Abstract
The COVID-19 pandemic began in the city of Wuhan, China, at the end of 2019 and quickly spread worldwide. The disease is caused by contact with the SARS-CoV-2 virus, which probably jumped from an animal host to humans. SARS-CoV-2 infects various tissues in the body, notably the lungs, and patients usually die from respiratory complications. Mathematical models of the disease have been instrumental to guide the implementation of mitigation strategies aimed at slowing the spread of the disease. One of the key parameters of mathematical models is the basic reproduction ratio R0, which measures the degree of infectivity of affected individuals. The goal of mitigation is to reduce R0 as close or below 1 as possible, as it means that new infections are in decline. In the present work, we use the recursive least-squares algorithm to establish the stochastic variability of a time-varying R0(t) from eight different countries: Argentina, Belgium, Brazil, Germany, Italy, New Zealand, Spain, and the United States of America. The proposed system can be implemented as an online tracking application providing information about the dynamics of the pandemic to health officials and the public at large.
Highlights
On March 11, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) a global pandemic [1]
We model the transmission dynamic of COVID-19 in eight countries (Figure 1) using least-squares algorithm (LSA) techniques
Estimations of the transmission ratio produce a dynamic representation from the perspective of the time series of R 0(t) [ designated as R 0(t)], which allows the modeling of its dynamics and its randomness in order to assess stochastic properties correlated to the time-varying reproduction number, which might reflect how health authorities have been handling the challenges posed by the pandemics in each country considered in this work
Summary
On March 11, 2020, the World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) a global pandemic [1]. The LSA is one of the most popular estimation methods in machine learning and has been used in many scientific and engineering applications [30,31,32], including epidemiology, for calibrating mathematical models’ parameters based on time series data while generating disease forecasts in the near or long terms [14, 33,34,35] While it has been used for centuries as a classic curve-fitting technique [31, 32], it is still a basic tool in modern data science because of its least-squares Euclidean l2-norm minimization that is advantageous over other norms and metrics, such as the l∞ and l1 norms, granting reduced sensibility to outliers due to the squared error [32, 36]. We combine data from the number of susceptibles, infections, recoveries, deaths, and individual parameters of three coupled differential equations in order to improve the estimation of R0(t)
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