Prediction of brucellosis incidence in Urumqi / 公共卫生与预防医学

Objective To explore the application of seasonal autoregressive integrated moving average (ARIMA) model in the prediction of brucellosis in Urumqi, and to use this model to predict the incidence trend of brucellosis in Urumqi. Methods The monthly incidence data of brucellosis in Urumqi from January 2010 to December 2021 were selected to construct the ARIMA prediction model. The prediction effect of the model was evaluated by mean standard deviation (RMSE) and mean absolute error (MAE). The monthly incidence of brucellosis in Urumqi in 2022 was predicted by the constructed model. Results The incidence of brucellosis in Urumqi had obvious seasonal distribution, and the cases were concentrated from May to July. ARIMA(1,1,1)(1,0,1)12 was the optimal prediction model, with RMSE=0.883 and MAE=5.24. The monthly incidence of brucellosis in Urumqi in 2022 was predicted to be 7, 4, 4, 6, 9, 9, 10, 7, 7, 5, 5, and 5 cases, respectively. Conclusion ARIMA model can well fit and predict the monthly incidence of brucellosis in Urumqi and provide a basis for the monitoring and prevention of brucellosis.

Application and efficacy of patient-based real-time quality control in intralaboratory comparison of various hematology analyzers / 中华检验医学杂志

Objective:To evaluate the application value of patient-based real-time quality control (PBRTQC) algorithms in intralaboratory comparison between various hematology analyzers.Method:From April 1 st 2020 to March 31 th 2021, data of white blood cell (WBC) counts and daily comparison results of fresh venous blood, measured by five hematology analyzers, were collected at the Department of Laboratory Medicine in Hebei Children′s Hospital. First, the professional intelligent PBRTQC software system was applied to conduct the parameter setting, program establishment, and performance verification. Three concentration ranges of WBC were selected, low concentration (2.5-4.5)×10 9/L, medium concentration (6.0-8.0)×10 9/L and high concentration (12.0-14.0)×10 9/L for the comparison. Next, WBC counts were calculated with both of the EWMA and median methods, the results were then analyzed by PBRTQC using the module of"intralaboratory comparison of hematology analyzers". Finally, bias of intralaboratory comparison among various hematology analyzers analyzed by means of EWMA and daily comparison results of fresh venous blood were compared. Based on the standard of WS/T 406-2012,allowable error ±7.50% in WBC counts was set as the relative bias standard among different instruments. Results:(1) A total of 38 313 sample results were included, there were 70 warning results out of these samples based on the EWMA quality control method established on the data of patients with white blood cell count in our laboratory, with an early warning rate of 0.183‰, a probability of error detection of 100%, and a probability of false loss of control of 0. EWMA quality control efficiency met the quality objectives. (2) In the comparison monitoring of the results of 5 blood cell analyzers at high concentrations, the coincidence rate between EWMA and median method were both 100% (46/46) in weekly and monthly comparison, and EWMA could maintain a relatively stable monitoring efficiency in daily comparison. (3) In the selected natural month, the consistency rate between EWMA method and fresh blood comparison method was 95.24% (20/21).Conclusion:PBRTQC can be used as a valuable supplementary tool of IQC to continuously and effectively monitor the consistency of data derived from intralaboratory hematology analyzers with different bands and types, which can not only reduce the risk of quality and operating costs, but also improve the efficiency of laboratory management.

An Atrial Fibrillation Classification Method Study Based on BP Neural Network and SVM / 中国医疗器械杂志

Atrial fibrillation is a common arrhythmia, and its diagnosis is interfered by many factors. In order to achieve applicability in diagnosis and improve the level of automatic analysis of atrial fibrillation to the level of experts, the automatic detection of atrial fibrillation is very important. This study proposes an automatic detection algorithm for atrial fibrillation based on BP neural network (back propagation network) and support vector machine (SVM). The electrocardiogram (ECG) segments in the MIT-BIH atrial fibrillation database are divided into 10, 32, 64, and 128 heartbeats, respectively, and the Lorentz value, Shannon entropy, K-S test value and exponential moving average value are calculated. These four characteristic parameters are used as the input of SVM and BP neural network for classification and testing, and the label given by experts in the MIT-BIH atrial fibrillation database is used as the reference output. Among them, the use of atrial fibrillation in the MIT-BIH database, the first 18 cases of data are used as the training set, and the last 7 cases of data are used as the test set. The results show that the accuracy rate of 92% is obtained in the classification of 10 heartbeats, and the accuracy rate of 98% is obtained in the latter three categories. The sensitivity and specificity are both above 97.7%, which has certain applicability. Further validation and improvement in clinical ECG data will be done in next study.

