Abstract
The coronavirus disease 2019 (COVID-19) diffusion process, starting in China, caused more than 4600 lives until June 2020 and became a major threat to global public health systems. In Greece, the phenomenon started on February 2020 and it is still being developed. This paper presents the implementation of a hybrid Genetic Programming (hGP) method in finding fitting models of the Coronavirus (COVID 19) for the cumulative confirmed cases in China for the first saturation level until May 2020 and it proposes forecasting models for Greece before summer tourist season. The specific hGP method encapsulates the use of some well-known diffusion models for forecasting purposes, epidemiological models and produces time dependent models with high performance statistical indices. A retrospective study confirmed the excellent forecasting performance of the method until 3 June 2020.
Highlights
The coronavirus disease 2019 (COVID-19) diffusion process, starting in China, caused more than 4600 lives until June 2020 and became a major threat to global public health systems
An SIR (Susceptible - Infected - Recovered) model is an epidemiological model that calculates the number of infected people with a disease and recovered or died by a disease in a specific population over time
The second SIR model describes the epidemic spread over a longer period [7], as endemic phenomenon
Summary
The coronavirus disease 2019 (COVID-19) diffusion process, starting in China, caused more than 4600 lives until June 2020 and became a major threat to global public health systems. In Greece, the phenomenon started on February 2020 and it is still being developed. This paper presents the implementation of a hybrid Genetic Programming (hGP) method in finding fitting models of the Coronavirus (COVID 19) for the cumulative confirmed cases in China for the first saturation level until May 2020 and it proposes forecasting models for Greece before summer tourist season. The specific hGP method encapsulates the use of some wellknown diffusion models for forecasting purposes, epidemiological models and produces time dependent models with high performance statistical indices. A retrospective study confirmed the excellent forecasting performance of the method until 3 June 2020
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