Epidemic status and model analysis of cardiovascular and cerebrovascular diseases attributed to renal insufficiency in elderly Chinese / 公共卫生与预防医学

Objective To analyze the changing trend of disease burden attributable to renal insufficiency in cardiovascular disease (CVD) among the elderly in China from 1990 to 2019, and to forecast the disability-adjusted life years (DALY) in the next 10 years, so as to provide a reference basis for accurate prevention and control of CVD attributable to renal insufficiency in China. Methods Data were obtained from the Global Health Data Exchange (GHDx) database to describe the current status of CVD prevalence attributable to renal insufficiency. The joinpoint model was used to estimate the annual percentage change and average annual percentage change to assess the temporal trend of CVD attributable to renal insufficiency in China. An autoregressive moving average model was created by R4.0.2 software to predict the disease burden of CVD attributable to renal insufficiency in China. Results Compared with 1990, CVD mortality and DALY rates attributed to renal insufficiency increased in the male elderly population and decreased in women. Mortality and DALY rates attributed to ischemic heart disease, ischemic stroke, and peripheral arterial disease attributed to renal insufficiency showed an increasing trend, and mortality and DALY rates for cerebral hemorrhage decreased. There was an overall increasing trend in the attribution of CVD due to renal insufficiency. Conclusion The burden of diseases attributable to renal insufficiency in Chinese elderly with CVD is relatively high, and the impact on each disease is different, which requires the attention of relevant authorities.

Application of ARIMA model in predicting pediatric red blood cell usage / 中国输血杂志

【Objective】 To explore the feasibility of using autoregressive moving average model (ARIMA) to predict the dosage of suspended red blood cells in children, and to provide a basis for the development of clinical blood reserve plans in children's hospitals. 【Methods】 ARIMA model was constructed using the total blood consumption of clinical suspended red blood cells from March 2016 to May 2022 at the Children's Hospital of Chongqing Medical University as the data source by SPSS26.0 software. The optimal model was used to predict the clinical suspended red blood cell consumption from June to October 2022, and the predictive effect of the model was tested. 【Results】 ARIMA(0, 1, 1) (0, 1, 1)12 was the optimal model for predicting the consumption of suspended red blood cells in pediatrics. The autocorrelation function and partial autocorrelation function of the residual sequence basically fell within the 95% confidence interval. At the same time, Ljung-Box Q statistical results showed that there was no correlation between the residual (P>0.05), indicating that the residual was white noise, which met the randomicity hypothesis. The average relative error between the predicted values of the model and the actual clinical red blood cell usage from June to October 2022 was 5%, indicating high prediction accuracy. 【Conclusion】 The blood usage of children has obvious seasonal and periodic patterns, and the optimal model ARIMA (0, 1, 1) (0, 1, 1)12 can better fit the trend of changes in pediatric suspended red blood cell usage, thus providing a basis for the development of clinical blood reserve plans in children's hospitals.

Epidemiological characteristics and prediction of varicella trends in Yangpu District， Shanghai， from 2005 to 2022 / 上海预防医学

ObjectiveTo analyze the epidemiological characteristics of varicella in Yangpu District， Shanghai from 2005 to 2022， predict the trend of varicella in Yangpu District in 2023， and provide evidence for prevention and control of varicella outbreaks. MethodsInformation of varicella cases reported in Yangpu District from 2005 to 2022 was obtained from the China Information System for Disease Control and Prevention. Descriptive statistics was used to characterize the varicella epidemiology. An autoregressive integrated moving average （ARIMA） model was established by using the number of cases per month from 2005 to2022 to predict the trend of varicella epidemics in Yangpu District in 2023. The varicella incidence in 2022 was used to evaluate the fitness of the ARIMA model. ResultsFrom January 2005 to December 2021， a total of 11 527 cases of varicella were reported in Yangpu District， Shanghai. After excluding duplicates and clinical diagnoses， 11 413 cases were included into the analysis. The annual average incidence rate was 51.87/105， the age of onset was mainly under 20 years old （66.5%）， and the occupation was mainly students （49.7%）. The ARIMA （1，1，0）×（0，1，1）12 model was constructed and showed a good fitness while using monthly reported varicella cases in 2022 for model fitting. It was predicted that 1 089 cases of varicella would be reported in Yangpu District in 2023. ConclusionIt is predicted that varicella cases in Yangpu District will increase in 2023. It is recommended to continue promoting delayed varicella vaccination to maintain a high level of vaccination rate. Before the peak of the epidemic， health education regarding varicella should be strengthened， and measures for epidemic prevention and control should be reinforced to prevent varicella outbreaks.

