Estimation and Monitoring of COVID-19's Transmissibility From Publicly Available Data
Mathematical modeling, covid-19, Transmission dynamics, epidemic spreading, Pattern recognition, Disease prediction
The 2019 coronavirus disease (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 this 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. 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.
Applied mathematics. Quantitative methods, Probabilities. Mathematical statistics