Forecasting COVID-19 Cases in the Philippines Using Various Mathematical Models
Due to the rapid increase of COVID-19 infection cases in many countries such as the Philippines, efforts in forecasting daily infections have been made to better manage the pandemic and respond effectively. In this study, we considered the cumulative COVID-19 infection cases in the Philippines from 6 March 2020 to 31 July 2020, and forecasted the cases from 1–15 August 2020 using various mathematical models—weighted moving average, exponential smoothing, Susceptible-Exposed-Infected-Recovered (SEIR) model, Ornstein-Uhlenbeck process, Autoregressive Integrated Moving Average (ARIMA) model, and random forest. We compared the results to the actual data using traditional error metrics. Our results showed that the ARIMA (1,2,1) model had the closest forecast values to the actual data. Policymakers can use this result in determining which forecast method to use for their community to have data-based information for the preparation of their personnel and facilities.
Keywords: forecasting · epidemics · moving average · exponential smoothing · ARIMA · Ornstein-Uhlenbeck · SEIR · random forest
Copyright (c) 2023 Edd Francis O. Felix, Monica C. Torres, Christian Alvin H. Buhat, Ben Paul B. Dela Cruz, Eleanor B. Gemida, Jonathan B. Mamplata
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