The predictive effect of ARIMA model for occupational pneumoconiosis in Guangdong Province / 中国职业医学

Objective: To verify the accuracy of the autoregressive integrated moving average (ARIMA) in predicting the incidence of occupational pneumoconiosis (hereinafter referred as pneumoconiosis) and to predict the incidence of pneumoconiosis in Guangdong Province in the next five years. Methods: A follow-up survey was performed to collect data on pneumoconiosis patients reported in Guangdong Province from 1956 to 2021. Collected data from 1956 to 2016 were used as the training set to build an ARIMA model. Collected data from 2017 to 2021 were used as the prediction set to evaluate the predicting result of the ARIMA model. The ARIMA model was used to predict the incidence of pneumoconiosis in Guangdong Province in next five years. Results: The ARIMA (1,1,2) model was set up after model identification and order estimation. The model was used to predict the prediction set, and its result was good. The ARIMA result and actual values in 2021 were 213 and 210 cases, respectively, with a difference of only three cases. The number of pneumoconiosis cases predicted using the ARIMA model in Guangdong Province from 2022 to 2026 was 214, 204, 202, 194, and 191 cases, respectively, showing a trend of low-level prevalence. Conclusion: The ARIMA model demonstrates high accuracy in predicting pneumoconiosis incidence over a long period of time and with large sample sizes. The forecast results of the ARIMA(1,1,2) model indicate that the incidence of pneumoconiosis in Guangdong Province will be around 200 cases in the next five years, indicating a low-level prevalence.

Predictive analysis of the number of hospitalized patients with acute pancreatitis based on time series model / 中华胰腺病杂志

Objective:To predict and analyze the number of acute pancreatitis (AP) inpatients based on time series model, and to explore the predictive efficiency of the model.Methods:Clinical data of AP inpatients in the Affiliated Hospital of Southwest Medical University from January 2014 to December 2019 were collected. R software was used to collect the time series of AP inpatients, and the trend and seasonal characteristics of AP inpatients from 2014 to 2018 were analyzed. Furthermore, the autoregressive moving average (ARIMA) model was established through stationarity test, model ordering and model testing steps, and the best selected model was used to predict the monthly number of inpatients in 2019 to verify its prediction efficiency.Results:A total of 3 939 AP patients were included in the study. The most common etiology for AP was cholestrogenic (48.2%), followed by hyperacylglyceremia (36.3%). The peak age of hospitalization was from 40 to 60 years old. Time series analysis showed that the number of AP inpatients increased year by year. The highest peak of the disease was from February to March, followed by September to November; and there was seasonal variation and the incidence was relatively small in summer. The established original training set sequence did not pass the stationarity test ( P=0.061), so the ARIMA model was established after it was transformed into a stationarity sequence by first-order difference. According to the criterion of minimum AIC value, ARIMA(2, 1, 1)(1, 1, 1) 12 was selected as the best model. The model was used to predict the number of AP inpatients in 2019, showing that it could better fit the trend of onset time and had good short-term prediction effect. The mean root error and absolute error were 6.8790 and 4.7783, respectively. Conclusions:The number of AP inpatients increases year by year with seasonal changes. ARIMA model is effective in predicting the number of AP inpatients and can be used for short-term prediction.

Epidemiological characteristics analysis and incidence trend prediction of gonorrhea in Hubei Province in 2010-2021 / 公共卫生与预防医学

