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.
Highlights
The coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus
Given the pandemic situation that we are experiencing today and that we have current available data about the increase or decrease in mobility for some modes of transport, transit stations and parking lots, we consider the 2017 O-D matrix as a reference matrix and we update it to a scenario in 2020 using the daily mobility reports provided by Google [25] and the government of Mexico City [26]
We present an explanation of how to estimate the mobility per day between the boroughs that compound the Mexico City, both on a weekday and on weekends; combining available information from the origin-destination survey carried out in 2017 with the current mobility indices that Google and the government of Mexico City report, depending on the mode of transport used to make each trip and we use the Fratar method to balance the daily origindestination matrices
Summary
The coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus. The coronaviruses are a family of viruses that cause infection in humans and animals. To incorporate mobility in the transmission model, the produced and attracted trips in the boroughs of Mexico City are considered (see Fig 1 and Table 1). Given the pandemic situation that we are experiencing today and that we have current available data about the increase or decrease in mobility for some modes of transport, transit stations and parking lots, we consider the 2017 O-D matrix as a reference matrix and we update it to a scenario in 2020 using the daily mobility reports provided by Google [25] and the government of Mexico City [26].
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