Associations Between Mobility Indices and the COVID-19 Pandemic in Thailand

Abstract

This study aims to examine the associations between the Coronavirus disease 2019 (COVID-19) pandemic and alternative indicators. Specifically, Apple mobility index, Google community mobility index, and Nighttime-light (NTL) data are used for empirical analyses using ordinary least squares (OLS) and panel regressions as research methods. Results produced by OLS models show that Apple’s subcategory of driving activity and Google’s subcategory of visiting transit places are negatively associated with the number of COVID-19 cases. To extend the spatiotemporal details of this analysis, we formulate the panel data by integrating the monthly provincial indicators of Apple mobility index, NTL index, and the COVID-19 infected cases. Both fixed- and random-effects panel regression models indicate that Apple’s driving and walking mobility subcategories are negatively associated with the COVID-19 infected cases. By contrast, the relationship between the NTL index and the intensity of the COVID-19 outbreak is inconclusive. These findings suggest that Apple's mobility index can be applied as an alternative and timely indicator of economic activity, particularly for observing the near real-time intensity of mobility and transportation volume. In addition, these findings can serve as a resource for developing spatial models for urban planning and geographical impacts.