Objective To analyze the epidemiological characteristics and incidence trend of gonorrhea in Hubei Province, and to provide reference for scientific formulation of prevention and control measures. Methods Based on the surveillance data of gonorrhea from 2010 to 2021, three-way distribution and ARIMA model were used for data analysis and incidence prediction. Results From 2010 to 2021, the reported incidence rate fluctuated between 3.01/100 000-7.07/100 000, and the average annual reported incidence rate was 4.62/100 000. The reported incidence rate showed the characteristics of “first fall and then rise, and then fall and rise again”, and the peak incidence period was from June to December. The male to female ratio of reported cases was 5.78:1, and the number of reported cases in the age group of 20-39 years old accounted for 62.43% of the total number of cases. The reported cases were mainly housework and unemployed, farmers, and unknown occupation. The severity of the regional incidence was divided into 5 categories by the Q-type clustering, and the most serious category included Shennongjia Forest District, Huangshi City, and Wuhan City. The ARIMA model predicted the incidence rate to be in good agreement with the actual incidence rate, with a predicted number of 3 343 cases in 2022. Conclusion At present, gonorrhea in Hubei Province is still at a high prevalence level. There are obvious differences in gender, age, occupation, and regional distribution. The ARIMA model is suitable for predicting the incidence of gonorrhea, and it is predicted that the incidence will increase slightly in 2022.

Application of ARIMA model to predict schistosomiasis cases in China / 中华地方病学杂志

Objective:An autoregressive integrated moving average (ARIMA) model was used to predict the number of monthly reported cases of schistosomiasis in China (excluding Hong Kong, Macao and Taiwan), so as to provide a scientific basis for prevention and control of schistosomiasis.Methods:Using ARIMA model, taking the time series of monthly reported cases of schistosomiasis in China from January 2009 to December 2018 as the training set, after stabilizing analysis with R 3.6.2 software, ARIMA models were selected by using screening parameters such as akaike information criterion and bayesian information criterion. Taking the number of monthly reported cases of schistosomiasis in China from January to December 2019 as the test set for verification and monthly optimization, an optimal ARIMA model was obtained. The prediction effect of the optimal ARIMA model was verified by the number of monthly reported cases of schistosomiasis in China from January 2019 to October 2020.Results:Based on the data of monthly reported cases of schistosomiasis in China from January 2009 to December 2018, four ARIMA models were obtained, namely ARIMA(2,0,2)(1,0,1)[12], ARIMA(2,0,2)(0,0,1)[12], ARIMA(2,0,2)(1,0,0)[12] and ARIMA(2,0,2). By comparing the actual number of cases from January to December 2019 with the predicted values of the four ARIMA models, the optimal prediction model of monthly reported cases of schistosomiasis was ARIMA(2,0,2)(1,0,1)[12], and the mean relative error of the prediction was 0.51%.Conclusions:The ARIMA model constructed in this study has high accuracy and is suitable for short-term prediction and analysis of the number of schistosomiasis cases in China. It can provide data support for prevention and control of the disease, and has certain practical guiding significance.

Prediction of Prenatal Ultrasound Screening Staff's Situation in Beijing Based on ARIMA Model / 中国医学科学院学报

Objective To understand the current situation and predict the trends in number and composition of prenatal ultrasound screening staff in Beijing. Methods We analyzed the region,age,professional title and other characteristics of prenatal ultrasound screening personnel in Beijing during 2007-2015.We then built an ARIMA model basing on the current situation to predict the number and composition of the staff in 2016-2020. Results The number of prenatal ultrasound screening staff showed an upward trend in 2007-2020 and was predicted to reach 1269 in 2020.During this period,the educational achievement and professional title of the staff showed a downward trend,and the working years became shorter,mainly below 5 years.The proportion of resident doctors remained at 26.6%,and that of the staff receiving further education would reach 43.2% by the end of 2020. Conclusion The prediction under ARIMA model suggests that efforts should be made to strengthen the training of young doctors and provide them opportunities for further study.

ARIMA model in blood supply prediction of multi blood components / 中国输血杂志

【Objective】 To establish an ARIMA model to fit the distributed units of four blood components from 2010 to 2018 in Tianjin and test the fitting degree, so as to predict the future issuing units of these blood products, and provide scientific basis for the blood center to formulate blood collection and donor recruitment plan. 【Methods】 The monthly distributed data of blood components from 2010 to 2019 were sorted out to establish the ARIMA model. The model identification, parameter estimation and test of the distributed data concerning red cells, plasma, apheresis platelet and white cells from January 2010 to December 2019 were performed to determine the optimal model using Eviews 10.0 software. Considering the obvious trend and seasonality of data, the seasonal model was chosen to predict the issuing of four blood products in January to December 2019, and the fitting degree was tested by comparing with the actual value. 【Results】 The ARIMA model residual autocorrelation function and partial autocorrelation function of four blood components showed that the regression residuals of each product had the same variance. The predicted value of supply was basically within 95% CI, and the curve trend of model fitting value and actual value was basically consistent, The average relative errors of red cells, plasma, apheresis platelets and white cells were 6.19%, 5.08%, 1.72% and 7.17%, respectively. 【Conclusion】 ARIMA model can appropriately fit the change trend of blood supply in Tianjin, which is helpful to understand the clinical requirements in the near future, provide the basis for blood collection, recruitment and inventory management.

