Impact of the COVID-19 outbreak and interventions on hand, foot and mouth disease in Zhengzhou, China, 2014-2022: a retrospective study.

BACKGROUND: Since December 2019, COVID-19 has spread rapidly around the world, and studies have shown that measures to prevent COVID-19 can largely reduce the spread of other infectious diseases. This study explored the impact of the COVID-19 outbreak and interventions on the incidence of HFMD. METHODS: We gathered data on the prevalence of HFMD from the Children's Hospital Affiliated to Zhengzhou University. An autoregressive integrated moving average model was constructed using HFMD incidence data from 2014 to 2019, the number of cases predicted from 2020 to 2022 was predicted, and the predicted values were compared with the actual measurements. RESULTS: From January 2014 to October 2022, the Children's Hospital of Zhengzhou University admitted 103,995 children with HFMD. The average number of cases of HFMD from 2020 to 2022 was 4,946, a significant decrease from 14,859 cases from 2014 to 2019. We confirmed the best ARIMA (2,0,0) (1,1,0)12 model. From 2020 to 2022, the yearly number of cases decreased by 46.58%, 75.54%, and 66.16%, respectively, compared with the forecasted incidence. Trends in incidence across sexes and ages displayed patterns similar to those overall. CONCLUSIONS: The COVID-19 outbreak and interventions reduced the incidence of HFMD compared to that before the outbreak. Strengthening public health interventions remains a priority in the prevention of HFMD.

Research process, recap, and prediction of Chiari malformation based on bicentennial history of nomenclature and terms misuse.

There is an absent systematic analysis or review that has been conducted to clarify the topic of nomenclature history and terms misuse about Chiari malformations (CMs). We reviewed all reports on terms coined for CMs for rational use and provided their etymology and future development. All literature on the nomenclature of CMs was retrieved and extracted into core terms. Subsequently, keyword analysis, preceding and predicting (2023-2025) compound annual growth rate (CAGR) of each core term, was calculated using a mathematical formula and autoregressive integrated moving average model in Python. Totally 64,527 CM term usage was identified. Of these, 57 original terms were collected and then extracted into 24 core-terms. Seventeen terms have their own featured author keywords, while seven terms are homologous. The preceding CAGR of 24 terms showed significant growth in use for 18 terms, while 13, three, three, and five terms may show sustained growth, remain stable, decline, and rare in usage, respectively, in the future. Previously, owing to intricate nomenclature, Chiari terms were frequently misused, and numerous seemingly novel but worthless even improper terms have emerged. For a very basic neuropathological phenomenon tonsillar herniation by multiple etiology, a mechanism-based nosology seems to be more conducive to future communication than an umbrella eponym. However, a good nomenclature also should encapsulate all characteristics of this condition, but this is lacking in current CM research, as the pathophysiological mechanisms are not elucidated for the majority of CMs.

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.

Driving effect of multiplex factors on human brucellosis in high incidence region, implication for brucellosis based on one health concept.

Brucellosis is a typical zoonosis driven by various risk factors, including environmental ones. The present study aimed to explore the driving effect of environmental factors on human brucellosis in a high incidence rate area, which provides understanding and implications in mitigating disease transmission risk in a multi-system between the human-animal-environment interface for preventing and controlling brucellosis based on the One Health concept. Based on the monthly time series data of human brucellosis and environmental variables, a Seasonal Autoregressive Integrated Moving Average Model with explanatory variables (SARIMAX) was applied to assess the association between environmental indicators and human brucellosis incidence (IHB). The results indicated distinct seasonal fluctuation during the study duration, tending to climb from April to August. Atmospheric pressure, precipitation, relative humidity, mean temperature, sunshine duration, and normalized difference vegetation index significantly drive IHB. Moreover, the well-fitting and predicting capability were performed and assessed in the optimal model was the SARIMAX (0,1,1) (0,1,1)12 model with the normalized difference vegetation index (ß = 0.349, P = 0.036) and mean temperature (ß = 0.133, P = 0.046) lagged in 6 months, and the precipitation lagged in 1 month (ß = -0.090, P = 0.004). Our study suggests the association between environmental risk factors and human brucellosis infection, which can be contributed to mitigating the transmission risk in the environmental drivers in a multi-system interface through comprehensive prevention and intervention strategies based on the One Health concept.

A hybrid of long short-term memory neural network and autoregressive integrated moving average model in forecasting HIV incidence and morality of post-neonatal population in East Asia: global burden of diseases 2000-2019.

