Forecasting COVID-19 cases using time series modeling and association rule mining.

Abstract

BACKGROUND:

The aim of this study was to evaluate the most effective combination of autoregressive integrated moving average (ARIMA), a time series model, and association rule mining (ARM) techniques to identify meaningful prognostic factors and predict the number of cases for efficient COVID-19 crisis management.

METHODS:

The 3685 COVID-19 patients admitted at Thailand's first university field hospital following the four waves of infections from March 2020 to August 2021 were analyzed using the autoregressive integrated moving average (ARIMA), its derivative to exogenous variables (ARIMAX), and association rule mining (ARM).

RESULTS:

The ARIMA (2, 2, 2) model with an optimized parameter set predicted the number of the COVID-19 cases admitted at the hospital with acceptable error scores (R2 = 0.5695, RMSE = 29.7605, MAE = 27.5102). Key features from ARM (symptoms, age, and underlying diseases) were selected to build an ARIMAX (1, 1, 1) model, which yielded better performance in predicting the number of admitted cases (R2 = 0.5695, RMSE = 27.7508, MAE = 23.4642). The association analysis revealed that hospital stays of more than 14 days were related to the healthcare worker patients and the patients presented with underlying diseases. The worsening cases that required referral to the hospital ward were associated with the patients admitted with symptoms, pregnancy, metabolic syndrome, and age greater than 65 years old.

CONCLUSIONS:

This study demonstrated that the ARIMAX model has the potential to predict the number of COVID-19 cases by incorporating the most associated prognostic factors identified by ARM technique to the ARIMA model, which could be used for preparation and optimal management of hospital resources during pandemics.

• Authors: Rachasak Somyanonthanakul, Kritsasith Warin, Watchara Amasiri, Karicha Mairiang, Chatchai Mingmalairak, Wararit Panichkitkosolkul, Krittin Silanun, Thanaruk Theeramunkong, Surapon Nitikraipot, Siriwan Suebnukarn
• Journal: BMC medical research methodology (BMC Med Res Methodol) Vol. 22 Issue 1 Pg. 281 (11 01 2022) ISSN: 1471-2288 [Electronic] England
• PMID: 36316659 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: © 2022. The Author(s).

Topics

• Humans
• Aged
• COVID-19 (epidemiology)
• Time Factors
• Models, Statistical
• Pandemics
• Forecasting
• Data Mining