Prediction on clinical platelet demand in Suzhou based on ARIMA model / 中国输血杂志

【Objective】 To establish an ARIMA model suitable for clinical platelet demand prediction in Suzhou, which can be used as reference to predict future clinical platelet demand and provide scientific basis for platelet collection, preparation, stock management and clinical deployment for blood banks, so as to achieve the maximum balance between platelets supply and demand . 【Methods】 The data of platelet consumption in Suzhou from 2009 to 2019 were collected and analyzed by SPSS 26 software, Time series analysis method was used to establish the ARIMA model. The model was further optimized through model identification, parameter estimation and optimal model test, and then used to predict clinical platelet consumption from January to November 2020. The predicted value was compared with the actual value to verify the prediction effect of the model. 【Results】 The optimal model for the prediction of platelet clinical demand was ARIMA (0, 1, 1) (0, 1, 1) 12. The ACF autocorrelation function value and PACF partial autocorrelation function value of the residuals were within 95% CI. Meanwhile, the LJUNG BOX test was 13.982 (P>0.05), indicating that there was no autocorrelation in the residuals. The trend of the curve between the predicted and actual value was basically the same(except for February 2020), and the predicted values were within 95% CI, with the average relative error of 7.22%, which was lower than 10%, showing good prediction effect. 【Conclusion】 ARIMA model can be used for short-term prediction of clinical platelets demand in Suzhou, and can provide basis for reasonable collection, preparation and deployment of platelets.

Study on prediction of clinical demand for plasma components in Suzhou city based on ARIMA model / 中国输血杂志

【Objective】 To establish a prediction model of clinical blood demand in Suzhou urban area by ARIMA model, and to predict future clinical blood demand by sorting out the historical data, so as to guide the reasonable collection and scientific deployment of blood resources, and achieve the balance of clinical blood supply and demand. 【Methods】 The monthly data of clinical use of plasma components in Suzhou city from 2009 to 2019 were obtained, and analyzed by SPSS26 software and ARIMA model. Through model identification, parameter estimation and optimal model test, the optimal model for clinical blood prediction was determined and used to predict the clinical consumption of plasma components from January to November 2020. The predicted value was compared with the actual value to verify the prediction effect of the model. 【Results】 The optimal model was ARIMA(0, 1, 1)(0, 1, 1)12. The values of ACF autocorrelation function and PACF partial autocorrelation function of residual were both within 95%CI. Meanwhile, the Yang-Box Q statistic value was 11.596, P>0.05, which passed the white noise test. The predicted values of clinical consumption of plasma components in Suzhou urban area from January to November 2020 were all within 95%CI, consistent with the trend of actual values, with small mean relative error(7.9%) and good prediction effect. 【Conclusion】 ARIMA model can be used for short-term prediction on clinical use of plasma components in Suzhou city, and provide reference for reasonable collection, preparation and scientific deployment.

Estimación de casos de COVID-19 en países de Suramérica empleando modelos ARIMA (Autorregresivo Integrado de Promedio Móvil) / Estimation of COVID-19 cases in South American countries using ARIMA models