Epidemiological characteristics and trend prediction of pulmonary tuberculosis in Ankang City, Shaanxi Province from 2011 to 2021 / 中国热带医学

@#Abstract: Objective To analyze the epidemiological characteristics of pulmonary tuberculosis (PTB) in Ankang City from 2011 to 2021, so as to provide a scientific basis for the formulation of PTB prevention and control strategy. Methods Descriptive statistics were used to analyze the epidemiological characteristics of PTB in Ankang City from 2011 to 2021, and a time series model was established to quantitatively predict the incidence of pulmonary tuberculosis in 2023. Results The incidence rate in Ankang City showed a significant upward trend from 2011 to 2017, and a more obvious downward trend in 2017-2021 (P<0.05), and the decrease rate in 2021 was 40.36% compared with that in 2017. The proportion of etiological positivity increased from 12.5% in 2014 to over 50.00% after 2019. The incidence season was mainly concentrated in the first quarter, accounting for 28.39% of the annual incidence. High incidence areas were concentrated in the south of Ankang: Langao County, Ziyang County and Zhenping County, with 128.32/100 000, 117.07/100 000 and 110.44/100 000, respectively. Low incidence areas were located in the north of Ankang: Ningshan County, with 60.62/100 000. Farmers and students were the high incidence groups, accounting for 81.80% and 4.97% of the total cases respectively. The incidence of young children was relatively low, but cases were reported every year. The incidence rate of male was 2.39 times that of female. The age of onset increased significantly from 15 years old, and the peak incidence was in the age group of 60-<80 years old, followed by the age group of 45-<60 years old, the average annual incidence was 136.44/100 000 and 104.47/100 000, respectively. The model ARIMA(0,1,1)(0,1,1)12 predicted that the incidence of the disease generally increased from October 2022 to March 2023, then steadily decreased, and increased again in December. Conclusions The incidence of tuberculosis varies in different areas of Ankang City, and males, farmers, students and the elderly are all factors of high incidence of tuberculosis. Therefore, different prevention and control strategies should be adopted according to the characteristics of population in different areas. The number of cases in Ankang City in 2023 showed an overall downward trend, which can provide a reference for the prevention and control of PTB.

Time series analysis and prediction model of percentage of influenza-like illness （ILI） cases in Shanghai / 上海预防医学

ObjectiveTo predict the incidence trend of influenza-like illness proportion （ILI%） in Shanghai using the seasonal autoregressive integrated moving average model （SARIMA）， and to provide an important reference for timely prevention and control measures. MethodsTime series analysis was performed on ILI% surveillance data of Shanghai Municipal Center for Disease Control and Prevention from the 15th week of 2015 to the 52nd week of 2019， and a prediction model was established. Seasonal autoregressive integrated moving average （SARIMA） model was established using data from the foregoing 212 weeks， and prediction effect of the model was evaluated using data from the latter 36 weeks. ResultsFrom the 15th week of 2015 to the 52nd week of 2019， the average ILI% in Shanghai was 1.494%， showing an obvious epidemic peak. SARIMA（1，0，0） （2，0，0） 52 was finally modeled. The residual of the model was white noise sequence， and the true values were all within the 95% confidence interval of the predicted values. ConclusionSARIMA（1，0，0） （2，0，0） 52 can be used for the medium term prediction of ILI% in Shanghai， and can play an early warning role for the epidemic and outbreak of influenza in Shanghai.

