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. 2020 Jul:136:109924.
doi: 10.1016/j.chaos.2020.109924. Epub 2020 May 22.

Forecast and evaluation of COVID-19 spreading in USA with reduced-space Gaussian process regression

Affiliations

Forecast and evaluation of COVID-19 spreading in USA with reduced-space Gaussian process regression

Ricardo Manuel Arias Velásquez et al. Chaos Solitons Fractals. 2020 Jul.

Abstract

In this report, we analyze historical and forecast infections for COVID-19 death based on Reduced-Space Gaussian Process Regression associated to chaotic Dynamical Systems with information obtained in 82 days with continuous learning, day by day, from January 21 th , 2020 to April 12 th . According last results, COVID-19 could be predicted with Gaussian models mean-field models can be meaning- fully used to gather a quantitative picture of the epidemic spreading, with infections, fatality and recovery rate. The forecast places the peak in USA around July 14 th 2020, with a peak number of 132,074 death with infected individuals of about 1,157,796 and a number of deaths at the end of the epidemics of about 132,800. Late on January, USA confirmed the first patient with COVID-19, who had recently traveled to China, however, an evaluation of states in USA have demonstrated a fatality rate in China (4%) is lower than New York (4.56%), but lower than Michigan (5.69%). Mean estimates and uncertainty bounds for both USA and his cities and other provinces have increased in the last three months, with focus on New York, New Jersey, Michigan, California, Massachusetts, ... (January e April 12 th ). Besides, we propose a Reduced-Space Gaussian Process Regression model predicts that the epidemic will reach saturation in USA on July 2020. Our findings suggest, new quarantine actions with more restrictions for containment strategies implemented in USA could be successfully, but in a late period, it could generate critical rate infections and death for the next 2 month.

Keywords: COVID-19; Forecast; Gaussian; USA.

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Conflict of interest statement

There is no conflict of interest in this work.

Figures

Fig. 1
Fig. 1
Distribution infections for USA.
Fig. 2
Fig. 2
Active cases variation on percent associated from March to April 2020.
Fig. 3
Fig. 3
Methodology diagram.
Fig. 4
Fig. 4
Fatality rate (%) in function of confirmed patients for COVID-19 in USA.
Fig. 5
Fig. 5
Recovery rate (%) in function of total recovered patients for COVID-19 in USA.
Fig. 6
Fig. 6
Forecast for contagion, death and recovery associated to COVID-19 in USA.
Fig. 7
Fig. 7
Forecast contagion, active,death, recovered until last estimate active patients.
Fig. 8
Fig. 8
Reported cases by date of onset (blue points) and estimated USA cases from each state by date of onset (Yellow line) for new contagion, deaths and recovered patients. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 9
Fig. 9
a) Dynamic representation, b) historical information c) forecast and evaluation in USA, Spain and Italy.

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