El objetivo principal de este trabajo es emplear modelos ARIMA para la estimación de nuevos contagios usando datos públicos disponibles para Venezuela y la región suramericana, actualmente foco principal de un segundo brote de la COVID-19. Se realiza la predicción a 30 días del número de casos de Covid-19 en países suramericanos usando los datos públicos disponibles. Se emplearon modelos ARIMA para estimar el impacto de nuevos contagios en las dinámicas de infección para Suramérica. Desde la aparición del primer caso de la nueva neumonía Covid-19 en China, esta enfermedad se ha convertido en un problema de salud pública global y representa un gran reto el control de la infección para los países de Suramérica. Al 24 de junio de 2020 un total de 1.866.090 casos han sido detectados en la región y en el caso particular de Venezuela un total de 4.365 casos. El rápido incremento en el número de casos y la alta tasa de contagios asociado con el virus han llevado al desarrollo de distintas aproximaciones matemáticas, tales como: modelos SIR, SEIR, redes neuronales y regresiones lineales que permitan predecir la probable evolución de la epidemia. Los modelos ARIMA han sido empleados con éxito en otras infecciones como influenza, malaria, SARS, entre otras. Los resultados de las estimaciones realizadas empleando estos modelos muestran que aún en la región hacen falta mayores esfuerzos que conlleven al control de la epidemia(AU)

The main objective of this work is to use ARIMA models for the estimation of new contagions using public data available for Venezuela and the South American region, currently the main focus of a second COVID19 outbreak. A 30-day prediction is made for the num-ber of Covid-19 cases in South American countries using available public data. ARIMA models were used to estimate the impact of new contagions on infection dynamics for South America Since the appearance of the first case of the new Covid-19 pneumonia in China, which has become a global public health problem and the great challenge that the infection has represented for the countries of South America to June 24, 2020, a total of 1,866,090 cases have been detected and in the particular case of Venezuela a total of 4,365 cases have been detected for the same date. The rapid increase in the number of cases and the high rate of contagion associated with the virus have led to the development of different mathematical approaches, such as: SIR, SEIR models, neural networks and linear regressions that allow predicting the probable evolution of the epidemic. The ARIMA model has been successfully used in other infections such as influenza, malaria, SARS, among others. In the following work, the 30 - day prediction of the number of Covid-19 cases in South American countries is made using public data available. The results of the estimates made using these models show that even in the region, greater efforts are needed to control the epidemic(AU)

Predicting the number of visceral leishmaniasis cases in Kashgar, Xinjiang, China using the ARIMA-EGARCH model

Objective: To forecast the visceral leishmaniasis cases using autoregress integrated moving average (ARIMA) and hybrid ARIMA-EGARCH model, which offers a scientific basis to control visceral leishmaniasis spread in Kashgar Prefecture of Xinjiang, China. Methods: The data used in this paper are monthly visceral leishmaniasis cases in the Kashgar Prefecture of Xinjiang from 2004 to 2016. The sample data between 2004 and 2015 were used for the estimation to choose the best model and the sample data in 2016 were used for the forecast. Time series of visceral leishmaniasis started on 1 January 2004 and ended on 31 December 2016, consisting of 1 790 observations reported in Kashgar Prefecture. Results: For Xinjiang, the total number of reported cases were 2 187, the male-to-female ratio of cases was 1:1.42. Patients aged between 0 and 10 years accounted for 82.72% of all reported cases and the largest percentage of visceral leishmaniasis cases was detected among scattered children who accounted for 68.82%. The monthly incidences fitted by ARIMA (2, 1, 2) (1, 1, 1)

Comparative study of ARIMA model and seasonal index model in the prediction of mumps in Hubei Province / 公共卫生与预防医学

Objective To establish an ARIMA model and a seasonal index model to predict the trend of mumps, compare the advantages and disadvantages of the two models, and to provide a scientific basis for the prevention and control of mumps. Methods ARIMA model and seasonal index model were established based on the monthly incidence of mumps in Hubei Province from 2008 to 2019. Results The average annual incidence rate from 2008 to 2019 was 28.89 / 100,000. April-July was the month of high incidence. The established ARIMA model and seasonal index model were （1-1.070B+0.441B2-0.291B3）*（1-B12）*Xt=（1-0.611B12）*Ɛt and Xt=（2.802-0.006t）*St. The average relative errors of the ARIMA model and the seasonal index model were 11.49% and 20.86%, respectively. Conclusion The ARIMA model and the seasonal index model both have good applicability in predicting the onset time characteristics and trend of mumps. However, while the ARIMA model demonstrated more advantages in fitting the annual change trend, the seasonal index model is better in fitting the monthly change trend. The two models can be used in combination to predict the trend of mumps.

