Data in Brief · 2020 · 780 citations · 5 references
Veterinary MedicineInfectious Disease ControlInfectious DiseasesEpidemiologyForecastingInfectious Disease EpidemiologyDisease ForecastingDisease SpreadDisease Trend AnalysisEpidemic ForecastingTime Series ForecastingTime-series Analysis In EpidemiologyArima ModelsAutoregressive Integrated Moving AverageAutoregressive Integrated Moving Average ForecastingEconometric Forecast ModelsEpidemic Trend AnalysisForecasting Disease OutbreaksForecasting Disease SpreadPandemic Forecasting
COVID‑19 is a global threat, and many studies use mathematical models to predict its evolution, though such models may be biased and require real‑time case definition and data collection for comparison and future analysis. The study proposes a simple econometric model to predict COVID‑19 spread. The authors applied an ARIMA model to Johns Hopkins epidemiological data to forecast COVID‑19 prevalence and incidence trends.
Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.
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Forecasting of demand using ARIMA model
Jamal Fattah, Latifa Ezzine, Zineb Aman et al. · International Journal of Engineering Business Management · 2018 · 457 citations · Full text