ArticleComputer methods and programs in biomedicine update2021
Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models.
Article in Computer methods and programs in biomedicine update, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Global and regional insights: unravelling the epidemiological factors and burden of autism spectrum disorders with a focus on China from 1990 to 2021.BMJ paediatrics open · 2026Article
- Interruption time series analysis using autoregressive integrated moving average model: evaluating the impact of COVID-19 on the epidemic trend of gonorrhea in China.BMC public health · 2023Article
- Forecasting of Beijing PMHeliyon · 2022Article
- Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models.Scientific reports · 2022Article
- Lumpy Skin Disease Outbreaks in Africa, Europe, and Asia (2005-2022): Multiple Change Point Analysis and Time Series Forecast.Viruses · 2022Article
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Abstract
BACKGROUND AND
objectivesAuto regressive integrated moving average (ARIMA) model is a popular model to forecast future values of a time series using the past values of the same series. However, if the variance of the time series varies with time, the 95% confidence interval estimated by the ARIMA will not be accurate. This study proposes a method to revise the ARIMA model to suit time series with heteroscedasticity.
methodsMultiple historical ARIMA models were constructed with publicly available COVID-19 data in Alberta, Canada. The time series between different time periods were applied for these models. The means and their 95% confidence intervals of the differences between the forecasted values and the corresponding actual values were computed. The forecasted values of the general ARIMA models were modified by adding these differences.
resultsThe average incident cases forecasted with the proposed method are lower than those with a general ARIMA model during the forecasted period. The 95% confidence intervals of the forecasted incidence with the proposed method are narrower. During the forecasted period (13 weeks) the average incidence was predicted to increase first and then decrease exponentially.
conclusionThe proposed method can be used to automatically specify the best ARIMA model, to fit time series with heteroscedasticity and to forecast longer period of the trends in the future. In the next 13 weeks, the Covid-19 incidence may decrease but not eliminate. To stop the transmission of infections eventually, persistent effects complying with accurate forecasts are necessary.
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