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. 2023 Mar:157:113413.
doi: 10.1016/j.jbusres.2022.113413. Epub 2023 Jan 6.

Predicting mobility using limited data during early stages of a pandemic

Michael T Lash  1 , S Sajeesh  2 , Ozgur M Araz  2
Affiliations

Predicting mobility using limited data during early stages of a pandemic

Michael T Lash et al. J Bus Res. 2023 Mar.

Abstract

The COVID-19 pandemic has changed consumer behavior substantially. In this study, we explore the drivers of consumer mobility in several metropolitan areas in the United States under the perceived risks of COVID-19. We capture multiple dimensions of perceived risk using local and national cases and death counts of COVID-19, along with real-time Google Trends data for personal protective equipment (PPE). While Google Trends data are popular inputs in many studies, the risk of multicollinearity escalates with the addition of more relevant terms. Therefore, multicollinearity-alleviating methods are needed to appropriately leverage information provided by Google Trends data. We develop and utilize a novel optimization scheme to induce linear models containing strictly significant covariates and minimal multicollinearity. We find that there are a variety of unique factors that drive mobility in different geographic locations, as well as several factors that are common to all locations.

Keywords: COVID-19; Hill-climbing algorithm; Mobility; Multicollinearity; Retail activity; Risk perception.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
The three states and coinciding cities considered by our study with population density by county indicated with coloring. Blue indicates counties with higher population density and orange lower population density. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2
Fig. 2
A timeline showing several relevant COVID milestones, our study period, and the period in which Google Mobility established baselines from which relative COVID mobility is measured. * Indicates peak cases within the sample period (March 3 - May 29, 2020).
Fig. 3
Fig. 3
Google Mobility data across three cities from March 3rd to May 29th, 2020.
Fig. 4
Fig. 4
Cases and Fatalities by city from March 3rd to May 29th, 2020.
Fig. 5
Fig. 5
Google Trends values by city from March 3rd to May 29th, 2020.
Fig. 6
Fig. 6
Prediction vs actual retail mobility for the testing period May 23rd - May 29th (inclusive) with 95% prediction confidence bounds. Blue dots indicate observed retail mobility values, the cyan line indicates retail mobility predictions, and the red line indicates upper and lower 95% prediction confidence. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 7
Fig. 7
Estimates vs actual grocery and pharmacy mobility for the testing period May 23rd - May 29th (inclusive) with 95% prediction confidence bounds. Blue dots indicate observed grocery and pharmacy mobility values, the cyan line indicates retail mobility predictions, and the red line indicates upper and lower 95% prediction confidence. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
See this image and copyright information in PMC

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