ArticleBMC medical research methodology2022
Forecasting COVID-19 cases using time series modeling and association rule mining.
Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Artificial intelligence research in journals indexed in the web of science "infectious diseases" category: a bibliometric analysis, 2016-2025.Infection · 2026Article
- Enhancing COVID-19 Forecasting Accuracy in Malaysia Using a Hybrid ARIMA-LSTM Model With Exogenous Variables: A Time-Series Predictive Study.Health science reports · 2026Article
- Observational
- Long-term forecast for antibacterial drug consumption in Germany using ARIMA models.Naunyn-Schmiedeberg's archives of pharmacology · 2025Article
- Statistical Analysis and Forecasts of Performance Indicators in the Romanian Healthcare System.Healthcare (Basel, Switzerland) · 2025Article
- Time series analysis and seasonality trends of SARS-CoV-2 in Ecuador (2020-2024): a four-year study.Frontiers in public health · 2025Article
- Article
- Health system resilience during the COVID-19 pandemic: A comparative analysis of disruptions in care from 32 countries.Health services research · 2024Article
- Long-term trend prediction of pandemic combining the compartmental and deep learning models.Scientific reports · 2024Article
- Using Association Rules to Obtain Sets of Prevalent Symptoms throughout the COVID-19 Pandemic: An Analysis of Similarities between Cases of COVID-19 and Unspecified SARS in São Paulo-Brazil.International journal of environmental research and public health · 2024Article
- The Discovery of Oral Cancer Prognostic Factor Ranking Using Association Rule Mining.European journal of dentistry · 2024Article
- A dynamic ensemble model for short-term forecasting in pandemic situations.PLOS global public health · 2024Article
- Spatial distribution of COVID-19 patients in Sri Lanka.BMC public health · 2023Article
- A novel FCTF evaluation and prediction model for food efficacy based on association rule mining.Frontiers in nutrition · 2023Article
- Were metabolic and other chronic diseases the driven onset epidemic forces of COVID-19 in Mexico?Frontiers in public health · 2023Article
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Authors and funding
10 authors.
Funding
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Abstract
backgroundThe 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.
methodsThe 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).
resultsThe 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 (R
conclusionsThis 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.
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