Comparison of the application effects of SARIMA, GAM and LSTM in prediction of hemorrhagic fever with renal syndrome / 中华地方病学杂志

Objective:To analyze the effects of seasonal autoregressive integrated moving average model (SARIMA), generalized additive model (GAM), and long-short term memory model (LSTM) in fitting and predicting the incidence of hemorrhagic fever with renal syndrome (HFRS), so as to provide references for optimizing the HFRS prediction model.Methods:The monthly incidence data of HFRS from 2004 to 2017 of the whole country and the top 9 provinces with the highest incidence of HFRS (Heilongjiang, Shaanxi, Jilin, Liaoning, Shandong, Hebei, Jiangxi, Zhejiang and Hunan) were collected in the Public Health Science Data Center (https://www.phsciencedata.cn/), of which the data from 2004 to 2016 were used as training data, and the data from January to December 2017 were used as test data. The SARIMA, GAM, and LSTM of HFRS incidence in the whole country and 9 provinces were fitted with the training data; the fitted model was used to predict the incidence of HFRS from January to December 2017, and compared with the test data. The mean absolute percentage error ( MAPE) was used to evaluate the model fitting and prediction accuracy. When MAPE < 20%, the model fitting or prediction effect was good, 20%-50% was acceptable, and > 50% was poor. Results:From the perspective of overall fitting and prediction effect, the optimal model for the whole country and Heilongjiang, Shaanxi, Jilin, Liaoning and Jiangxi was SARIMA ( MAPE was 19.68%, 20.48%, 44.25%, 19.59%, 23.82% and 35.29%, respectively), among which the fitting and prediction effects of the whole country and Jilin were good, and the rest were acceptable. The optimal model for Shandong and Zhejiang was GAM ( MAPE was 18.29% and 21.25%, respectively), the fitting and prediction effect of Shandong was good, and Zhejiang was acceptable. The optimal model for Hebei and Hunan was LSTM ( MAPE was 26.52% and 22.69%, respectively), and the fitting and prediction effects were acceptable. From the perspective of fitting effect, GAM had the highest fitting accuracy in the whole country data, with MAPE = 10.44%. From the perspective of prediction effect, LSTM had the highest prediction accuracy in the whole country data, with MAPE = 12.23%. Conclusions:SARIMA, GAM, and LSTM can all be used as the optimal models for fitting the incidence of HFRS, but the optimal models fitted in different regions show great differences. In the future, in the establishment of HFRS prediction models, as many alternative models as possible should be included for screening to ensure higher fitting and prediction accuracy.

The predictive value of time series forecasting model in prehospital emergency medical services demand in Guangzhou / 中华急诊医学杂志

Objective:To study the value of autoregressive integrated moving average (ARIMA) and autoregressive (AR) models in predicting the daily number of ambulances in prehospital emergency medical services demand in Guangzhou.Methods:Matlab simulation software was used to analyze the emergency dispatching departure records in Guangzhou from January 1, 2021 to December 31, 2021. A time series for the number of ambulances per day was calculated. After identifying the time series prediction model, ARIMA(1,1,1), AR(4) and AR(7) models were obtained. These models were used to predict the number of ambulances per day. ARIMA(1,1,1) model divided the time series into the training set and test set. Prony method was used for parameter calculation, and the demands of number of ambulances of the next few months were forecasted. AR(4) and AR(7) models used uniformity coefficient to forecast the demands of number of ambulances on that very day.Results:ARIMA(1,1,1), AR(4) and AR(7) can effectively predict the number of ambulances per day. The prediction fitting error of ARIMA (1,1,1) decreased with the extension of prediction time. The mean absolute percentage error (MAPE) of forecast results of daily vehicle output of emergency dispatching within two months was less than 6% and the predicted results were almost within the 95% confidence interval. The residual analysis of the model verified that the model was significantly effective.Conclusions:ARIMA model can make a long-term within two months and effective prediction fitting of the daily vehicle output of emergency dispatching, and AR model can make a short-term and effective prediction of the daily vehicle output of emergency dispatching.

