scholarly journals Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China

2018 ◽  
Vol 146 (13) ◽  
pp. 1680-1688 ◽  
Author(s):  
Ling Sun ◽  
Lu-Xi Zou
Keyword(s):  
Moving Average ◽  
Mainland China ◽  
Public Health Problem ◽  
Information Criterion ◽  
Hemorrhagic Fever ◽  
Geographical Information ◽  
Spatiotemporal Pattern ◽  
Forecasting Model ◽  
Sarima Model ◽  
Province Level

AbstractHemorrhagic fever with renal syndrome (HFRS) caused by hantaviruses is a serious public health problem in China, accounting for 90% of HFRS cases reported globally. In this study, we applied geographical information system (GIS), spatial autocorrelation analyses and a seasonal autoregressive-integrated moving average (SARIMA) model to describe and predict HFRS epidemic with the objective of monitoring and forecasting HFRS in mainland China. Chinese HFRS data from 2004 to 2016 were obtained from National Infectious Diseases Reporting System (NIDRS) database and Chinese Centre for Disease Control and Prevention (CDC). GIS maps were produced to detect the spatial distribution of HFRS cases. The Moran's I was adopted in spatial global autocorrelation analysis to identify the integral spatiotemporal pattern of HFRS outbreaks, while the local Moran's Ii was performed to identify ‘hotspot’ regions of HFRS at province level. A fittest SARIMA model was developed to forecast HFRS incidence in the year 2016, which was selected by Akaike information criterion and Ljung–Box test. During 2004–2015, a total of 165 710 HFRS cases were reported with the average annual incidence at province level ranged from 0 to 13.05 per 100 000 persons. Global Moran's I analysis showed that the HFRS outbreaks presented spatially clustered distribution, with the degree of cluster gradually decreasing from 2004 to 2009, then turned out to be randomly distributed and reached lowest point in 2012. Local Moran's Ii identified that four provinces in northeast China contributed to a ‘high–high’ cluster as a traditional epidemic centre, and Shaanxi became another HFRS ‘hotspot’ region since 2011. The monthly incidence of HFRS decreased sharply from 2004 to 2009 in mainland China, then increased markedly from 2010 to 2012, and decreased again since 2013, with obvious seasonal fluctuations. The SARIMA ((0,1,3) × (1,0,1)12) model was the most fittest forecasting model for the dataset of HFRS in mainland China. The spatiotemporal distribution of HFRS in mainland China varied in recent years; together with the SARIMA forecasting model, this study provided several potential decision supportive tools for the control and risk-management plan of HFRS in China.

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

2020 ◽  
Author(s):  
Chang Qi ◽  
Dandan Zhang ◽  
Yuchen Zhu ◽  
Lili Liu ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Predictive Ability ◽  
Information Criterion ◽  
Hemorrhagic Fever ◽  
Forecast Performance ◽  
Sarima Model ◽  
Control And Prevention ◽  
And Control

Abstract Background The early warning model of infectious diseases plays a key role in prevention and control. Our 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 Disease Reporting Information System of the 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.

scholarly journals Analysis of Hemorrhagic Fever With Renal Syndrome Using Wavelet Tools in Mainland China, 2004–2019

2020 ◽  
Vol 8 ◽  
Author(s):  
Lu-Xi Zou ◽  
Ling Sun
Keyword(s):  
Mainland China ◽  
Public Health Problem ◽  
Hemorrhagic Fever ◽  
Geographical Information ◽  
Time Intervals ◽  
Accurate Analysis ◽  
Cyclical Fluctuation ◽  
Support Tools ◽  
Life Threatening ◽  
Set Up

Introduction : Hemorrhagic fever with renal syndrome (HFRS) is a life-threatening public health problem in China, accounting for ~90% of HFRS cases reported globally. Accurate analysis and prediction of the HFRS epidemic could help to establish effective preventive measures.Materials and Methods : In this study, the geographical information system (GIS) explored the spatiotemporal features of HFRS, the wavelet power spectrum (WPS) unfolded the cyclical fluctuation of HFRS, and the wavelet neural network (WNN) model predicted the trends of HFRS outbreaks in mainland China.Results : A total of 209,209 HFRS cases were reported in mainland China from 2004 to 2019, with the annual incidence ranged from 0 to 13.05 per 100,0000 persons at the province level. The WPS proved that the periodicity of HFRS could be half a year, 1 year, and roughly 7-year at different time intervals. The WNN structure of 12-6-1 was set up as the fittest forecasting model for the HFRS epidemic.Conclusions : This study provided several potential support tools for the control and risk-management of HFRS in China.

