average forecasting models.

RESULTS

Our models indicate that operative time will continue to remain stable. Specifically, operative time for ASA Class 2 is projected to fall within 1 min of the previously calculated weighted mean. Additionally, ASA Class 3 projections fall within 3 min of this value.

CONCLUSION

Operative time will remain within 3 min of the most recently reported mean up to the year 2027. Therefore, our findings do not support lowering physician compensation based on this metric. Future analyses should evaluate if operative times adjust over in light of changing patient demographics and alternative reimbursement models.

Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology

Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models.

A novel grey model based on traditional Richards model and its application in COVID-19

In 2020, a new type of coronavirus is in the global pandemic. Now, the number of infected patients is increasing. The trend of the epidemic has attracted global attention. Based on the traditional Richards model and the differential information principle in grey prediction model, this paper uses the modified grey action quantity to propose a new grey prediction model for infectious diseases. This model weakens the dependence of the Richards model on single-peak and saturated S-shaped data, making Richards model more applicable, and uses genetic algorithm to optimize the nonlinear terms and the background value. To illustrate the effectiveness of the model, groups of slowly growing small-sample and large-sample data are selected for simulation experiments. Results of eight evaluation indexes show that the new model is better than the traditional GM(1,1) and grey Richards model. Finally, this model is applied to China, Italy, Britain and Russia. The results show that the new model is better than the other 7 models. Therefore, this model can effectively predict the number of daily new confirmed cases of COVID-19, and provide important prediction information for the formulation of epidemic prevention policies.

Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections

The outbreak of novel coronavirus (COVID-19) attracted worldwide attention. It has posed a significant challenge for the global economies, especially the healthcare sector. Even with a robust healthcare system, countries were not prepared for the ramifications of COVID-19. Several statistical, dynamic, and mathematical models of the COVID-19 outbreak including the SEIR model have been developed to analyze the infection its transmission dynamics. The objective of this research is to use public data to study the properties associated with the COVID-19 pandemic to develop a dynamic hybrid model based on SEIRD and ascertainment rate with automatically selected parameters. The proposed model consists of two parts: the modified SEIRD dynamic model and ARIMA models. We fit SEIRD model parameters against historical values of infected, recovered and deceased population divided by ascertainment rate, which, in turn, is also a parameter of the model. Residuals of the first model for infected, recovered, and deceased populations are then corrected using ARIMA models. The model can analyze the input data in real-time and provide long- and short-term forecasts with confidence intervals. The model was tested and validated on the US COVID statistics dataset from the COVID Tracking Project. For validation, we use unseen recent statistical data. We use five common measures to estimate model prediction ability: MAE, MSE, MLSE, Normalized MAE, and Normalized MSE. We proved a great model ability to make accurate predictions of infected, recovered, and deceased patients. The output of the model can be used by the government, private sectors, and policymakers to reduce health and economic risks significantly improved consumer credit scoring.

Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China

Background

Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB.

Methods

We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018.

Results

Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively.

Conclusions

Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.

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

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

Analysis and Estimation of COVID-19 Spreading in Russia Based on ARIMA Model

Russia has been currently in the "hard-hit" area of the COVID-19 outbreak, with more than 396,000 confirmed cases as of May 30. It is necessary to analyze and predict its epidemic situation to help formulate effective public health policies. Autoregressive integrated moving average (ARIMA) models were developed to predict the cumulative confirmed, dead, and recovered cases, respectively. R 3.6.2 software was used to fit the data from January 31 to May 20, 2020, and predict the data for the next 30 days. The COVID-19 epidemic in Russia was divided into two stages and reached its peak in May. The epidemic began to stabilize on May 19. The case fatality rate has been at an extremely low level. ARIMA (2,2,1), ARIMA (3,2,0), and ARIMA (0,2,1) were the models of cumulative confirmed, dead, and recovered cases, respectively. After testing, the mean absolute percentage error (MAPE) of three models were 0.6, 3.9, and 2.4, respectively. This paper indicates that Russia's health system capacity can effectively respond to the COVID-19 pandemic. Three ARIMA models have a good fitting effect and can be used for short-term prediction of the COVID-19 trend, providing a theoretical basis for Russia to formulate new intervention policies.

Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India

Objective

The aim of this study is to apply the advanced error-trend-seasonal (ETS) framework to forecast the prevalence and mortality series of COVID-19 in the USA, the UK, Russia, and India, and the predictive performance of the ETS framework was compared with the most frequently used autoregressive integrated moving average (ARIMA) model.

Materials and Methods

The prevalence and mortality data of COVID-19 in the USA, the UK, Russia, and India between 20 February 2020 and 15 May 2020 were extracted from the WHO website. Then, the data subsamples between 20 February 2020 and 3 May 2020 were treated as the training horizon, and the others were used as the testing horizon to construct the ARIMA models and the ETS models.

Results

Based on the model evaluation criteria, the ARIMA (0,2,1) and ETS (M,MD,N), sparse coefficient ARIMA (0,2,(1,6)) and ETS (A,AD,M), ARIMA (1,1,1) and ETS (A,MD,A), together with ARIMA (2,2,1) and ETS (A,M,A) specifications were identified as the preferred ARIMA and ETS models for the prevalence data in the USA, the UK, Russia, and India, respectively; the ARIMA (0,2,1) and ETS (M,A,M), ARIMA (0,2,1) and ETS (M,A,N), ARIMA (0,2,1) and ETS (A,A,N), coupled with ARIMA (0,2,2) and ETS (M,M,N) specifications were selected as the optimal ARIMA and ETS models for the mortality data in these four countries, respectively. Among these best-fitting models, the ETS models produced smaller forecasting error rates than the ARIMA models in all the datasets.

Conclusion

The ETS framework can be used to nowcast and forecast the long-term temporal trends of the COVID-19 prevalence and mortality in the USA, the UK, Russia, and India, and which provides a notable performance improvement over the most frequently used ARIMA model. Our findings can aid governments as a reference to prepare for and respond to the COVID-19 pandemic both in restricting the transmission of the disease and in lowering the disease-related deaths in the upcoming days.

Prediction of hepatitis E using machine learning models

BACKGROUND

Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared.

METHODS

Autoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE).

RESULTS

By analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence prediction, using ARIMA, SVM and LSTM. And we obtained 22.25, 20.0368, 11.75 for cases number prediction, using three models. For MAPE metrics, the results were 23.5%, 21.7%, 15.08%, and 23.6%, 21.44%, 13.6%, for incidence prediction and cases number prediction, respectively. For MAE metrics, the results were 0.018, 0.0167, 0.011 and 18.003, 16.5815, 9.984, for incidence prediction and cases number prediction, respectively.

CONCLUSIONS

Comparing ARIMA, SVM and LSTM, we found that nonlinear models(SVM, LSTM) outperform linear models(ARIMA). LSTM obtained the best performance in all three metrics of RSME, MAPE, MAE. Hence, LSTM is the most suitable for predicting hepatitis E monthly incidence and cases number.

Prediction of Daily Blood Sampling Room Visits Based on ARIMA and SES Model

This paper is aimed at establishing a combined prediction model to predict the demand for medical care in terms of daily visits in an outpatient blood sampling room, which provides a basis for rational arrangement of human resources and planning. On the basis of analyzing the comprehensive characteristics of the randomness, periodicity, trend, and day-of-the-week effects of the daily number of blood collections in the hospital, we firstly established an autoregressive integrated moving average model (ARIMA) model to capture the periodicity, volatility, and trend, and secondly, we constructed a simple exponential smoothing (SES) model considering the day-of-the-week effect. Finally, a combined prediction model of the residual correction is established based on the prediction results of the two models. The models are applied to data from 60 weeks of daily visits in the outpatient blood sampling room of a large hospital in Chengdu, for forecasting the daily number of blood collections about 1 week ahead. The result shows that the MAPE of the combined model is the smallest overall, of which the improvement during the weekend is obvious, indicating that the prediction error of extreme value is significantly reduced. The ARIMA model can extract the seasonal and nonseasonal components of the time series, and the SES model can capture the overall trend and the influence of regular changes in the time series, while the combined prediction model, taking into account the comprehensive characteristics of the time series data, has better fitting prediction accuracy than a single model. The new model can well realize the short-to-medium-term prediction of the daily number of blood collections one week in advance.