ArticleJournal of applied statistics2023
Forecasting waved daily COVID-19 death count series with a novel combination of segmented Poisson model and ARIMA models.
Article in Journal of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Prediction of the disease burden of nasopharyngeal carcinoma in China: A comparative analysis of ARIMA, LSTM, and DNN models.Medicine · 2025Article
- Editorial to the special issue: statistical perspectives on analytics for COVID-19 data.Journal of applied statistics · 2023Article
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2 authors.
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Abstract
Autoregressive Integrated Moving Average (ARIMA) models have been widely used to forecast and model the development of various infectious diseases including COVID-19 outbreaks; however, such use of ARIMA models does not respect the count nature of the pandemic development data. For example, the daily COVID-19 death count series data for Canada and the United States (USA) are generally skewed with lots of low counts. In addition, there are generally waved patterns with turning points influenced by government major interventions against the spread of COVID-19 during different periods and seasons. In this study, we propose a novel combination of the segmented Poisson model and ARIMA models to handle these features and correlation structures in a two-stage process. The first stage of this process is a generalization of trend analysis of time series data. Our approach is illustrated with forecasting and modeling of daily COVID-19 death count series data for Canada and the USA.
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