The COVID-19 pandemic has been widely spread and affected millions of people and caused hundreds of deaths worldwide, especially in patients with comorbilities and COVID-19. This manuscript aims to present models to predict, firstly, the number of coronavirus cases and secondly, the hospital care demand and mortality based on COVID-19 patients who have been diagnosed with other diseases. For the first part, I present a projection of the spread of coronavirus in Mexico, which is based on a contact tracing model using Bayesian inference. I investigate the health profile of individuals diagnosed with coronavirus to predict their type of patient care (inpatient or outpatient) and survival. Specifically, I analyze the comorbidity associated with coronavirus using Machine Learning. I have implemented two classifiers: I use the first classifier to predict the type of care procedure that a person diagnosed with coronavirus presenting chronic diseases will obtain (i.e. outpatient or hospitalised), in this way I estimate the hospital care demand; I use the second classifier to predict the survival or mortality of the patient (i.e. survived or deceased). I present two techniques to deal with these kinds of unbalanced datasets related to outpatient/hospitalised and survived/deceased cases (which occur in general for these types of coronavirus datasets) to obtain a better performance for the classification.
【초록키워드】 COVID-19, coronavirus, Mortality, COVID-19 pandemic, machine learning, hospital, Comorbidity, Contact tracing, chronic disease, Spread, Health, survival, Patient, death, Bayesian inference, dataset, Care, predict, Deceased, COVID-19 patient, Patient care, Classifier, other diseases, individual, manuscript, datasets, hospitalised, affected, caused, the patient, diagnosed, occur, presenting, survived, 【제목키워드】 COVID-19, Bayesian, current, machine learning approach,