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

Abstract

This work presents a tool for forecasting the spread of the new coronavirus in Mexico City, which is based on a mathematical model with a metapopulation structure that uses Bayesian statistics and is inspired by a data-driven approach. The daily mobility of people in Mexico City is mathematically represented by an origin-destination matrix using the open mobility data from Google and the Transportation Mexican Survey. This matrix is incorporated in a compartmental model. We calibrate the model against borough-level incidence data collected between 27 February 2020 and 27 October 2020, while using Bayesian inference to estimate critical epidemiological characteristics associated with the coronavirus spread. 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 borough separately instead of taken all of the boroughs together at once. This clustering analysis can be implemented in smaller or larger scales in different parts of the world. In addition, this clustering analysis is divided into the phases that the government of Mexico City has set up to restrict individual movement in the city. We also calculate the reproductive number in Mexico City using the next generation operator method and the inferred model parameters obtaining that this threshold is in the interval (1.2713, 1.3054). Our analysis of mobility trends can be helpful when making public health decisions.

Conditioning factors in the spreading of Covid-19 – Does geography matter?

“…At a global level, a positive correlation was found between human mobility and COVID-19 infections [ 45 , 92 , 75 , 76 , 139 ]. At a national scale, several studies found that mobility reduction generally implies a restrain on infections [ 59 , 140 , 141 ]. A positive correlation between mobility and excess mortality was also found in Italy [ 23 ] and Ecuador [ 142 ].…”

Two-Step Cluster Analysis of Passenger Mobility Segmentation during the COVID-19 Pandemic

“…Due to the temporary stoppage of economic and social aspects, businesses were forced to change their mindset and accelerate the onset of digitization. The active introduction of online applications allowed businesses to continue their economic activity continuously during the pandemic situation [64][65][66]. A study in Turkey investigated the impact of the pandemic on mobility and confirmed the importance of some types of mobility, such as regarding grocery stores or workplaces [67] Prediction of COVID-19 severity was evaluated in another study using longwise cluster analysis, which was evaluated as an effective way of redistributing patients based on different clinics [68].…”

Inference on spatiotemporal dynamics for coupled biological populations

“…The recent growth in the study of metapopulation dynamics has been driven partly by the COVID-19 pandemic [9][10][11][12][13][14][15][16][17] and in part by methodological advances facilitating the fitting of metapopulation models to spatiotemporal data. Until the start of this millennium, developing dynamic models with both statistical and scientific justification was a long-standing open problem for even a single population [18].…”