AI4Pandemics Talk: Kernel Enrique Prieto Moreno, Facultad de Ciencias, UNAM Cancelled

Title: Forecast of COVID-19 dissemination in Mexico: a Bayesian and Machine Learning approaches

Zoom: 

Abstract: The COVID-19 pandemic has been widely spread, affected and caused the death of millions of people worldwide, especially in patients with associated comorbidities. In this talk, firstly, I present a projection of the spread of coronavirus in Mexico, which is based on a com-
partmental contact tracing model using Bayesian inference. Secondly, I present a projection of the hospital care demand and mortality of COVID-19 patients based on their health profile.

Specifically, I analyze the comorbidity associated with coronavirus using Machine Learning. I have implemented a classifier to predict the type of care procedure (outpatient/hospitalized)that a person diagnosed with coronavirus presenting chronic diseases will need, in this way I estimate the hospital care demand; next, I implement a second classifier to predict the survival/mortality of the patient. I present two techniques to deal with these kinds of imbalanced datasets related to outpatient/hospitalized and survived/deceased cases which occur in general for these types of diseases to obtain a better performance for the classification.
Finally, I present a metapopulation model to forecast the spread of the new coronavirus in Mexico City, using Bayesian inference. The daily mobility of people in Mexico City is mathematically represented by an origin-destination (O-D) matrix using a combination of three sources: the Mexico City government, an INEGI Transportation Mexican Survey, and COVID-19 Google Data Community Reports. Next, this O-D matrix is incorporated in a compartmental model. Given that working with metapopulation models leads to rather high computational time consumption, and parameter estimation of these models may lead to high memory RAM consumption, we do a clustering analysis that is based on mobility trends to work on these clusters of boroughs separately instead of taking all of them together at once.Our analysis of mobility trends can be helpful when making public health decisions.