BACKGROUND: To forecast the human immunodeficiency virus (HIV) incidence and mortality of post-neonatal population in East Asia including North Korea, South Korea, Mongolia, Japan and China Mainland and Taiwan province. METHODS: The data on the incidence and mortality of HIV in post-neonatal population from East Asia were obtained from the Global Burden of Diseases (GBD). The morbidity and mortality of post-neonatal HIV population from GBD 2000 to GBD 2013 were applied as the training set and the morbidity and mortality from GBD 2014 to GBD 2019 were used as the testing set. The hybrid of ARIMA and LSTM model was used to construct the model for assessing the morbidity and mortality in the countries and territories of East Asia, and predicting the morbidity and mortality in the next 5 years. RESULTS: In North Korea, the incidence and mortality of HIV showed a rapid increase during 2000-2010 and a gradual decrease during 2010-2019. The incidence of HIV was predicted to be increased and the mortality was decreased. In South Korea, the incidence was increased during 2000-2010 and decreased during 2010-2019, while the mortality showed fluctuant trend. As predicted, the incidence of HIV in South Korea might be increased and the mortality might be decreased during 2020-2025. In Mongolia, the incidence and mortality were slowly decreased during 2000-2005, increased during 2005-2015, and rapidly decreased till 2019. The predicted incidence and mortality of HIV showed a decreased trend. As for Japan, the incidence of HIV was rapidly increased till 2010 and then decreased till 2015. The predicted incidence of HIV in Japan was gradually increased. The mortality of HIV in Japan was fluctuant during 2000-2019 and was slowly decreased as predicted. The incidence and mortality of HIV in Taiwan during 2000-2019 was increased on the whole. The predicted incidence of HIV during was stationary and the mortality was decreased. In terms of China Mainland, the incidence and mortality of HIV was fluctuant during 2000-2019. The predicted incidence of HIV in China Mainland was stationary while the mortality was rapidly decreased. CONCLUSION: On the whole, the incidence of HIV combined with other diseases in post-neonatal population was increased before 2010 and then decreased during 2010-2019 while the mortality of those patients was decreased in East Asia.

Prediction of congenital heart disease for newborns: comparative analysis of Holt-Winters exponential smoothing and autoregressive integrated moving average models.

OBJECTIVE: To describe the temporal trend of the number of new congenital heart disease (CHD) cases among newborns in Jinhua from 2019 to 2020 and explored an appropriate model to fit and forecast the tendency of CHD. METHODS: Data on CHD from 2019 to 2020 was collected from a health information system. We counted the number of newborns with CHD weekly and separately used the additive Holt-Winters ES method and ARIMA model to fit and predict the number of CHD for newborns in Jinhua. By comparing the mean square error, rooted mean square error and mean absolute percentage error of each approach, we evaluated the effects of different approaches for predicting the number of CHD in newborns. RESULTS: A total of 1135 newborns, including 601 baby girls and 534 baby boys, were admitted for CHD from HIS in Jinhua during the 2-year study period. The prevalence of CHD among newborns in Jinhua in 2019 was 0.96%. Atrial septal defect was diagnosed the most frequently among all newborns with CHD. The number of CHD cases among newborns remained stable in 2019 and 2020. There were fewer cases in spring and summer, while cases peaked in November and December. The ARIMA(2,1,1) model relatively offered advantages over the additive Holt-winters ES method in predicting the number of newborns with CHD, while the accuracy of ARIMA(2,1,1) was not very ideal. CONCLUSIONS: The diagnosis of CHD is related to many risk factors, therefore, when using temporal models to fit and predict the data, we must consider such factors' influence and try to incorporate them into the models.

Did Human Microbes Affect Tourist Arrivals before the COVID-19 Shock? Pre-Effect Forecasting Model for Slovenia.

In 2020, with a substantial decline in tourist arrivals slightly before the time of COVID-19, the innovative econometric approach predicted possible responses between the spread of human microbes (bacteria/viruses) and tourist arrivals. The article developed a conceptually tested econometric model for predicting an exogenous shock on tourist arrivals driven by the spread of disease using a time series approach. The reworked study is based on an autoregressive integrated moving average (ARIMA) model to avoid spurious results. The periods of robust empirical study were obtained from the data vectors i) from January 2008 to December 2018 and ii) from January 2008 to December 2020. The data were obtained from the National Institute of Public Health (NIPH) and the Statistical Office of the Republic of Slovenia. The ARIMA model predicted the number of declines in tourist arrivals for the approaching periods due to the spread of viruses. Before the outbreak of COVID-19, pre-pandemic results confirmed a one-fifth drop in tourist arrivals in the medium term. In the short term, the decline could be more than three-quarters. A further shock can be caused by forecasted bacterial infections; less likely to reduce tourist demand in the long term. The results can improve the evidence for public health demand in risk reduction for tourists as possible patients. The data from the NIPH are crucial for monitoring public health and tourism management as a base for predictions of unknown events.

Exploring China stepping into the dawn of chemical pesticide-free agriculture in 2050.

