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

TL;DR

This study leverages mobile phone GPS data and a Brownian bridge model to estimate urban residents' mobility and residence times, integrating these insights into an epidemiological model to evaluate COVID-19 intervention impacts in Hermosillo, Mexico.

Contribution

It introduces a novel application of the Brownian bridge model to infer population mobility from mobile data and integrates this into a disease spread model during the COVID-19 outbreak.

Findings

Mobility estimates align with known intervention periods.

The model effectively captures residence and movement patterns.

Results inform public health strategies during epidemics.

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