Application of ARIMA model in the prediction of syphilis incidence in Anhui Province / 公共卫生与预防医学

Objective To explore the application of time series autoregressive integrated moving average (ARIMA) based on seasonal difference to predict the number of syphilis cases in Anhui Province, and to provide a reference for early warning and control of syphilis. Methods Using R 3.6.2 software, the number of syphilis cases in Anhui Province from January 2004 to December 2016 was used for model fitting, and the resulting model was used to predict the incidence from January to December 2017. The difference between the predicted value and actual observed value was compared to evaluate the prediction effect of this model fitting. Results The incidence of syphilis in Anhui Province was on the rise with obvious periodicity. ARIMA(1,1,1)(0,1,2)12 was the optimal model, with the AIC being -264.81 and the BIC being -249.99. Box-Pierce test showed that λ2 value was 1.444(P=0.963), 10.459(P=0.576), and the difference was not statistically significant (P>0.05), indicating that the residual sequence was white noise. The model accuracy effect evaluation showed that the MAE was 0.06, the RMSE was 0.09, and the MAPE was 1.00%, indicating that the model fitting effect was good. The 2017 data was used to test the effect of the model extrapolation, and the results showed MAPE=6.09%, indicating that the model extrapolation effect was good. The actual value fell within 95% confidence interval of the predicted value, and the model prediction effect was relatively ideal. Conclusion The ARIMA(1,1,1)(0,1,2)12 model could better fit and predict the number of syphilis cases in Anhui Province, which may provide a theoretical basis for early warning, prevention and control of syphilis.

Predicting the number of visceral leishmaniasis cases in Kashgar, Xinjiang, China using the ARIMA-EGARCH model

Objective: To forecast the visceral leishmaniasis cases using autoregress integrated moving average (ARIMA) and hybrid ARIMA-EGARCH model, which offers a scientific basis to control visceral leishmaniasis spread in Kashgar Prefecture of Xinjiang, China. Methods: The data used in this paper are monthly visceral leishmaniasis cases in the Kashgar Prefecture of Xinjiang from 2004 to 2016. The sample data between 2004 and 2015 were used for the estimation to choose the best model and the sample data in 2016 were used for the forecast. Time series of visceral leishmaniasis started on 1 January 2004 and ended on 31 December 2016, consisting of 1 790 observations reported in Kashgar Prefecture. Results: For Xinjiang, the total number of reported cases were 2 187, the male-to-female ratio of cases was 1:1.42. Patients aged between 0 and 10 years accounted for 82.72% of all reported cases and the largest percentage of visceral leishmaniasis cases was detected among scattered children who accounted for 68.82%. The monthly incidences fitted by ARIMA (2, 1, 2) (1, 1, 1)12 model were consistent with the real data collected from 2004 to 2015. However, the predicted cases failed to comply with the observed case number; we then attempted to establish a hybrid ARIMA-EGARCH model to fit visceral leishmaniasis. Finally, the ARIMA (2, 1, 2) (1, 1, 1)12- EGARCH (1, 1) model showed a good estimation when dealing with volatility clustering in the data series. Conclusions: The combined model has been determined as the best prediction model with the root-mean-square error (RMSE) of 7.23% in the validation phase, which means that this model has high validity and rationality and can be used for short-term prediction of visceral leishmaniasis and could be applied to the prevention and control of the disease.

Malaria in India: A Predictive Study

@#Introduction: Malaria is devastating infectious disease not only India but also throughout the globe due to its high morbidity and mortality factor for last few centuries. From 19th and early 20th centuries, almost a quarter of the Indian populations were severely suffering from malaria. The economic loss due to increased mortality in malaria was estimated 10 million rupees per year in 1935. According to the World Malaria Report of 2017, malaria incidence accounted for 58% of cases in India. The objective of this study is to prediction of “annual” malaria incidences in India, depending on the basis of last 22 years national malaria epidemiology data. Methods: This study uses data from the official website of the National Program for the Control of Vector borne Diseases (NVBDCP) (http://nvbdcp. gov.in/) from 1995 to 2016. For creating a forecasting tool on Malaria surveillance in India, Econometric forecasting model (ARIMA Model ((0,1,1) (1,0,0) 12)) was used. Results: ARIMA statistical model ((0,1,1) (1,0,0) 12) found to be highly effective and significant (P < 0.05) in prediction of future epidemiological surveillance of malaria in India. ARIMA statistical model could be successfully use in prediction of annual malaria incidences in India after adjusting different highly contributing environmental and geographical factors, such as climate change, temperature, rainfall, and relative humidity. Conclusion: The historical forecast of the occurrence of malaria in India will allow the government to improve planning, control and prevention through public health interventions. In addition, the pharmaceutical industry will assist medical members in pre-treatment and drug interventions to respond to the increased or decreased occurrence of malaria.