China has implemented a series of policies to reduce the usage of chemical pesticides to maintain food production safety and to reduce water and soil pollution. However, there is still a huge gap in developing biological pesticides to replace chemical agents or managing pests to prevent crop production loss. It is necessary to predict the future use of chemical pesticides and to exploit the potential ways to control pests and crop diseases. Pesticide usage is affected by seasonal changes and analyzed by using a seasonal autoregressive integrated moving average (ARIMA) model (a statistical model that predicts future trends using time-series data). The future development of biopesticides in China was predicted using the compound annual growth rate (CAGR), which is calculated via the equation [(Final value/Starting value)1/years - 1] according to the annual growth rate of target products over time. According to the reducing trend of pesticide and biological pesticide usage annually, China is predicted possibly step into the era of pesticide-free agriculture in 2050 based on the analysis of the ARIMA model. With CAGR calculation, China will produce from 500 thousand to one million tons of biopesticides in 2050, which can meet the need to replace chemical pesticides in agriculture to prevent the present crop production loss. To achieve the goal, China still has the greatest challenges to develop biopesticides and use various strategies to control pest and crop diseases. China may step into the dawn of chemical pesticide-free agriculture in 2050 if biopesticides can be developed smoothly and pests can be controlled well using various strategies.

Distributed lag interrupted time series model for unclear intervention timing: effect of a statement of emergency during COVID-19 pandemic.

BACKGROUND: Interrupted time series (ITS) analysis has become a popular design to evaluate the effects of health interventions. However, the most common formulation for ITS, the linear segmented regression, is not always adequate, especially when the timing of the intervention is unclear. In this study, we propose a new model to overcome this limitation. METHODS: We propose a new ITS model, ARIMAITS-DL, that combines (1) the Autoregressive Integrated Moving Average (ARIMA) model and (2) distributed lag functional terms. The ARIMA technique allows us to model autocorrelation, which is frequently observed in time series data, and the decaying cumulative effect of the intervention. By contrast, the distributed lag functional terms represent the idea that the intervention effect does not start at a fixed time point but is distributed over a certain interval (thus, the intervention timing seems unclear). We discuss how to select the distribution of the effect, the model construction process, diagnosing the model fitting, and interpreting the results. Further, our model is implemented as an example of a statement of emergency (SoE) during the coronavirus disease 2019 pandemic in Japan. RESULTS: We illustrate the ARIMAITS-DL model with some practical distributed lag terms to examine the effect of the SoE on human mobility in Japan. We confirm that the SoE was successful in reducing the movement of people (15.0-16.0% reduction in Tokyo), at least between February 20 and May 19, 2020. We also provide the R code for other researchers to easily replicate our method. CONCLUSIONS: Our model, ARIMAITS-DL, is a useful tool as it can account for the unclear intervention timing and distributed lag effect with autocorrelation and allows for flexible modeling of different types of impacts such as uniformly or normally distributed impact over time.

Shigellosis seasonality and transmission characteristics in different areas of China: A modelling study.

Objective: In China, the burden of shigellosis is unevenly distributed, notably across various ages and geographical areas. Shigellosis temporal trends appear to be seasonal. We should clarify seasonal warnings and regional transmission patterns. Method: This study adopted a Logistic model to assess the seasonality and a dynamics model to compare the transmission in different areas. The next-generation matrix was used to calculate the effective reproduction number (R eff) to quantify the transmissibility. Results: In China, the rate of shigellosis fell from 35.12 cases per 100,000 people in 2005 to 7.85 cases per 100,000 people in 2017, peaking in June and August. After simulation by the Logistic model, the 'peak time' is mainly concentrated from mid-June to mid-July. China's 'early warning time' is primarily focused on from April to May. We predict the 'peak time' of shigellosis is the 6.30th month and the 'early warning time' is 3.87th month in 2021. According to the dynamics model results, the water/food transfer pathway has been mostly blocked off. The transmissibility of different regions varies greatly, such as the mean R eff of Longde County (3.76) is higher than Xiamen City (3.15), higher than Chuxiong City (2.52), and higher than Yichang City (1.70). Conclusion: The 'early warning time' for shigellosis in China is from April to May every year, and it may continue to advance in the future, such as the early warning time in 2021 is in mid-March. Furthermore, we should focus on preventing and controlling the person-to-person route of shigellosis and stratified deploy prevention and control measures according to the regional transmission.

Service recommendation driven by a matrix factorization model and time series forecasting.

The rise of high-quality cloud services has made service recommendation a crucial research question. Quality of Service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. The fact is that users only consume a few of the broad range of existing services. Thereby, perform a high-accurate service recommendation becomes a challenging task. To tackle the aforementioned challenges, we propose a data sparsity resilient service recommendation approach that aims to predict relevant services in a sustainable manner for end-users. Indeed, our method performs both a QoS prediction of the current time interval using a flexible matrix factorization technique and a QoS prediction of the future time interval using a time series forecasting method based on an AutoRegressive Integrated Moving Average (ARIMA) model. The service recommendation in our approach is based on a couple of criteria ensuring in a lasting way, the appropriateness of the services returned to the active user. The experiments are conducted on a real-world dataset and demonstrate the effectiveness of our method compared to the competing recommendation methods.

