Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches.

Kernel Prieto,Simone Lolli

doi:10.1371/journal.pone.0259958

Abstract

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.

Highlights

Several mathematical models for disease transmission, and to predict and control disease spread have been proposed because emerging and re-emerging infectious diseases represent a major threat to public health, and may cause large economic and social losses
A considerable effort has been made to study the impact of control measures to eradicate the outbreak of an epidemic, and in particular an immediate response for a possible influenza pandemic crisis [4]
I aimed to develop models of COVID-19 using Machine Learning to accurately predict both hospital care demand and mortality based on patients who present diseases such as hypertension, obesity, diabetes and smoking

Summary

Introduction

Several mathematical models for disease transmission, and to predict and control disease spread have been proposed because emerging and re-emerging infectious diseases represent a major threat to public health, and may cause large economic and social losses. Mathematical models include compartmental epidemic models, which are deterministic systems of ordinary and partial differential equations or stochastic difference equations [5] For diseases such as influenza, typhoid fever, anthrax, diphtheria, tetanus, cholera, hepatitis B, pertussis, pneumonia, and coronavirus, the process of transmission between individuals takes. I aimed to develop models of COVID-19 using Machine Learning to accurately predict both hospital care demand and mortality based on patients who present diseases such as hypertension, obesity, diabetes and smoking. A discussion and some conclusions are presented in the last section 5

Model formulation

Parameter estimation

Clinical analysis with machine learning

Findings

Discussion and conclusions