Mobility and COVID-19 in Andorra: Country-Scale Analysis of High-Resolution Mobility Patterns and Infection Spread

Abstract

Throughout the COVID-19 pandemic, nonpharmaceutical interventions, such as mobility restrictions, have been globally adopted as critically important strategies to curb the spread of infection. However, such interventions come with immense social and economic costs and the relative effectiveness of different mobility restrictions are not well understood. Some recent works have used telecoms data sources that cover fractions of a population to understand behavioral changes and how these changes have impacted case growth. This study analyzed uniquely comprehensive datasets in order to examine the relationship between mobility and transmission of COVID-19 in the country of Andorra. The data consisted of spatiotemporal telecoms data for all mobile subscribers in the country, serology screening results for 91% of the population, and COVID-19 case reports. A comprehensive set of mobility metrics was developed using the telecoms data to indicate entrances to the country, contact with tourists, stay-at-home rates, trip-making and levels of crowding. Mobility metrics were compared to infection rates across communities and transmission rate over time. All metrics dropped sharply at the start of the country's lockdown and gradually rose again as the restrictions were gradually lifted. Several of these metrics were highly correlated with lagged transmission rate. There was a stronger correlation for measures of indoor crowding and inter-community tripmaking, and a weaker correlation for total trips (including intra-community trips) and stay-at-homes rates. These findings provide support for policies which aim to discourage gathering indoors while lifting the most restrictive mobility limitations.

Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra

“…Combined with σ −1 = 5.2, d = 7 results in a total estimated average delay from exposure to case report ∼17.7 days. This is consistent with previous work over a similar study period in Andorra that studied the correlation between mobility metrics and transmission rates over various lags and found the best correlations were with mobility metrics lagged by 18 days [27].…”

“…The time series of reported COVID-19 cases is shown with the time series of the trips and entrances metrics in Fig 1. Other studies have implied that changes in case growth often lag changes in behavior and mobility metrics by 14 or more days [16, 19, 23].…”

“…This work presents a unique case study in Andorra, with comprehensive datasets that include telecoms data covering 100% of mobile subscribers in the country, and results from a serology testing program that more than 90% of the population voluntarily participated in. Previous work used these data sources to compare various mobility metrics and infection rates with retrospective correlation analysis [23]. This work builds upon these previous findings and develops compartmental epidemic models.…”

Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra

“…The time series of reported COVID-19 cases is shown with the time series of the trips and entrances metrics in Fig 1 . Other studies have implied that changes in case growth often lag changes in behavior and mobility metrics by 14 or more days [ 17 , 21 , 27 ]. However, Fig 1 shows that daily trips were able to increase throughout May of 2020 while newly reported cases remained low.…”

“…This work presents a unique case study in Andorra, with comprehensive datasets that include telecoms data covering 100% of mobile subscribers in the country, and results from a serology testing program that more than 90% of the population voluntarily participated in. Previous work used these data sources to compare various mobility metrics and infection rates with retrospective correlation analysis [ 27 ]. This work builds upon these previous findings and develops compartmental epidemic models.…”