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 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 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 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 Forecasting the incidence of mumps in Chongqing based on a SARIMA model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hongfang Qiu ◽  
Han Zhao ◽  
Haiyan Xiang ◽  
Rong Ou ◽  
Jing Yi ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Moving Average ◽  
Health Sector ◽  
Incidence Rates ◽  
Percentage Error ◽  
Simple Method ◽  
Sarima Model ◽  
Incidence Data ◽  
Control And Prevention ◽  
Infection Disease

Abstract Background Mumps is classified as a class C infection disease in China, and the Chongqing area has one of the highest incidence rates in the country. We aimed to establish a prediction model for mumps in Chongqing and analyze its seasonality, which is important for risk analysis and allocation of resources in the health sector. Methods Data on incidence of mumps from January 2004 to December 2018 were obtained from Chongqing Municipal Bureau of Disease Control and Prevention. The incidence of mumps from 2004 to 2017 was fitted using a seasonal autoregressive comprehensive moving average (SARIMA) model. The root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to compare the goodness of fit of the models. The 2018 incidence data were used for validation. Results From 2004 to 2018, a total of 159,181 cases (93,655 males and 65,526 females) of mumps were reported in Chongqing, with significantly more men than women. The age group of 0–19 years old accounted for 92.41% of all reported cases, and students made up the largest proportion (62.83%), followed by scattered children and children in kindergarten. The SARIMA(2, 1, 1) × (0, 1, 1)12 was the best fit model, RMSE and MAPE were 0.9950 and 39.8396%, respectively. Conclusion Based on the study findings, the incidence of mumps in Chongqing has an obvious seasonal trend, and SARIMA(2, 1, 1) × (0, 1, 1)12 model can also predict the incidence of mumps well. The SARIMA model of time series analysis is a feasible and simple method for predicting mumps in Chongqing.

scholarly journals Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Loshini Thiruchelvam ◽  
Sarat C. Dass ◽  
Rafdzah Zaki ◽  
Abqariyah Yahya ◽  
Vijanth S. Asirvadam
Keyword(s):  
Air Quality ◽  
Goodness Of Fit ◽  
Model Comparison ◽  
Prediction Models ◽  
Moving Average ◽  
Pollution Index ◽  
Information Criterion ◽  
Air Pollutant ◽  
Dengue Case ◽  
Arima Models

This study investigated the potential relationship between dengue cases and air quality – as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were –800.66, –796.22, and –790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.

scholarly journals The changing epidemiology of hemorrhagic fever with renal syndrome in Southeastern China during 1963–2020: A retrospective analysis of surveillance data

2021 ◽  
Vol 15 (8) ◽  
pp. e0009673
Author(s):  
Rong Zhang ◽  
Zhiyuan Mao ◽  
Jun Yang ◽  
Shelan Liu ◽  
Ying Liu ◽  
...  
Keyword(s):  
Geographical Distribution ◽  
Moving Average ◽  
Hemorrhagic Fever ◽  
Seasonal Distribution ◽  
Zhejiang Province ◽  
Surveillance Data ◽  
Female Ratio ◽  
Control And Prevention ◽  
Epidemiological Characteristics ◽  
Demographic Features

Background Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by hantavirus which was endemic Zhejiang Province, China. In this study, we aim to explore the changing epidemiology of HFRS in Zhejiang, identify high-risk areas and populations, and evaluate relevant policies and interventions to better improve HFRS control and prevention. Methods Surveillance data on HFRS during 1963–2020 in Zhejiang Province were extracted from Zhejiang Provincial Center for Disease Control and Prevention archives and the Chinese Notifiable Disease Reporting System. The changing epidemiological characteristics of HFRS including seasonal distribution, geographical distribution, and demographic features, were analyzed using joinpoint regression, autoregressive integrated moving average model, descriptive statistical methods, and Spatio-temporal cluster analysis. Results From 1963 to 2020, 114 071 HFRS cases and 1269 deaths were reported in Zhejiang Province. The incidence increased sharply from 1973 and peaked in 1986, then decreased steadily and maintained a stable incidence from 2004. HFRS cases were reported in all 11 prefecture-level cities of Zhejiang Province from 1963 to 2020. The joint region (Shengzhou, Xinchang, Tiantai, and surrounding areas), and Kaihua County are the most seriously affected regions throughout time. After 1990, the first HFRS incidence peak was in May-June, with another one from November to January. Most HFRS cases occurred in 21- (26.48%) and 30- years group (24.25%) from 1991 to 2004, but 41- (25.75%) and 51-years (23.30%) had the highest proportion from 2005 to 2020. Farmers accounted for most cases (78.10%), and cases are predominantly males with a male-to-female ratio of 2.6:1. It was found that the median time from onset to diagnosis was 6.5 days (IQR 3.75–10.42), and the time from diagnosis to disease report was significantly shortened after 2011. Conclusions We observed dynamic changes in the seasonal distribution, geographical distribution, and demographic features of HFRS, which should be well considered in the development of control and prevention strategies in future. Additional researches are warranted to elucidate the environmental, meteorological, and social factors associated with HFRS incidence in different decades.

scholarly journals Simulation and forecasting of the influenza epidemic process using seasonal autoregressive integrated moving average model

