ArticleScientific reports2022
Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 43 citations in OpenAlex.
- A novel time-series modeling framework for predicting tuberculosis incidence in Sichuan, China.BMC infectious diseases · 2026Article
- Reflections on predictive modeling for infectious diseases.Frontiers in public health · 2026Article
- Model-free prognostication of non-linear time series.PloS one · 2026Article
- Navigating Samarinda's climate: A comparative analysis of rainfall forecasting models.MethodsX · 2025Article
- Global trends and health workforce analysis of breast cancer burden from high red meat consumption 1990-2050 using machine learning approach.Frontiers in nutrition · 2025Article
- Spatial and temporal analysis and forecasting of TB reported incidence in western China.BMC public health · 2024Article
- Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.Scientific reports · 2024Article
- The burden of cardiovascular disease in Asia from 2025 to 2050: a forecast analysis for East Asia, South Asia, South-East Asia, Central Asia, and high-income Asia Pacific regions.The Lancet regional health. Western Pacific · 2024Article
- Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend.BMC infectious diseases · 2024Article
- Mushroom poisoning outbreaks in Guizhou Province, China: a prediction study using SARIMA and Prophet models.Scientific reports · 2023Article
- Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images.Interdisciplinary perspectives on infectious diseases · 2022Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Globally, since the outbreak of the Omicron variant in November 2021, the number of confirmed cases of COVID-19 has continued to increase, posing a tremendous challenge to the prevention and control of this infectious disease in many countries. The global daily confirmed cases of COVID-19 between November 1, 2021, and February 17, 2022, were used as a database for modeling, and the ARIMA, MLR, and Prophet models were developed and compared. The prediction performance was evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The study showed that ARIMA (7, 1, 0) was the optimum model, and the MAE, MAPE, and RMSE values were lower than those of the MLR and Prophet models in terms of fitting performance and forecasting performance. The ARIMA model had superior prediction performance compared to the MLR and Prophet models. In real-world research, an appropriate prediction model should be selected based on the characteristics of the data and the sample size, which is essential for obtaining more accurate predictions of infectious disease incidence.
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