Forecasting Models of the Coronavirus (COVID-19) Cumulative Confirmed Cases Using a Hybrid Genetic Programming Method

Konstantinos Salpasaranis, Vasilios Stylianakis
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引用次数: 1

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.
基于混合遗传规划的冠状病毒(COVID-19)累计确诊病例预测模型
从中国开始的2019冠状病毒病(COVID-19)传播过程,截至2020年6月,已造成4600多人死亡,成为全球公共卫生系统的重大威胁。在希腊,这种现象始于2020年2月,目前仍在发展中。本文提出了一种混合遗传规划(hGP)方法,用于寻找冠状病毒(COVID - 19)的拟合模型,该模型适用于中国到2020年5月的第一个饱和水平的累积确诊病例,并提出了夏季旅游季节之前希腊的预测模型。特定的hGP方法封装了用于预测目的的一些著名的扩散模型,流行病学模型的使用,并产生具有高性能统计指标的时间依赖模型。一项回顾性研究证实了该方法在2020年6月3日前的出色预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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