Predicting Covid-19 Trajectory Using Machine Learning

Abstract

The pandemic caused by COVID-19 in 2020 triggered a devastating effect on the economy and health of the world population, whose social implications for the next few years are still uncertain. Two types of standard tests are used to detect COVID-19: the viral test that indicates whether the patient is infected and the antibody test that allows us to observe if the patient has previously had an infection. These tests employ techniques such as reverse transcription and polymerase chain reaction (RT-PCR), immunochromatographic lateral flow or rapid test, and ELISA-type immunoassay In this paper we have designed and implemented a system whose main purpose is to detect the rise of Covid-19 cases using disruptive technologies such as artificial intelligence and intelligent computing, manifested through machine learning (Machine Learning) and deep learning (Deep Learning). Combined with data science, Big Data and advanced data analytics, among others that present various research and development options, it can help the early detection of COVID-19 through the search for relevant characteristics that allow the scientific community identify biochemical, molecular and cellular factors that facilitate the early detection of the virus in its different states of infection, incubation, propagation and treatments to be used