ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2022
Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy.
Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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43 citing papers in PubMed.
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- Modeling the seasonal epidemic of human brucellosis in China: A comparative time series analysis.PloS one · 2026Article
- Forecasting Under-5 Mortality Rate in Somalia to 2030: a comparative analysis of univariate and multivariate ARIMAX models.Frontiers in public health · 2026Article
- Global burden of metabolic dysfunction-associated steatotic liver disease from 1990 to 2021 and the prediction for the next 10 years.Preventive medicine reports · 2025Article
- Multiple change point analysis and time series forecast of brucellosis reports in Xinjiang, China from 2010 to 2024.BMC public health · 2025Article
- A systematic methodology to capture the global pattern of rheumatic heart disease: the Rheumatic Heart Disease Endemicity Index (RHDEI).BMC global and public health · 2025Article
- Navigating Samarinda's climate: A comparative analysis of rainfall forecasting models.MethodsX · 2025Article
- Modeling and Forecasting Dead-on-Arrival in Broilers Using Time Series Methods: A Case Study from Thailand.Animals : an open access journal from MDPI · 2025Article
- Exploring different modelling approaches to forecast the community acute respiratory infections burden in children: an Italian epidemiological time series study.BMC public health · 2025Article
- Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan.BMC public health · 2025Article
- Formulating and evaluating time series algorithms to forecast daily asthma hospital admissions.Journal of clinical and translational science · 2025Article
- Global and regional trends in under-five diarrheal disease burden: impact of human development index and geographic disparities, 1990-2021.Gastroenterology and hepatology from bed to bench · 2025Article
- Spatial Distribution Analysis and Comparative Forecasting of Dengue Resurgence in the Philippines (2025-2027): A Nationwide Study.Transboundary and emerging diseases · 2025Article
- Time-series analysis for forecasting monthly workload at two elephant hospitals in Thailand.PloS one · 2025Article
- Trends and multi-model prediction of hepatitis B incidence in Xiamen.Infectious Disease Modelling · 2024Article
- Development of an approach to forecast future takeaway outlet growth around schools and population exposure to takeaways in England.International journal of health geographics · 2024Article
- Impact of COVID-19 Interventions on Respiratory and Intestinal Infectious Disease Notifications - Jiangsu Province, China, 2020-2023.China CDC weekly · 2024Article
- Seasonality of influenza-like illness and short-term forecasting model in Chongqing from 2010 to 2022.BMC infectious diseases · 2024Article
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Abstract
The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic's second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box-Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020-October 13, 2020 were extracted from the website of the Italian Ministry of Health ( www.salute.gov.it ). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities' decision-making, especially in the short-term.
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