2021 ◽  
pp. 22-35
Author(s):  
Dmytro Chumachenko ◽  
Ievgen Meniailov ◽  
Andrii Hrimov ◽  
Vladislav Lopatka ◽  
Olha Moroz ◽  
...  
Keyword(s):  
Process Simulation ◽  
Respiratory Infections ◽  
Moving Average ◽  
Simulation Models ◽  
Influenza Epidemic ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Epidemic Process ◽  
Moving Average Model ◽  
Control And Prevention

Today's global COVID-19 pandemic has affected the spread of influenza. COVID-19 and influenza are respiratory infections and have several similar symptoms. They are, however, caused by various viruses; there are also some differences in the categories of people most at risk of severe forms of these diseases. The strategies for their treatment are also different. Mathematical modeling is an effective tool for controlling the epidemic process of influenza in specified territories. The results of modeling and forecasts obtained with the help of simulation models make it possible to develop timely justified anti-epidemic measures to reduce the dynamics of the incidence of influenza. The study aims to develop a seasonal autoregressive integrated moving average (SARIMA) model for influenza epidemic process simulation and to investigate the experimental results of the simulation. The work is targeted at the influenza epidemic process and its dynamic in the territory of Ukraine. The subjects of the research are methods and models of epidemic process simulation, which include machine learning methods, in particular the SARIMA model. To achieve the aim of the research, we have used methods of forecasting and have built the influenza epidemic process SARIMA model. Because of experiments with the developed model, the predictive dynamics of the epidemic process of influenza for 10 weeks were obtained. Such a forecast can be used by persons making decisions on the implementation of anti-epidemic and deterrent measures if the forecast exceeds the epidemic thresholds of morbidity. Conclusions. The paper describes experimental research on the application of the SARIMA model to the epidemic process of influenza simulation. Models have been verified by influenza morbidity in the Kharkiv region (Ukraine) in epidemic seasons for the time ranges as follows: 2017-18, 2018-19, 2019-20, and 2020-21. Data were provided by the Kharkiv Regional Centers for Disease Control and Prevention of the Ministry of Health of Ukraine. The forecasting results show a downward trend in the dynamics of the epidemic process of influenza in the Kharkiv region. It is due to the introduction of anti-epidemic measures aimed at combating COVID-19. Activities such as wearing masks, social distancing, and lockdown also contribute to reducing seasonal influenza epidemics.

scholarly journals The effect of climate variables on the incidence of Crimean Congo Hemorrhagic Fever (CCHF) in Zahedan, Iran

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sairan Nili ◽  
Narges Khanjani ◽  
Yunes Jahani ◽  
Bahram Bakhtiari
Keyword(s):  
Goodness Of Fit ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Generalized Additive Models ◽  
Hemorrhagic Fever ◽  
Additive Models ◽  
Meteorological Variables ◽  
Fatality Rate ◽  
Crimean Congo Hemorrhagic Fever ◽  
Sarima Models

Abstract Background The Crimean-Congo Hemorrhagic fever (CCHF) is endemic in Iran and has a high fatality rate. The aim of this study was to investigate the association between CCHF incidence and meteorological variables in Zahedan district, which has a high incidence of this disease. Methods Data about meteorological variables and CCHF incidence was inquired from 2010 to 2017 for Zahedan district. The analysis was performed using univariate and multivariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models and Generalized Additive Models (GAM) using R software. AIC, BIC and residual tests were used to test the goodness of fit of SARIMA models, and R2 was used to select the best model in GAM/GAMM. Results During the years under study, 190 confirmed cases of CCHF were identified in Zahedan district. The fatality rate of the disease was 8.42%. The disease trend followed a seasonal pattern. The results of multivariate SARIMA showed the (0,1,1) (0,1,1)12 model with maximum monthly temperature lagged 5 months, forecasted the disease better than other models. In the GAM, monthly average temperature lagged 5 months, and the monthly minimum of relative humidity and total monthly rainfall without lag, had a nonlinear relation with the incidence of CCHF. Conclusions Meteorological variables can affect CCHF occurrence.

Public health professionals’ perceptions of the capacity of China’s CDCs to address emerging and re-emerging infectious diseases

2019 ◽  
Author(s):  
Michael Xiaoliang Tong ◽  
Alana Hansen ◽  
Scott Hanson-Easey ◽  
Jianjun Xiang ◽  
Scott Cameron ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Health Professionals ◽  
Disease Surveillance ◽  
Emerging Infectious Diseases ◽  
Public Health Professionals ◽  
International Collaborations ◽  
Future Challenges ◽  
Control And Prevention ◽  
And Control

AbstractBackgroundChina’s capacity to control and prevent emerging and re-emerging infectious diseases is critical to the nation’s population health. This study aimed to explore the capacity of Centers for Disease Control and Prevention (CDCs) in China to deal with infectious diseases now and in the future.MethodsA survey was conducted in 2015 among 973 public health professionals at CDCs in Beijing and four provinces, to assess their capacity to deal with emerging and re-emerging infectious diseases.ResultsAlthough most professionals were confident with the current capacity of CDCs to cope with outbreaks, nearly all indicated more funding was required to meet future challenges. Responses indicated that Yunnan Province faced more challenges than Anhui, Henan and Liaoning Provinces in being completely prepared and able to deal with outbreaks. Participants aged 20–39 years were more likely than those aged 40 and over to believe strategies such as interdisciplinary and international collaborations for disease surveillance and control, would assist capacity building.ConclusionThe capacity of China’s CDCs to deal with infectious diseases was excellent. However, findings suggest it is imperative to increase the number of skilled CDC staff, financial support, and strengthen county level staff training and health education programs.

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