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

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Chang Qi ◽  
Dandan Zhang ◽  
Yuchen Zhu ◽  
Lili Liu ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Predictive Ability ◽  
Information Criterion ◽  
Hemorrhagic Fever ◽  
Forecast Performance ◽  
Sarima Model ◽  
Control And Prevention ◽  
And Control

Abstract 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.

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

2020 ◽  
Author(s):  
Chang Qi ◽  
Dandan Zhang ◽  
Yuchen Zhu ◽  
Lili Liu ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Predictive Ability ◽  
Information Criterion ◽  
Hemorrhagic Fever ◽  
Forecast Performance ◽  
Sarima Model ◽  
Control And Prevention ◽  
And Control

Abstract Background The early warning model of infectious diseases plays a key role in prevention and control. Our 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 supplied by the Disease Reporting Information System of the Shandong Center for Disease Control and Prevention from January 1, 2005 to December 31, 2018. The SARFIMA model considering both the short-memory and long-memory were performed to fit and predict the HFRS series. Besides, we compared accuracy of fitting and prediction between SARFIMA and SARIMA which were used widely in infectious diseases. Results Both SARFIMA and SARIMA models show good fit of data. Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA(2, 0.15, 2)(1, 0, 0) 12 : Akaike information criterion (AIC): -630.61; SARIMA(2, 0, 2)(1, 1, 0) 12 : AIC: -196.04) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE): 0.067; SARIMA: RMSE: 0.111). Conclusions The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help us to improve the forecast of HFRS incidence.

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

2020 ◽  
Author(s):  
Chang Qi ◽  
Dandan Zhang ◽  
Yuchen Zhu ◽  
Lili Liu ◽  
Chunyu Li ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Predictive Ability ◽  
Information Criterion ◽  
Hemorrhagic Fever ◽  
Forecast Performance ◽  
Sarima Model ◽  
Control And Prevention ◽  
And Control

Abstract Background: The early warning model of infectious diseases plays a key role in prevention and control. Our 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 Disease Reporting Information System of the 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.

scholarly journals A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of São Paulo, Brazil

2011 ◽  
Vol 44 (4) ◽  
pp. 436-440 ◽  
Author(s):  
Edson Zangiacomi Martinez ◽  
Elisângela Aparecida Soares da Silva ◽  
Amaury Lelis Dal Fabbro
Keyword(s):  
Moving Average ◽  
Forecasting Model ◽  
Time Series Models ◽  
R Software ◽  
Relative Precision ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Predicted Values ◽  
Best Fit ◽  
Monthly Incidence

INTRODUCTION: Forecasting dengue cases in a population by using time-series models can provide useful information that can be used to facilitate the planning of public health interventions. The objective of this article was to develop a forecasting model for dengue incidence in Campinas, southeast Brazil, considering the Box-Jenkins modeling approach. METHODS: The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. We fitted a model based on the reported monthly incidence of dengue from 1998 to 2008, and we validated the model using the data collected between January and December of 2009. RESULTS: SARIMA (2,1,2) (1,1,1)12 was the model with the best fit for data. This model indicated that the number of dengue cases in a given month can be estimated by the number of dengue cases occurring one, two and twelve months prior. The predicted values for 2009 are relatively close to the observed values. CONCLUSIONS: The results of this article indicate that SARIMA models are useful tools for monitoring dengue incidence. We also observe that the SARIMA model is capable of representing with relative precision the number of cases in a next year.

scholarly journals Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Cai-Xia Lv ◽  
Shu-Yi An ◽  
Bao-Jun Qiao ◽  
Wei Wu
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Arima Model ◽  
Mainland China ◽  
Hemorrhagic Fever ◽  
Prediction Performance ◽  
Forecasting Model ◽  
Incidence Data ◽  
One Step ◽  
The One

Abstract Background Hemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators. Methods We collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training sets to establish the seasonal ARIMA model and XGBoost model, while the 2018 data were used to test the prediction performance. In the multistep XGBoost forecasting model, one-hot encoding was used to handle seasonal features. Furthermore, a series of evaluation indices were performed to evaluate the accuracy of the multistep forecast XGBoost model. Results There were 200,237 HFRS cases in China from 2004 to 2018. A long-term downward trend and bimodal seasonality were identified in the original time series. According to the minimum corrected akaike information criterion (CAIC) value, the optimal ARIMA (3, 1, 0) × (1, 1, 0)12 model is selected. The index ME, RMSE, MAE, MPE, MAPE, and MASE indices of the XGBoost model were higher than those of the ARIMA model in the fitting part, whereas the RMSE of the XGBoost model was lower. The prediction performance evaluation indicators (MAE, MPE, MAPE, RMSE and MASE) of the one-step prediction and multistep prediction XGBoost model were all notably lower than those of the ARIMA model. Conclusions The multistep XGBoost prediction model showed a much better prediction accuracy and model stability than the multistep ARIMA prediction model. The XGBoost model performed better in predicting complicated and nonlinear data like HFRS. Additionally, Multistep prediction models are more practical than one-step prediction models in forecasting infectious diseases.

