ArticleBMC research notes2019
Comparative evaluation of time series models for predicting influenza outbreaks: application of influenza-like illness data from sentinel sites of healthcare centers in Iran.
Article in BMC research notes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Interpretable early warnings using machine learning in an online game-experiment.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Comparative estimation of the spread of acute diarrhea and dengue in India using statistical mathematical and deep learning models.Scientific reports · 2025Article
- Patients' attitude and information-seeking behaviour on intra-venous fluid therapy in emergency department for common cold: a cross-sectional study.International journal of emergency medicine · 2025Article
- Exploring health seeking behaviors for common cold management.Exploratory research in clinical and social pharmacy · 2023Article
- Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology.Complex & intelligent systems · 2023Article
- Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: a real-case study.Medical & biological engineering & computing · 2022Article
- Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States.Healthcare (Basel, Switzerland) · 2022Article
- Advances in the Rapid Diagnostic of Viral Respiratory Tract Infections.Frontiers in cellular and infection microbiology · 2022Review
- Estimating COVID-19Machine learning with applications · 2021Article
- The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review.Heliyon · 2021Review
- A systematic review on AI/ML approaches against COVID-19 outbreak.Complex & intelligent systems · 2021Article
- Enhanced Gaussian process regression-based forecasting model for COVID-19 outbreak and significance of IoT for its detection.Applied intelligence (Dordrecht, Netherlands) · 2021Article
- Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA).Chaos, solitons, and fractals · 2020Article
- Prediction of daily COVID-19 cases in European countries using automatic ARIMA model.Journal of public health research · 2020Article
- Tailoring time series models for forecasting coronavirus spread: Case studies of 187 countries.Computational and structural biotechnology journal · 2020Article
- Forecasting the monthly incidence rate of brucellosis in west of Iran using time series and data mining from 2010 to 2019.PloS one · 2020Article
- Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread.Frontiers in public healthReview
- COVID-19: Forecasting confirmed cases and deaths with a simple time series model.International journal of forecastingArticle
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveForecasting the time of future outbreaks would minimize the impact of diseases by taking preventive steps including public health messaging and raising awareness of clinicians for timely treatment and diagnosis. The present study investigated the accuracy of support vector machine, artificial neural-network, and random-forest time series models in influenza like illness (ILI) modeling and outbreaks detection. The models were applied to a data set of weekly ILI frequencies in Iran. The root mean square errors (RMSE), mean absolute errors (MAE), and intra-class correlation coefficient (ICC) statistics were employed as evaluation criteria.
resultsIt was indicated that the random-forest time series model outperformed other three methods in modeling weekly ILI frequencies (RMSE = 22.78, MAE = 14.99 and ICC = 0.88 for the test set). In addition neural-network was better in outbreaks detection with total accuracy of 0.889 for the test set. The results showed that the used time series models had promising performances suggesting they could be effectively applied for predicting weekly ILI frequencies and outbreaks.
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