Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models

Author:

Sciannameo Veronica12ORCID,Azzolina Danila13ORCID,Lanera Corrado1ORCID,Acar Aslihan Şentürk4ORCID,Corciulo Maria Assunta1,Comoretto Rosanna Irene15ORCID,Berchialla Paola2ORCID,Gregori Dario1ORCID

Affiliation:

1. Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, 35131 Padova, Italy

2. Center of Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torino, 10124 Turin, Italy

3. Department of Environmental and Preventive Sciences, University of Ferrara, 44121 Ferrara, Italy

4. Department of Actuarial Sciences, Hacettepe University, 06230 Ankara, Turkey

5. Department of Public Health and Pediatrics, University of Torino, 10124 Turin, Italy

Abstract

The COVID-19 outbreak involved a spread of prediction efforts, especially in the early pandemic phase. A better understanding of the epidemiological implications of the different models seems crucial for tailoring prevention policies. This study aims to explore the concordance and discrepancies in outbreak prediction produced by models implemented and used in the first wave of the epidemic. To evaluate the performance of the model, an analysis was carried out on Italian pandemic data from February 24, 2020. The epidemic models were fitted to data collected at 20, 30, 40, 50, 60, 70, 80, 90, and 98 days (the entire time series). At each time step, we made predictions until May 31, 2020. The Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) were calculated. The GAM model is the most suitable parameterization for predicting the number of new cases; exponential or Poisson models help predict the cumulative number of cases. When the goal is to predict the epidemic peak, GAM, ARIMA, or Bayesian models are preferable. However, the prediction of the pandemic peak could be made carefully during the early stages of the epidemic because the forecast is affected by high uncertainty and may very likely produce the wrong results.

Publisher

MDPI AG

Subject

Health Information Management,Health Informatics,Health Policy,Leadership and Management

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