Modelación matemática de los casos autóctonos de Covid-19. Municipio Bayamo. Febrero- Agosto 2021

El municipio Bayamo acumuló, 8162 casos positivos autóctonos de febrero a agosto en el año 2021, es el centro de la epidemia en la provincia de COVID-19 provocada por el SARS -CoV-2 determinado por el test de Proteína C Reactiva, representa el53,2 % del total de los casos en ese periodo en Granma, muy diferente a lo ocurrido en el año 2020 en el cual la provincia acumuló solamente 185personas contagiadas en nueve meses, con una tasa de 22.6 la más baja de Cuba. La provincia Granma acumuló 119 fallecidos en agosto/2021 que representa el 62,9 % de todos los muertos desde que comenzó la pandemia hasta agosto, lo que indica la alta incidencia de la epidemia que hay en estos momentos. Para la modelación matemática y el análisis de los casos positivos autóctonos de todos los ocurridos durante los meses de febrero a agosto en el año 2021 en Bayamo se obtuvieron polinomios de grado tres y cuatro que modelan el comportamiento de la epidemia durante los siete meses analizados, así como el de los fallecidos durante el mes de agosto en Granma con un carácter predictivo mayor al 98 % en todos los modelos.

The Bayamo municipality accumulated 8162 autochthonous positive cases from February to August in 2021, it is the center of the epidemic in the province of COVID-19 caused by SARS-CoV-2 determined by the C-Reactive Protein test, represents the 53.2% of the total cases in that period in Granma, very different from what happened in 2020 in which the province accumulated only 185 infected people in nine months, with a rate of 22.6, the lowest in Cuba. Granma province accumulated 119 deaths in August / 2021, which represents 62.9% of all deaths since the pandemic began until August, which indicates the high incidence of the epidemic that exists at the moment. For the mathematical modeling and analysis of the autochthonous positive cases of all those that occurred during the months of February to August in 2021 in Bayamo, polynomials of degree three and four were obtained that model the behavior of the epidemic during the seven months analyzed. as well as that of the deceased during the month of August in Granma with a predictive character greater than 98% in all models.

O município de Bayamo acumulou 8.162 casos autóctones positivos de fevereiro a agosto de 2021, é o centro da epidemia na província de COVID-19 causada pelo SARS-CoV-2 determinado pelo teste da Proteína C Reativa, representa 53,2% de o total de casos nesse período no Granma, muito diferente do que aconteceu em 2020 em que a província acumulou apenas 185 pessoas infectadas em nove meses, com uma taxa de 22,6, a mais baixa de Cuba. A província do Granma acumulou 119 mortes em agosto / 2021, o que representa 62,9% de todas as mortes desde o início da pandemia até agosto, o que indica a alta incidência da epidemia que existe no momento. Para a modelagem matemática e análise dos casos positivos autóctones de todos os ocorridos durante os meses de fevereiro a agosto de 2021 em Bayamo, foram obtidos polinômios de grau três e quatro que modelam o comportamento da epidemia durante os sete meses analisados. bem como o dos falecidos durante o mês de agosto no Granma com caráter preditivo superior a 98% em todos os modelos.

Current Status and Prospects of Non-Invasive Central Arterial Pressure Measurement / 医用生物力学

Cardiovascular disease is one of the important factors that threaten the health of residents, ranking the first among various causes of death, so the monitoring and diagnosis of human cardiovascular health is particularly important. Compared with traditional brachial artery pressure, central arterial pressure (CAP) has a higher correlation with the occurrence of many cardiovascular events. The measurement of CAP can more accurately reflect the real situation of human blood pressure, and provide an important basis for diagnosis and disease prevention. Therefore, the realization of high-precision, high-generalization ability and low-cost non-invasive measurement of CAP has always been the research focus in this field. This article combines the relevant literature in China and abroad to summarize the current status of CPA measurement, introduces related research progress from two aspects, namely parameter measurement and waveform measurement, and discusses the characteristics of the existing methods and the future development.