Research on the sustainable development of agricultural product supply chain in three northeast provinces in China.

Background: The sustainable development of the agricultural product supply chain (APSC) is the key to protecting public health. Methods: This paper explores the sustainable development status of the APSC in three northeast provinces of China from 2007 to 2020 and the development trend in the next 5 years by using the entropy weight-matter-element extension model (MEEM) and autoregressive integrated moving average model (ARIMA), taking into account the background of relatively backward development and the high proportion of agricultural output in these three provinces. Results: According to the research results, the sustainable development of the APSC in Jilin Province is relatively stable, Heilongjiang Province has made considerable progress in the sustainable development of the APSC in recent years, while Liaoning Province has shown a significant downward trend in recent years in the sustainable development of the APSC, despite a strong development momentum in previous years. Conclusions: The findings of this paper can be applied to the governance of APSC in other rural areas with uneven development. The assessment also provides guidance on the quality and safety of agricultural products and public health, and raises the awareness of policymakers on the importance of the APSC.

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.

Establishment and Evaluation of a Time Series Model for Predicting the Seasonality of Acute Upper Gastrointestinal Bleeding.

OBJECTIVE: We aimed to establish and evaluate a time series model for predicting the seasonality of acute upper gastrointestinal bleeding (UGIB). METHODS: Patients with acute UGIB who were admitted to the Emergency Department and Gastrointestinal Endoscopy Center of Guangdong Provincial Hospital of Traditional Chinese Medicine from January 2013 to December 2019 were enrolled in the present study. The incidence trend of UGIB was analyzed by seasonal decomposition method. Then, exponential smoothing model and autoregressive integrated moving average model (ARIMA) were used to establish the model and forecast, respectively. RESULTS: Finally, the exponential smoothing model with better fitting and prediction effect was selected. The smooth R2 was 0.586, and the Ljung-Box Q (18) statistic value was 22.272 (P = 0.135). The incidence of UGIB had an obvious seasonal trend, with a peak in annual January and a seasonal factor of 140%. After that, the volatility had gradually declined, with a trough in August and a seasonal factor of 67.8%. Since then, it had gradually increased. CONCLUSION: The prediction effect of exponential smoothing model is better, which can provide prevention and treatment strategies for UGIB, and provide objective guidance for more medical staff in Emergency Department and Gastrointestinal Endoscopy Center during the peak period of UGIB.

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.

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.

SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA.

BACKGROUND: The early warning model of infectious diseases plays a key role in prevention and control. This study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect. METHODS: Data on notified HFRS cases in Weifang city, Shandong Province were collected from the official website and Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases. RESULTS: Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA (1, 0.11, 2)(1, 0, 1)12: Akaike information criterion (AIC):-631.31; SARIMA (1, 0, 2)(1, 1, 1)12: AIC: - 227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE):0.058; SARIMA: RMSE: 0.090). CONCLUSIONS: The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

Exploring the Dynamics of Hemorrhagic Fever with Renal Syndrome Incidence in East China Through Seasonal Autoregressive Integrated Moving Average Models.

OBJECTIVE: The purpose of this study was to explore the dynamics of incidence of hemorrhagic fever with renal syndrome (HFRS) from 2000 to 2017 in Anqiu City, a city located in East China, and find the potential factors leading to the incidence of HFRS. METHODS: Monthly reported cases of HFRS and climatic data from 2000 to 2017 in the city were obtained. Seasonal autoregressive integrated moving average (SARIMA) models were used to fit the HFRS incidence and predict the epidemic trend in Anqiu City. Univariate and multivariate generalized additive models were fit to identify and characterize the association between the HFRS incidence and meteorological factors during the study period. RESULTS: Statistical analysis results indicate that the annualized average incidence at the town level ranged from 1.68 to 6.31 per 100,000 population among 14 towns in the city, and the western towns exhibit high endemic levels during the study periods. With high validity, the optimal SARIMA(0,1,1,)(0,1,1)12 model may be used to predict the HFRS incidence. Multivariate generalized additive model (GAM) results show that the HFRS incidence increases as sunshine time and humidity increases and decreases as precipitation increases. In addition, the HFRS incidence is associated with temperature, precipitation, atmospheric pressure, and wind speed. Those are identified as the key climatic factors contributing to the transmission of HFRS. CONCLUSION: This study provides evidence that the SARIMA models can be used to characterize the fluctuations in HFRS incidence. Our findings add to the knowledge of the role played by climate factors in HFRS transmission and can assist local health authorities in the development and refinement of a better strategy to prevent HFRS transmission.