On mobility trends analysis of COVID-19 dissemination in Mexico City

Abstract

This work presents a forecast of the spread of the new coronavirus in Mexico City based on a mathematical model with metapopulation structure by using Bayesian Statistics inspired in a data-driven approach. The mobility of humans on a daily basis in Mexico City is mathematically represented by a origin-destination matrix using the open mobility data from Google and a Transportation Mexican Survey. This matrix, is incorporated in a compartmental model. We calibrate the model against borough-level incidence data collected between February 27, 2020 and October 27, 2020 using Bayesian inference to estimate critical epidemiological characteristics associated with the coronavirus spread. Since working with metapopulation models lead to rather high computational time consume, we do a clustering analysis based on mobility trends in order to work on these clusters of borough separately instead of taken all the boroughs together at once. This clustering analysis could be implemented in smaller or lager scale in different part of the world. In addition, this clustering analysis is divided in the phases that the government of Mexico City has set up to restrict the individuals movement in the city. Also, we calculate the reproductive number in Mexico City using the next generation operator method and the inferred model parameters. The analysis of mobility trends can be helpful in public health decisions.

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

“…I show trace plots, credible intervals, bands projections with medians and a MAP curve (for the t-walk case) and the joint crosstab The model has many implicit assumptions which may be incorrect, e.g., it assumes that the transmission rate is constant and homogeneous through the whole country, which is by far incorrect [34], that is, we can certainly say that every region state in Mexico has its own pandemic, and it is not true that mobility from the North to the South in Mexico is the same as in a specific state of Mexico. A better projection for Mexico City, which has a considerable percentage of coronavirus in the whole country can be found in [69]. Also, the model does not take into account the government interventions, which in each state were announced by a color of the traffic light, red meaning almost all the activities had to be suspended, yellow, some of the activities could reactivate, and green, a considerable percentage of activities could reactivate, depending of each state government.…”

Comorbidity Analysis: Overlapping Semicircles With Wigner Law and Random Matrix Theory

“…In [20] in 2021 they found a high incidence of comorbidities in deaths that occurred up to August 2020. In and another analysis, [21] in 2021 give predictions on the spread of the pandemic using Bayesian inference. However, as far as we know, there is no study that uses our methodology that has analyzed the Mexican case.…”