Application of an autoregressive integrated moving average model in prediction of cardiovascular disease mortality / 上海预防医学

Objective:To use autoregressive integrated moving average （ARIMA） model for predicting the mortality of cardiovascular diseases in residents in Yushui District， Jiangxi Province， and to provide basis for developing the prevention and control strategies as well as to promote the continuous optimization of chronic disease prevention and treatment demonstration area. Methods:Based on the cardiovascular death monitoring data of residents in Yushui District， Jiangxi Province from 2014 to 2018， Econometrics View 9.0 software was used to construct the ARIMA seasonal adjustment model to predict the monthly cardiovascular death in this area. Results:The monthly death rate of cardiovascular diseases in Yushui showed a long-term rising trend， with an apparent seasonal pattern （a peak of cardiovascular death from December to January each year）. After the original sequence was subjected to first-order difference and first-order seasonal difference， the difference sequence showed good stationarity （P<0.05）. All the theoretical models were listed and their model parameters were calculated respectively. After statistical test （P<0.05）， 7 alternative models for seasonal adjustment of ARIMA were selected. Among them， ARIMA（1，1，1）（1，1，1）12 is the optimal model selected in this study （R2=0.749， Adjustment R2=0.724， AIC=8.454， SC=8.633， HQ=8.515）.And its residual sequence was tested by white noise test （P>0.05）， indicating that the prediction effect was good. Conclusion:ARIMA（1，1，1）（1，1，1） 12 model can accurately simulate the long-term trend and seasonal pattern of cardiovascular disease death in Yushui， and make a scientific prediction of the trend and monthly distribution of cardiovascular disease death in the next three years.

Influenza incidence prediction based on ARIMAX model including meteorological factors / 预防医学

Objective@#To evaluate the feasibility of autoregressive integrated moving average with explanatory variables ( ARIMAX ) model including meteorological factors on the prediction of influenza-like illness ( ILI ), so as to provide a basis for the monitoring and early warning of influenza.@*Methods@#The ILI data reported by four sentinel hospitals in Yuhang District of Hangzhou from the 1st week of 2014 to the 26th week of 2018 was collected, as well as the meteorological data during the same period. The ARIMAX model was established using the percentage of ILI cases in total outpatients ( ILI% ) data from the 1st week of 2014 to the 52nd week of 2017 and the meteorological factors selected by Lasso regression model. The ILI% from the 1st to 26th week of 2018 was predicted and compared with the actual values to verify the ARIMAX model.@*Results@#From the 1st week of 2014 to the 26th week of 2018, a total of 60 419 cases of ILI were reported by the four sentinel hospitals of Yuhang District, with ILI% of 1.29%. Lasso regression analysis showed that there was a positive correlation between weekly average absolute humidity and ILI% ( r=27.769 ), and a negative correlation between weekly average temperature and ILI% ( r=-0.117 ). The ARIMAX (1, 0, 0) ( 1, 0, 0 )12 with weekly average temperature and absolute humidity was selected as the optimal model, with the Bayesian information criterion (BIC) value of 81.30 and the mean absolute percentage error (MAPE) value of 15.77%. The MAPE value of the ARIMAX model predicting the ILI% from 1st to 26th week of 2018 were 43.75%.@*Conclusion@#The ARIMAX model including meteorological factors can be used to predict the prevalence of ILI, but the accuracy needs to be promoted.

Research on high-efficiency electrocardiogram automatic classification based on autoregressive moving average model fitting / 生物医学工程学杂志

The automatic detection of arrhythmia is of great significance for the early prevention and diagnosis of cardiovascular diseases. Traditional arrhythmia diagnosis is limited by expert knowledge and complex algorithms, and lacks multi-dimensional feature representation capabilities, which is not suitable for wearable electrocardiogram (ECG) monitoring equipment. This study proposed a feature extraction method based on autoregressive moving average (ARMA) model fitting. Different types of heartbeats were used as model inputs, and the characteristic of fast and smooth signal was used to select the appropriate order for the arrhythmia signal to perform coefficient fitting, and complete the ECG feature extraction. The feature vectors were input to the support vector machine (SVM) classifier and K-nearest neighbor classifier (KNN) for automatic ECG classification. MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database were used to verify in the experiment. The experimental results showed that the feature engineering composed of the fitting coefficients of the ARMA model combined with the SVM classifier obtained a recall rate of 98.2% and a precision rate of 98.4%, and the

