The analysis of GM (1, 1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China

Prediction of Incidence Trend of Influenza-Like Illness in Wuhan Based on ARIMA Model

Objective

The autoregressive integrated moving average (ARIMA) model has been widely used to predict the trend of infectious diseases. This paper is aimed at analyzing the application of the ARIMA model in the prediction of the incidence trend of influenza-like illness (ILI) in Wuhan and providing a scientific basis for the prediction and prevention of influenza.

Methods

The weekly ILI data of two influenza surveillance sentinel hospitals in Wuhan City published on the website of the National Influenza Center of China were collected, and the ARIMA model was used to model the data from 2014 to 2020, to predict and verify the ILI data in 2021.

Results

The optimal model for the incidence trend of ILI in Wuhan was ARIMA (1, 1, 1), the residuals were in line with the white noise sequence (0.018 < Ljung-Box Q < 30.695, P > 0.05), and the relative error between the predicted value and the actual value was small, which all proved the model was practical.

Conclusion

ARIMA (1, 1, 1) can effectively simulate the short-term incidence trend of ILI in Wuhan.

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.

Impact of COVID-19 pandemic on the epidemiology of STDs in China: based on the GM (1,1) model

Background

COVID-19 and Sexually Transmitted Diseases (STDs) are two very important diseases. However, relevant researches about how COVID-19 pandemic has impacted on the epidemiological trend of STDs are limited in China. This study aimed to analyze the impact of COVID-19 on STDs in China and proposed relevant recommendations to be used in bettering health.

Methods

The incidence of HIV infection, syphilis and gonorrhea in China from 2008 to 2020 were collected. Grey Model (1,1) were established to predict the incidence of STDs with the incidence data of these three STDs from 2013 to 2018 considering the impact of policies in China, respectively. We then calculated the predictive incidence of each STD in 2019, 2020 and 2021 by the established Model. And we estimated the extent of the impact of COVID-19 on the epidemiological changes of STDs by analyzing the difference between the absolute percentage error (APE) of the predictive incidence and actual rate in 2019 and 2020.

Results

The incidence of HIV infection and syphilis showed a trend of increase from 2008 to 2019 in China, but that for gonorrhea was fluctuant. Of note, the incidence of these three STDs decreased significantly in 2020 compared with that in 2019. The APE of HIV infection, syphilis and gonorrhea in 2020 (20.54%, 15.45% and 60.88%) were about 7 times, 4 times and 2 times of that in 2019 (2.94%, 4.07% and 30.41%). The incidence of HIV infection, syphilis and gonorrhea would be 5.77/100,000, 39.64/100,000 and 13.19/100,000 in 2021 based on our model.

Conclusions

The epidemiological trend of STDs in China was significant influenced by COVID-19 pandemic. It is important to balance the control of COVID-19 and timely management of STDs during the COVID-19 epidemic to prevent or reduce the poor outcome among COVID-19 patients with STDs. New management strategies on STDs, such as leveraging social media, online medical care, rapid self-testing, timely diagnosis and treatment guarantee and balance of medical resources for STDs management should be adapted in the context of the long-term effects of COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07496-y.

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.

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

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

Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis

BACKGROUND

Brucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of great significance in controlling brucellosis and taking preventive measures.

METHODS

Our human brucellosis incidence data were extracted from Shanxi Provincial Center for Disease Control and Prevention. We used seasonal-trend decomposition using Loess (STL) and monthplot to analyse the seasonal characteristics of human brucellosis in Shanxi Province from 2007 to 2017. The autoregressive integrated moving average (ARIMA) model, a combined model of ARIMA and the back propagation neural network (ARIMA-BPNN), and a combined model of ARIMA and the Elman recurrent neural network (ARIMA-ERNN) were established separately to make predictions and identify the best model. Additionally, the mean squared error (MAE), mean absolute error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the performance of the model.

RESULTS

We observed that the time series of human brucellosis in Shanxi Province increased from 2007 to 2014 but decreased from 2015 to 2017. It had obvious seasonal characteristics, with the peak lasting from March to July every year. The best fitting and prediction effect was the ARIMA-ERNN model. Compared with those of the ARIMA model, the MAE, MSE and MAPE of the ARIMA-ERNN model decreased by 18.65, 31.48 and 64.35%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 60.19, 75.30 and 64.35%, respectively. Second, compared with those of ARIMA-BPNN, the MAE, MSE and MAPE of ARIMA-ERNN decreased by 9.60, 15.73 and 11.58%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 31.63, 45.79 and 29.59%, respectively.

CONCLUSIONS

The time series of human brucellosis in Shanxi Province from 2007 to 2017 showed obvious seasonal characteristics. The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA-BPNN and ARIMA models. This will provide some theoretical support for the prediction of infectious diseases and will be beneficial to public health decision making.

