Use of mobile phone sensing data to estimate residence and mobility times in urban patches during the COVID-19 epidemic: The case of the 2020 outbreak in Hermosillo, Mexico

Abstract

It is often necessary to introduce the main characteristics of population mobility dynamics to model critical social phenomena such as the economy, violence, transmission of information, or infectious diseases. In this work, we focus on modeling and inferring urban population mobility using the geospatial data of its inhabitants. The objective is to estimate mobility and times inhabitants spend in the areas of interest, such as zip codes and census geographical areas. The proposed method uses the Brownian bridge model for animal movement in ecology. We illustrate its possible applications using mobile phone GPS data in 2020 from the city of Hermosillo, Sonora, in Mexico. We incorporate the estimated residence-mobility matrix into a multi-patch compartmental SEIR model to assess the effect of mobility changes due to governmental interventions.

Multi-patch epidemic models with partial mobility, residency, and demography

“…As an application to the real world situation, the residence matrix P and the mobility parameter α for the AGEBs in Hermosillo, Sonora Mexico were estimated using the Brownian bridge technique. We refer the reader to the work by [43] in order to have an idea of how this estimation was carried out. The total number of AGEBs (which we consider as patches in this analysis) for which these parameters were estimated were n = 503.…”

Inference on a Multi-Patched Epidemic Model with Partial Mobility, Residency and Demography: The Case of 2020 COVID-19 Outbreak in Hermosillo, Mexico

“…Human mobility, being a geo-spatio-temporal phenomena [17,43,69,70], can be modelled by meta-population-multi-patched models [4,34,39,40,42,44], as traditional homogeneous compartmental models are incapable of capturing such a strong heterogeneous human behaviour [15,17,34,51]. Once such models have been constructed, a wide spectrum of quantitative analysis that leads to deep understanding of the reciprocity of mobility and the spread of infectious diseases can be conducted.…”

“…In 2020, the University of Sonora signed an agreement with the government of the State of Sonora that provided the geo-referenced COVID-19 cases in Hermosillo, Mexico, from 2020-01-01 to 2020-09-06. On the other hand, using mobile phone GPS data from 2020-09-21 to 2020-11-15, [4] estimated mobility parameters and residence times in Hermosillo's 582 urban AGEBs (Basic Census Geographical Units). As we are interested in modelling the number of cases in four important zones in Hermosillo (each aggregating AGEBs as shown in Figure 1) we use the geo-referenced and GPS information to obtain the number of COVID cases and estimate the mobility and residence within and between zones.…”

“…Moreover, simultaneous estimation of several parameters oftentimes leads to parameter non-identifiability problems [103][104][105][106][107][108][109]. As such, we use the mobile phone sensing data used in [4] for purposes of estimating the mobility vector α i , i = 1, 2, • • • , n and the mobility residence time matrices…”

“…The mathematical modeling of infectious diseases has been a continuously growing research area over the past few decades. This is due to the significant impact that infectious diseases can have on a wide range of important areas, including social, health, and economic development, whose main driving factor is human population [1][2][3][4][5]. Indeed, works such as [6] and [7], raised awareness on the fact that the emergence and re-emergence of infectious diseases had historically been neglected as a significant threat to public health.…”