The epidemic trend of viral hepatitis in Nanjing from 1989 to 2020 / 预防医学

Objective@#To analyze the epidemic trend of viral hepatitis in Nanjing from 1989 to 2019 and predict the incidence in 2020, so as to provide reference for the prevention and control of viral hepatitis.@*Methods@#The incidence data of viral hepatitis in Nanjing from 1989 to 2019 was retrieved from Nanjng Center for Disease Control and Prevention and National Infectious Disease Reporting System. The epidemic trend was analyzed by estimating the annual percent change ( APC ) and the average annual percent change ( AAPC ). The seasonal incidence of different types of viral hepatitis was analyzed by seasonal index. The autoregressive integrated moving average model ( ARIMA ) was built to predict monthly incidence rate of viral hepatitis in 2020. @*Results@#The annual incidence rate of viral hepatitis was 62.00/100 000 in Nanjing from 1989 to 2019, showing a downward trend ( AAPC=8.4%, P<0.05 ). From 1998 to 2019, the annual incidence rates of hepatitis A, B, C and E were 1.98/100 000, 14.31/100 000, 2.30/100 000 and 2.60/100 000. The incidence of hepatitis A and B showed downward trends ( AAPC=-11.81%, -6.02%, both P<0.05 ); the incidence trend of hepatitis C was not obvious ( P>0.05 ); the incidence of hepatitis E showed an increasing trend ( AAPC=4.82%, P<0.05 ). From 2015 to 2019, the third and fourth quarters were the epidemic seasons of hepatitis A, B and C, while the first and second quarters were the epidemic seasons of hepatitis E. The ARIMA model predicted that the monthly incidence rates of viral hepatitis in 2020 would range from 1.26/100 000 to 3.69/100 000, among which hepatitis B ranged from 1.21/100 000 to 2.58/100 000, hepatitis C from 0.20/100 000 to 0.48/100 000, hepatitis E from 0.09/100 000 to 0.25/100 000. @*Conclusions@#The incidence of viral hepatitis in Nanjing shows a downward trend. Among different types of hepatitis, hepatitis B has a higher incidence. All types of hepatitis have epidemic seasons. It is predicted that the monthly incidence rates of viral hepatitis will be 1.26/100 000 to 3.69/100 000 in 2020.

Predicting the trend of the COVID-19 outbreak and timely grading the current risk level of epidemic based on moving average prediction limits / 上海交通大学学报（医学版）

Objective : To establish a practical data-driven method that helps predict the evolutionary trend of the coronavirus disease 2019 (COVID-19) epidemic, track and prejudge the current risk classification of the epidemic area, and provide a quantitative evidence for precision prevention and control strategies. Methods ¡¤ A moving average prediction limit (MAPL) method was established based on the moving average method. The previous severe acute respiratory syndrome (SARS) epidemic data was used to verify the practicability of the MAPL method for predicting epidemic trends and quantitative risk. By tracking the COVID-19 outbreak epidemic data publicly reported since January 16, 2020, the MAPL method was used for timely epidemic trend prediction and the risk classification. Results ¡¤ According to the MAPL analysis, the na-tional epidemic of COVID-19 peaked in early February 2020. After active prevention and control in early stages, the overall epidemic situation in the country showed a downward trend from mid-February to mid-March. Compared with Hubei Province, the number of new cases in non-Hubei region declined rapidly in mid-February, but then increased slightly. The analysis of imported cases since March showed that there was a medium to high level of epidemic import risk in the near future. It is recommended to take corresponding prevention and control measures to prevent the epidemic from spreading again. Conclusion ¡¤ The MAPL method can assist in judging the epidemic trend of emerging infectious diseases and predicting the risk levels in a timely manner. Each epidemic district may implement a differentiated precision prevention and control strategies according to the local classification of epidemic risk. Since March, attention should be paid to the prevention and control of imported risks.