Total Health Expenditure and Its Driving Factors in China: A Gray Theory Analysis

The continuous growth in total health expenditure (THE) has become a social issue of common concern in most countries. In China, the total health expenditure (THE) is maintaining a rapid growth trend that is higher than that of the economy, which has become increasingly obvious in the 21st century and has brought a heavy burden to the government and residents. To analyze the main driving factors of THE in China in the 21st century and establish a predictive model, gray system theory was employed to explore the correlation degree between THE and nine hot topics in the areas of the economy, population, health service utilization, and policy using national data from 2000 to 2018. Additionally, a New Structure of the Multivariate Gray Prediction Model of THE was established and compared with the traditional grey model and widely used BP neural network to evaluate the prediction effectiveness of the model. We concluded that the Chinese government and society have played a crucial role in reducing residents’ medical burden. Besides this, the improved economy and aging population have increased the demand for health services, leading to the continual increase in THE. Lastly, the improved NSGM(1,N) model achieved good prediction accuracy and has unique advantages in simulating and predicting THE, which can provide a basis for policy formulation.

Study on the relationship between the incidence of influenza and climate indicators and the prediction of influenza incidence

In recent 2 years, the incidence of influenza showed a slight upward trend in Guangxi; therefore, some joint actions should be done to help preventing and controlling this disease. The factors analysis of affecting influenza and early prediction of influenza incidence may help policy-making so as to take effective measures to prevent and control influenza. In this study, we used the cross correlation function (CCF) to analyze the effect of climate indicators on influenza incidence, ARIMA and ARIMAX (autoregressive integrated moving average model with exogenous input variables) model methods to do predictive analysis of influenza incidence. The results of CCF analysis showed that climate indicators (PM2.5, PM10, SO₂, CO, NO2, O₃, average temperature, maximum temperature, minimum temperature, average relative humidity, and sunshine duration) had significant effects on the incidence of influenza. People need to take good precautions in the days of severe air pollution and keep warm in cold weather to prevent influenza. We found that the ARIMAX (1,0,1)(0,0,1)₁₂ with NO₂ model has good predictive performance, which can be used to predict the influenza incidence in Guangxi, and the predicted incidence may be useful in developing early warning systems and providing important evidence for influenza control policy-making and public health intervention.

Which Should be Conserved According to Priority During Urban Expansion? Ecological Lands or Farmland?

Understanding the trade-off between the loss of ecological lands and farmland to urbanisation could alleviate negative consequences to the environment and food security. This study aims to analyse the impact of urbanisation on ecological lands and farmland by comparing respective losses of habitat quality and agricultural productivity potential. Different scenarios are established indicating the priority of conserving ecological lands or farmland based on their vulnerability as measured by weight. With a land-use simulation model LANDSCAPE, the demand for urban land is met by occupying inferior ecological lands or farmland. Land-use patterns in each scenario are projected to 2035 in Wuhan (a rapidly urbanising city in China). As the weight of habitat quality decreases, the loss of habitat quality increases from 2.56 to 5.80%, while the loss of agricultural productivity potential decreases from 4.16 to 2.94%. The higher vulnerability of habitat quality loss indicates that ecological lands need a priori conservation than farmland in exchange for urbanisation.

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.

A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China

In recent years, the incidence of hepatitis B (HB) in Guangxi is higher than that of the national level; it has been increasing, so it is urgent to do a good predictive research of HB incidence, which can help analyze the early warning of hepatitis B in Guangxi, China. In the study, the feasibility of predicting HB incidence in Guangxi by autoregressive integrated moving average (ARIMA) model method and Elman neural network (ElmanNN) method was discussed respectively, and the prediction accuracy of the two models was compared. Finally, we established the ARIMA (0, 1, 1) model and ElmanNN with 8 neurons. Both ARIMA (0, 1, 1) model and ElmanNN model had good performance, and their prediction accuracy were high. The fitting and prediction root-mean-square error (RMSE) and mean absolute error (MAE) of ElmanNN were smaller than those of ARIMA (0, 1, 1) model, which indicated that ElmanNN was superior to ARIMA (0, 1, 1) model in predicting the incidence of hepatitis B in Guangxi. Based on the ElmanNN, the HB incidence from September 2019 to December 2020 in Guangxi was predicted, the predicted results showed that the incidence of HB in 2020 was slightly higher than that in 2019 and the change trend was similar to that in 2019, for 2021 and beyond, the ElmanNN model could be used to continue the predictive analysis.

A proposed sustainable and digital collection and classification center model to manage e-waste in emerging economies

Purpose

This study aims to propose an electronic waste collection and classification system to enhance social, environmental and economic sustainability by integrating data-driven technologies in emerging economies.

Design/methodology/approach

GM (1, 1) model under grey prediction is used in this study in order to estimate the trend of the amount of collected electronic waste in emerging economies.

Findings

It is revealed that the amount of collected electronic waste is increasing day by day, and within the framework of sustainability in the process of collecting and classification of electronic waste, digital technologies were found to be lacking. It has been determined that this deficiency, together with the increasing amount of electronic waste, has caused environmental, social and economic damage to emerging economies.

