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

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.

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

10.21203/rs.2.22527/v3 ◽

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.

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Forecasting the incidence of acute haemorrhagic conjunctivitis in Chongqing: a time series analysis

Epidemiology and Infection ◽

10.1017/s095026882000182x ◽

2020 ◽

Vol 148 ◽

Author(s):

Hongfang Qiu ◽

Dewei Zeng ◽

Jing Yi ◽

Hua Zhu ◽

Ling Hu ◽

...

Keyword(s):

Moving Average ◽

Development Trend ◽

Exponential Smoothing ◽

Percentage Error ◽

Peak Period ◽

Sarima Model ◽

Autoregressive Integrated Moving Average ◽

Incidence Data ◽

The Mean ◽

Actual Incidence

Abstract Acute haemorrhagic conjunctivitis is a highly contagious eye disease, the prediction of acute haemorrhagic conjunctivitis is very important to prevent and grasp its development trend. We use the exponential smoothing model and the seasonal autoregressive integrated moving average (SARIMA) model to analyse and predict. The monthly incidence data from 2004 to 2017 were used to fit two models, the actual incidence of acute haemorrhagic conjunctivitis in 2018 was used to validate the model. Finally, the prediction effect of exponential smoothing is best, the mean square error and the mean absolute percentage error were 0.0152 and 0.1871, respectively. In addition, the incidence of acute haemorrhagic conjunctivitis in Chongqing had a seasonal trend characteristic, with the peak period from June to September each year.

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Time series analysis and forecasting of chlamydia trachomatis incidence using surveillance data from 2008 to 2019 in Shenzhen, China

Epidemiology and Infection ◽

10.1017/s0950268820000680 ◽

2020 ◽

Vol 148 ◽

Cited By ~ 2

Author(s):

R. X. Weng ◽

H. L. Fu ◽

C. L. Zhang ◽

J. B. Ye ◽

F. C. Hong ◽

...

Keyword(s):

Time Series ◽

Chlamydia Trachomatis ◽

Moving Average ◽

Seasonal Fluctuation ◽

Percentage Error ◽

Sarima Model ◽

Autoregressive Integrated Moving Average ◽

Shenzhen City ◽

Control And Prevention

Abstract Chlamydia trachomatis (CT) infection has been a major public health threat globally. Monitoring and prediction of CT epidemic status and trends are important for programme planning, allocating resources and assessing impact; however, such activities are limited in China. In this study, we aimed to apply a seasonal autoregressive integrated moving average (SARIMA) model to predict the incidence of CT infection in Shenzhen city, China. The monthly incidence of CT between January 2008 and June 2019 in Shenzhen was used to fit and validate the SARIMA model. A seasonal fluctuation and a slightly increasing pattern of a long-term trend were revealed in the time series of CT incidence. The monthly CT incidence ranged from 4.80/100 000 to 21.56/100 000. The mean absolute percentage error value of the optimal model was 8.08%. The SARIMA model could be applied to effectively predict the short-term CT incidence in Shenzhen and provide support for the development of interventions for disease control and prevention.

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

BMC Medical Research Methodology ◽

10.1186/s12874-020-01130-8 ◽

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.

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

10.21203/rs.2.22527/v1 ◽

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.

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

10.21203/rs.2.22527/v2 ◽

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.

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Predicting the number of cases of dengue infection in Ribeirão Preto, São Paulo State, Brazil, using a SARIMA model

Cadernos de Saúde Pública ◽

10.1590/s0102-311x2011000900014 ◽

2011 ◽

Vol 27 (9) ◽

pp. 1809-1818 ◽

Cited By ~ 12

Author(s):

Edson Zangiacomi Martinez ◽

Elisângela Aparecida Soares da Silva

Keyword(s):

Moving Average ◽

Dengue Infection ◽

Arima Model ◽

Sao Paulo ◽

São Paulo ◽

Incidence Data ◽

Paulo State ◽

Control And Prevention ◽

São Paulo State ◽

Predicted Values

This study aimed to develop a forecasting model for the incidence of dengue in Ribeirão Preto, São Paulo State, Brazil, using time series analysis. The model was performed using the Seasonal Autoregressive Integrated Moving Average (SARIMA). Firstly, we fitted a model considering monthly notifications of cases of dengue recorded from 2000 to 2008 in Ribeirão Preto. We then extracted predicted values for 2009 from the adjusted model and compared them with the number of cases observed for that year. The SARIMA (2,1,3)(1,1,1)12 model offered best fit for the dengue incidence data. The results showed that the seasonal ARIMA model predicts the number of dengue cases very effectively and reliably, and is a useful tool for disease control and prevention.

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

RADIOELECTRONIC AND COMPUTER SYSTEMS ◽

10.32620/reks.2021.4.02 ◽

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.

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Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province

JTAM | Jurnal Teori dan Aplikasi Matematika ◽

10.31764/jtam.v4i1.1888 ◽

2020 ◽

Vol 4 (1) ◽

pp. 76

Author(s):

Desri Kristina Silalahi

Keyword(s):

Poverty Reduction ◽

Moving Average ◽

Arima Model ◽

Percentage Error ◽

Seasonal Effect ◽

Poor People ◽

West Java ◽

Sarima Model ◽

Reduction Strategies ◽

The Government

The government continues to carry out poverty reduction strategies in Indonesia, especially in West Java Province. West Java Province is a province that has the most populous population in Indonesia. This will affect the level of welfare and the amount of poverty. The strategy undertaken is inseparable from accurate poverty data and is available from year to year. Even from the available data, the government can forecast the number of poor people in the coming years. Seasonal Autoregressive Integrated Moving Average (SARIMA) method is one of forecasting methods. SARIMA is the development of the ARIMA model which has a seasonal effect. Based on the results of the study, that poverty data forecasting in the province of West Java using the SARIMA method obtained SARIMA model (0,1,1) (1,1,1)4. This model is the best model for forecasting data with an R-Squared value of 98%, Mean Square Error is 7.705.5800.000 and Mean Absolute Percentage Error IS 2,81%. It’s means this SARIMA model is very good in predicting poverty data in West Java Province.

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The Analysis of the 2008 US Financial Crisis: An Intervention Approach

Journal of Economics and Behavioral Studies ◽

10.22610/jebs.v10i1.2089 ◽

2018 ◽

Vol 10 (1) ◽

pp. 59

Author(s):

Katleho Daniel Makatjane ◽

Edward Kagiso Molefe ◽

Roscoe Bertrum Van Wyk

Keyword(s):

South Africa ◽

Exchange Rate ◽

Financial Crises ◽

Moving Average ◽

Absolute Error ◽

Percentage Error ◽

Intervention Model ◽

Sarima Model ◽

Error Metrics ◽

The Impact

The current study investigates the impact of the 2008 US financial crises on the real exchange rate in South Africa. The data used in this empirical analysis is for the period from January 2000 to June 2017. The Seasonal autoregressive integrated moving average (SARIMA) intervention charter was used to carry out the analysis. Results revealed that the financial crises period in South Africa occurred in March 2008 and significantly affected the exchange rate. Hence, the impact pattern was abrupt. Using the SARIMA model as a benchmark, four error metrics; to be precise mean absolute error (MAE), mean absolute percentage error (MAPE), mean error (ME) and Mean percentage error (MPE) was used to assess the performance of the intervention model and SARIMA model. The results of the SARIMA intervention model produced better forecasts as compared to that one of SARIMA model.

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