Mosquito density monitoring data by ARIMA model / 上海预防医学

Objective To forecast the trend of mosquito density index in Pudong New Area, Shanghai so as to provide evidence for disease control and risk-control measures for vector-borne diseases. Methods Mosquito monitoring data was collected in Pudong New Area between 2011 and 2015 at the city-level monitoring sites for analysis on the trend of the mosquito density index in Pudong New Area of Shanghai by using the Autoregressive Integrated Moving Average Model (ARIMA). Results From 2011 to 2015, a total of 135 times labor-hour monitoring were carried out at the city-level monitoring points in Pudong New Area.The mosquito density index averaged 6.17/labor-hour with a standard deviation at 4.93, S=[0, 18]/labor-hour.Using ARIMA to analyze the change trend of mosquito density index in Pudong New Area, ARIMA(2, 0, 1)became the final fitting model, with R2=0.808.In the model, the Ljung-Box Q test value was 19.632(AR1=1.866, AR2=-0.907), and MA parameter was 0.999. Conclusion ARIMA model can be used to predict mosquito density monitoring data, but low monitoring frequency and irregular cycle length will affect the prediction results.

Application of infectious disease index to prediction of infectious diseases / 预防医学

Objective@#To establish a prediction model for infectious disease index（IDI）by autoregressive integrated moving average（ARIMA）,and to provide forcast of infectious diseases to the public. @*Methods@#The data of the percentage of influenza-like illness（ILI）,the incidence rates of hand-foot-mouth disease（HFMD）and other infectious diarrhea（OID）from the 1st week of 2014 to the 14th week of 2018,and Breteau index（BI）from the 1st week of 2016 to the 14th week of 2018 were collected. ARIMA models were built to predict the risk indicators of ILI,HFMD,OID and BI. The weights of the four indicators were evaluated seasonally by the entropy weight method. Then the IDI was calculated and the data of ILI,HFMD, OID and BI from 15th to 19th week in 2018 was used for verification. @*Results@#The forecast was in summer,so IDI=ROUND（0.33×risk index of ILI percentage +0.47×risk index of HFMD incidence +0.10×risk index of OID incidence+0.10×risk index of BI）. The predicted IDI would be 2（less safe）in the whole city and Xiangzhou District,and 1（safe）in Doumen District and Jinwan District. The consistency rates of IDI prediction was 97.50%,95.00%,97.50%,85.00% and 77.50% from 15th to 19th week in 2018,respectively.@*Conclusion@#It was feasible to use IDI for short-term risk prediction of infectious diseases.

Microvolt T-wave alternans complemented with electrophysiologic study for prediction of ventricular tachyarrhythmias in patients with arrhythmogenic right ventricular cardiomyopathy: a long-term follow-up study / 中华医学杂志(英文版)

Background@#The long-term predicted value of microvolt T-wave alternans (MTWA) for ventricular tachyarrhythmia in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) remains unclear. Our study explored the characteristics of MTWA and its prognostic value when combined with an electrophysiologic study (EPS) in patients with ARVC.@*Methods@#All patients underwent non-invasive MTWA examination with modified moving average (MMA) analysis and an EPS. A positive event was defined as the first occurrence of sudden cardiac death, documented sustained ventricular tachycardia (VT), ventricular fibrillation, or the administration of appropriate implantable cardioverter defibrillator therapy including shock or antitachycardia pacing.@*Results@#Thirty-five patients with ARVC (age 38.6 ± 11.0 years; 28 males) with preserved left ventricular (LV) function were recruited. The maximal TWA value (MaxValt) was 17.0 (11.0–27.0) μV. Sustained VT was induced in 22 patients by the EPS. During a median follow-up of 99.9 ± 7.7 months, 15 patients had positive clinical events. When inducible VT was combined with the MaxValt, the area under the curve improved from 0.739 to 0.797. The receiver operating characteristic curve showed that a MaxValt of 23.5 μV was the optimal cutoff value to identify positive events. The multivariate Cox regression model for survival showed that MTWA (MaxValt, hazard ratio [HR], 1.06; 95% confidence interval [CI], 1.01–1.11; P = 0.01) and inducible VT (HR, 5.98; 95% CI, 1.33–26.8; P = 0.01) independently predicted positive events in patients with ARVC.@*Conclusions@#MTWA assessment with MMA analysis complemented by an EPS might provide improved prognostic ability in patients with ARVC with preserved LV function during long-term follow-up.