scholarly journals Evaluation and planning of Chagas control activities using geospatial tools

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Diego Weinberg ◽  
Mario Lanfri ◽  
Carlos M. Scavuzzo ◽  
Marcelo Abril ◽  
Sofía Lanfri
Keyword(s):  
Rural Communities ◽  
Linear Models ◽  
Control Strategies ◽  
Public Health Problem ◽  
Spatiotemporal Analysis ◽  
Point Of View ◽  
Geographical Information ◽  
Triatoma Infestans ◽  
Spatiotemporal Pattern ◽  
History Of

Chagas continues to be a relevant public health problem in Latin America. In this work, we present a spatiotemporal analysis applied for the evaluation and planning of Chagas vector control strategies. We analysed the spatial distribution of the vector Triatoma infestans infestation related to ongoing control interventions cycles in rural communities near Añatuya, Santiago del Estero, Argentina. A geographical information system was developed for the spatial analysis obtaining, for each house, variables that describe the history of spraying and infestation at each time of interventions. Bi-dimensional histograms were used to describe the spatiotemporal pattern of these activities and peri-domestic infestation at the last intervention was modelled by a neural network model. We qualitatively evaluate control programmes considering the history of infestation and spraying from a spatiotemporal point of view, incorporating new ways of visualising this information. Predictions are based on novel, non-linear models and spatiotemporal indices, which should be useful for strategically allocating Chagas control resources in the future and thus help to better plan spraying strategies.

scholarly journals Predicting Seasonal Influenza Based on SARIMA Model, in Mainland China from 2005 to 2018

2019 ◽  
Vol 16 (23) ◽  
pp. 4760 ◽  
Author(s):  
Cong ◽  
Ren ◽  
Xie ◽  
Wang
Keyword(s):  
Seasonal Influenza ◽  
Moving Average ◽  
Mainland China ◽  
Seasonal Fluctuation ◽  
Influenza Surveillance ◽  
Sarima Model ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Predicted Values ◽  
Relative Errors

Seasonal influenza is one of the mandatorily monitored infectious diseases, in China. Making full use of the influenza surveillance data helps to predict seasonal influenza. In this study, a seasonal autoregressive integrated moving average (SARIMA) model was used to predict the influenza changes by analyzing monthly data of influenza incidence from January 2005 to December 2018, in China. The inter-annual incidence rate fluctuated from 2.76 to 55.07 per 100,000 individuals. The SARIMA (1, 0, 0) × (0, 1, 1) 12 model predicted that the influenza incidence in 2018 was similar to that of previous years, and it fitted the seasonal fluctuation. The relative errors between actual values and predicted values fluctuated from 0.0010 to 0.0137, which indicated that the predicted values matched the actual values well. This study demonstrated that the SARIMA model could effectively make short-term predictions of seasonal influenza.

Prevalence of haemorrhagic fever with renal syndrome in mainland China: analysis of National Surveillance Data, 2004–2009

2011 ◽  
Vol 140 (5) ◽  
pp. 851-857 ◽  
Author(s):  
X. LIU ◽  
B. JIANG ◽  
P. BI ◽  
W. YANG ◽  
Q. LIU
Keyword(s):  
Moving Average ◽  
Demographic Data ◽  
Mainland China ◽  
Decision Support Tool ◽  
Haemorrhagic Fever ◽  
Sarima Model ◽  
Support Tool ◽  
Practical Applications ◽  
Control And Prevention ◽  
Mean Square Error Criterion

SUMMARYThe monthly and annual incidence of haemorrhagic fever with renal syndrome (HFRS) in China for 2004–2009 was analysed in conjunction with associated geographical and demographic data. We applied the seasonal autoregressive integrated moving average (SARIMA) model to fit and forecast monthly HFRS incidence in China. HFRS was endemic in most regions of China except Hainan Province. There was a high risk of infection for male farmers aged 30–50 years. The fitted SARIMA(0,1,1)(0,1,1)12 model had a root-mean-square-error criterion of 0·0133 that indicated accurate forecasts were possible. These findings have practical applications for more effective HFRS control and prevention. The conducted SARIMA model may have applications as a decision support tool in HFRS control and risk-management planning programmes.

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