Originality/value

The main originality of this study is integrating electronic waste collection and classification processes with data-driven technologies and sustainability, which is a relatively new subject.

A descriptive analysis of the Spatio-temporal distribution of intestinal infectious diseases in China

BACKGROUND

Intestinal infectious diseases (IIDs) have caused numerous deaths worldwide, particularly among children. In China, eight IIDs are listed as notifiable infectious diseases, including cholera, poliomyelitis, dysentery, typhoid and paratyphoid (TAP), viral Hepatitis A, viral Hepatitis E, hand-foot-mouth disease (HFMD) and other infectious diarrhoeal diseases (OIDDs). The aim of the study is to analyse the spatio-temporal distribution of IIDs from 2006 to 2016.

METHODS

Data on the incidence of IIDs from 2006 to 2016 were collected from the public health science data centre issued by the Chinese Center for Disease Control and Prevention. This study applied seasonal decomposition analysis, spatial autocorrelation analysis and space-time scan analysis. Plots and maps were constructed to visualize the spatio-temporal distribution of IIDs.

RESULTS

Regarding temporal analysis, the incidence of HFMD and Hepatitis E showed a distinct increasing trend, while the incidence of TAP, dysentery, and Hepatitis A presented decreasing trends over the last decade. The incidence of OIID remained steady. Summer is the season with the greatest number of cases of different IIDs. Regarding the spatial distribution, approximately all p values for the global Moran's I from 2006 to 2016 were less than 0.05, indicating that the incidences of the epidemics were unevenly distributed throughout the country. The high-risk areas for HFMD and OIDD were located in the Beijing-Tianjin-Tangshan (BTT) region and south China. The high-risk areas for TAP were located in some parts of southwest China. A higher incidence rates for dysentery and Hepatitis A were observed in the BTT region and some west provincial units. The high-risk areas for Hepatitis E were the BTT region and the Yangtze River Delta area.

CONCLUSIONS

Based on our temporal and spatial analysis of IIDs, we identified the high-risk periods and clusters of regions for the diseases. HFMD and OIDD exhibited high incidence rates, which reflected the negligence of Class C diseases by the government. At the same time, the incidence rate of Hepatitis E gradually surpassed Hepatitis A. The authorities should pay more attention to Class C diseases and Hepatitis E. Regardless of the various distribution patterns of IIDs, disease-specific, location-specific, and disease-combined interventions should be established.

Research on National Disaster Life Support Course in China

OBJECTIVES

To provide scientific, theoretical support for the improvement of medical disaster training, we systematically analyzed the National Disaster Life Support (NDLS) Course and established a training curriculum with feedback based on the current status of disaster medicine in China.

METHODS

The gray prediction model is applied to long-term forecast research on course effect. In line with the hypothesis, the NDLS course with feedback capability is more scientific and standardized.

RESULTS

The current training NDLS course system is suitable for Chinese medical disasters. After accepting the course training, audiences' capabilities were enhanced. In the constructed GM (1,1) model prediction, the developing coefficients of the pretest and the posttest are 0.04 and 0.057, respectively. In light of the coefficient, the model is appropriate for the long-term prediction. The predicted results can be used as the basis for constructing training closed-loop optimization feedback. It can indicate that the course system has a good effect as well.

CONCLUSIONS

According to the constructed GM model, the NDLS course system is scientific, practical, and operational. The research results can provide reference for relevant departments and be used for the construction of similar training course systems.

Trends in the Epidemiology of Sexually Transmitted Disease, Acquired Immune Deficiency Syndrome (AIDS), Gonorrhea, and Syphilis, in the 31 Provinces of Mainland China

BACKGROUND This study aimed to investigate trends in the epidemiology of the leading sexually transmitted diseases (STDs), acquired immune deficiency syndrome (AIDS), gonorrhea, and syphilis, in the 31 provinces of mainland China. MATERIAL AND METHODS This retrospective study analyzed the incidence data of STDs from official reports in China between 2004 and 2016. The grey model first order one variable, or GM (1,1), time series forecasting model for epidemiological studies predicted the incidence of STDs based on the annual incidence reports from 31 Chinese mainland provinces. Hierarchical cluster analysis was used to group the prevalence of STDs within each province. RESULTS The prediction accuracy of the GM (1,1) model was high, based on data during the 13 years between 2004 and 2016. The model predicted that the incidence rates of AIDS and syphilis would continue to increase over the next two years. Cluster analysis showed that 31 provinces could be classified into four clusters according to similarities in the incidence of STDs. Group A (Sinkiang Province) had the highest reported prevalence of syphilis. Group B included provinces with a higher incidence of gonorrhea, mainly in the southeast coast of China. Group C consisted of southwest provinces with a higher incidence of AIDS. CONCLUSIONS The GM (1,1) model was predictive for the incidence of STDs in 31 provinces in China. The predicted incidence rates of AIDS and syphilis showed an upward trend. Regional distribution of the major STDs highlights the need for targeted prevention and control programs.