Application of ARIMA model in predicting the incidence of tuberculosis in China from 2018 to 2019 / 中华流行病学杂志

Objective@#Autoregressive integrated moving average (ARIMA) model was used to predict the incidence of tuberculosis in China from 2018 to 2019, providing references for the prevention and control of pulmonary tuberculosis.@*Methods@#The monthly incidence data of tuberculosis in China were collected from January 2005 to December 2017. R 3.4.4 software was used to establish the ARIMA model, based on the monthly incidence data of tuberculosis from January 2005 to June 2017. Both predicted and actual data from July to December 2017 were compared to verify the effectiveness of this model, and the number of tuberculosis cases in 2018-2019 also predicted.@*Results@#From 2005 to 2017, a total of 13 022 675 cases of tuberculosis were reported, the number of pulmonary tuberculosis patients in 2017 was 33.68% lower than that in 2005, and the seasonal character was obvious, with the incidence in winter and spring was higher than that in other seasons. According to the incidence data from 2005 to 2017, we established the model of ARIMA (0,1,2)(0,1,0)12. The relative error between the predicted and actual values of July to December 2017 fitted by the model ranged from 1.67% to 6.80%, and the predicted number of patients in 2018 and 2019 were 789 509 and 760 165 respectively.@*Conclusion@#The ARIMA (0, 1, 2)(0, 1, 0)12 model well predicted the incidence of tuberculosis, thus can be used for short-term prediction and dynamic analysis of tuberculosis in China, with good application value.

A time-series prediction and analysis on rural inpatient with cardio-cerebrovascular disease in Wugang / 中华疾病控制杂志

Objective To establish a predictive model for inpatients of cardio-cerebrovascular disease in rural areas of Wugang through time series analysis, and predict the changing trend of cardio-cerebrovascular disease, so as to offer guidance for the health care resources allocation and prevention and control of cardio-cerebrovascular disease. Methods The seasonal autoregressive integrated moving average model (SARIMA) was constructed based on the monthly number of cases of cardio-cerebrovascular disease in rural areas from January 2013 to December 2016 by Stata 14.0 software, and the predictive effect of the model was verified with the monthly number of inpatients of cardio-cerebrovascular disease in 2017. Results The final fitting model of inpatients of cardio-cerebrovascular disease was SARIMA (2, 1, 1)×(0, 1, 0)12. The residual sequence of the model was diagnosed. Results of Ljung-Box Q test showed that the residual sequence was white noise sequence (Q=11.12, P=0.68). In addition, the 2017 forecast was basically consistent with the observations, the overall relative error was around -1.2%. The results showed that the summer was the peak period of cardiovascular and cerebrovascular hospitalization. Conclusion SARIMA model can accurately predict the number of inpatients of cardio-cerebrovascular disease in Wugang, which can provide data support for the hospital administrator to rationally allocate medical resources in the cardiovascular according to the needs of cardio-cerebrovascular treatment in different months.

Application and comparison of three models for the prediction of influenza-like illness in Jiangxi Province / 中华疾病控制杂志

@# Objective To establish the optimal epidemical trend prediction model of influenza in Jiangxi Province and provide scientific guidance for influenza prevention and control. Methods Monthly influenza sentinel surveillance data of Jiangxi Province were derived from the “Influenza Surveillance Information System In China” from 2013 to 2017, and the different forecasting methods were used to build model, such as autoregressive(AR),exponential smoothing(ES) and autoregressive integrated moving average(ARIMA), also compared predictions with actual values in 2017. Results R square of the three models were 0.731, 0.751 and 0.815 respectively; the root mean square error(MRSE) were 0.253, 0.243 and 0.212, respectively; mean absolute error(MAE)were 0.189, 0.178 and 0.151, respectively; mean absolute percentage error(MAPE) were 10.092, 9.523 and 8.124 respectively; the average relative error (MRE) were 11.45%, 10.92% and 8.96%, respectively. Conclusions ARIMA was a good model for predicting the percentage of influenza-like illness in outpatient visits in Jiangxi Province.