EGU21-2742, updated on 03 Mar 2021
https://doi.org/10.5194/egusphere-egu21-2742
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

On the uncertainty of real-time predictions of epidemic growths: A COVID-19 case study for China and Italy

Tommaso Alberti1 and Davide Faranda2
Tommaso Alberti and Davide Faranda
  • 1Istituto Nazionale di Astrofisica, Istituto di Astrofisica e Planetologia Spaziali, Roma, Italy (tommasoalberti89@gmail.com)
  • 2Laboratoire des Sciences du Climat et de l’Environnement, 5 CEA Saclay l’Orme des Merisiers, UMR 8212 CEA-CNRS-UVSQ, 6 Université Paris-Saclay & IPSL, Gif-sur-Yvette 91191, France

While COVID-19 is rapidly propagating around the globe, the need for providing real-time forecasts of the epidemics pushes fits of dynamical and statistical models to available data beyond their capabilities. Here we focus on statistical predictions of COVID-19 infections performed by fitting asymptotic distributions to actual data. By taking as a case-study the epidemic evolution of total COVID-19 infections in Chinese provinces and Italian regions, we find that predictions are characterized by large uncertainties at the early stages of the epidemic growth. Those uncertainties significantly reduce after the epidemics peak is reached. Differences in the uncertainty of the forecasts at a regional level can be used to highlight the delay in the spread of the virus. Our results warn that long term extrapolation of epidemics counts must be handled with extreme care as they crucially depend not only on the quality of data, but also on the stage of the epidemics, due to the intrinsically non-linear nature of the underlying dynamics. These results suggest that real-time epidemiological projections should include wide uncertainty ranges and urge for the needs of compiling high-quality datasets of infections counts, including asymptomatic patients.

Alberti T. and Faranda D. (2020) On the uncertainty of real-time predictions of epidemic growths: A COVID-19 case study for China and Italy. Commun. Nonlin. Sci. Num. Sim., 90, 105372.

How to cite: Alberti, T. and Faranda, D.: On the uncertainty of real-time predictions of epidemic growths: A COVID-19 case study for China and Italy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2742, https://doi.org/10.5194/egusphere-egu21-2742, 2021.

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