Analysis of mobility data to build contact networks for COVID-19

Abstract

As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission.

Understanding travel behavior adjustment under COVID-19

“…In the post-COVID-19 era, the conventional transportation system and shared bicycle systems have recovered faster than the public transportation system. At present, there have been a number of studies that constructed models of a relationship between the transportation system and the spread of the pandemic, which simulate the process of issuing the traffic control measures and quantitatively evaluate the effect of the measures on the further spreading of the pandemic ( Klise et al, 2021 ; O'Sullivan et al, 2020 ; Munshi et al, 2020 ; Anzai et al, 2020 ; Chang et al, 2021 ). For instance, Chang et al (2021) established a resident travel-SEIR model using the SafeGraph data, US Census data, and the information on the number of confirmed cases and deaths published in the New York Times.…”

Variability in Opioid-Related Drug Overdoses, Social Distancing, and Area-Level Deprivation during the COVID-19 Pandemic: a Bayesian Spatiotemporal Analysis

“…To estimate the number of people who stay at home each day, the number of mobile devices that were completely at home was divided by the total number of devices in each CBG (i.e., completely home device count divided by the device count) [32]. The data collected by SafeGraph does not capture devices that are out of service, not moving, lack a tracking app, have opted out of location services and/or are not linked to a home CBG [34]. However, although the data only includes a subset of the total population, the data has been subjected to an exhaustive 6-step process designed to guarantee its reliability, accuracy, and external validity (see, e.g., [35][36][37][38][39]).…”

Spatial Optimization to Improve COVID-19 Vaccine Allocation

“…[11][12][13][14] Additional details regarding mobility data collection have been published elsewhere. 18,19 Number of COVID-19 related deaths and hospital costs related to COVID-19 were estimated for use in analysis. These estimates were calculated by using the number of new COVID-19 cases along with the average national COVID-19 case-fatality-rate, average national rate of hospitalizations due to COVID-19, and hospital treatment costs of COVID-19 at time of respective observations.…”