Application of the exponential smoothing model and ARIMA model in prediction of the endemic situation of schistosomiasis in Hunan Province / 中国血吸虫病防治杂志

Objective To predict the changes in the prevalence of Schistosoma japonicum infections in humans and livestock in Hunan Province using the exponential smoothing model and the ARIMA model. Methods The data pertaining to S. japonicum infections in humans and livestock in Hunan Province from 1957 to 2015 were collected, and the exponential smoothing model and the ARIMA model were created using the software Eviews and PASW Statistics 18.0. In addition, the effectiveness of these two models for the prediction of S. japonicum infections in humans and livestock in Hunan Province from 2016 to 2018 was evaluated. Results The exponential smoothing model and the ARIMA model had a high goodness of fit for prediction of S. japonicum infections in humans and livestock in Hunan Province from 1957 to 2015. There was a linear trend in the prevalence of S. japonicum infections in humans and livestock in Hunan Province from 1957 to 2015. The prevalence of S. japonicum infections in humans predicted with the Brown’s linear trend and the prevalence of S. japonicum infections in livestock predicted with the Holt’s linear trend in Hunan Province from 2016 to 2018 fitted better the actual data than the ARIMA model; however, prediction of the ARIMA model indicated that the endemic situation of schistosomiasis remained at a low level in Hunan Province. Conclusion At a low epidemic level, development of highly sensitive tools for monitoring schistosomiasis is urgently needed in Hunan Province to fit the current endemic situation, and the schistosomiasis control measures should be intensified to consolidate the control achievements.

Prediction of brucellosis epidemic trend based on ARIMA model / 中华疾病控制杂志

Objective The aims is to predict the monthly incidence of brucellosis in China, in order to understand the epidemic trend of brucellosis in China, to formulate prevention and control strategies, and to provide data support and decision-making basis. Methods The national population and health science data sharing platform was used to collect the national incidence of brucellosis from January 2004 to December 2016 by month. The data were fitted and predicted using ARIMA model with R software. Results In this study, the parameters of the product season ARIMA (2,1,2) (2,1,1)12 model had statistical significance (all P<0.001). The model fitted well the monthly incidence of brucellosis in China. The average relative error between the predicted value and the actual value was 21.77%. The monthly average incidence of brucellosis in 2017, 2018, 2019 and 2020 were predicted to be 0.399 5/100 000, 0.423 8/100 000, 0.445 6/100 000 and 0.471 2/100 000 respectively, showing a gradually increasing trend ( 2=14.244, P<0.001), with a peak incidence from April to July. Conclusion Under natural conditions, the monthly incidence of human brucellosis in China will increase year by year, and corresponding measures should be taken to control it.

Analysis of epidemiological characteristics of Japanese encephalitis in Sichuan Province from 2008 to 2018 and application of autoregressive integrated moving average model / 中华疾病控制杂志

Objective The aim is to describe the epidemiological characteristics of Japanese encephalitis(JE) in Sichuan Province from 2008 to 2018, to build time series autoregressive integrated moving average(ARIMA) model, and to discuss the model application in the prediction of JE incidence trends. Methods Descriptive epidemiological analysis was used to analyze the epidemic situation of JE in Sichuan Province from 2008 to 2018. Monthly surveillance data of JE in Sichuan Province from January 2008 to December 2017 were used to fit ARIMA model. The number of reported cases from January to December in 2018 was used to test the model. Results The epidemic situation of JE in Sichuan Province from 2008 to 2018 showed a downward trend, and eastern and southern areas were the highly prevalent areas. The incidence peak was from July to September every year. Children were the high-risk group, but the incidence of adolescent and adult was on the rise in recent years. ARIMA(1,0,0)(2,1,0)12 could appropriately fit the time series. Conclusion ARIMA model can be used for short-term prediction of the reported incidence of JE in Sichuan Province.