Mobility and COVID-19 in Andorra: Country-scale analysis of high-resolution mobility patterns and infection spread

Abstract

In the absence of effective vaccines, non-pharmaceutical interventions, such as mobility restrictions, were globally adopted as critically important strategies for curbing the spread of COVID-19. However, such interventions come with immense social and economic costs and the relative effectiveness of different mobility restrictions are not well understood. This study analyzed uniquely comprehensive datasets for the entirety of a small country, consisting of serology data, telecoms data, and COVID-19 case reports, in order to examine the relationship between mobility and transmission of COVID-19. Andorra is a small European country where tourism is a large part of the economy. Stringent mobility restrictions were put in place in Spring 2020. Additionally, 91% of the population participated in a voluntary COVID-19 serology testing programme and those data were made available for this study. Furthermore, high resolution telecoms data for the entire population were available for analysis of mobility and proximity patterns. A set of mobility metrics were developed to indicate levels of crowding, stay-at-home rates, trip-making and contact with tourists. Mobility metrics were compared to infection rates across communities and transmission rate over time. Several of these metrics were highly correlated with transmission rate, with a lead time of approximately 18 days, with some metrics more highly correlated than others. There was a stronger correlation for measures of crowding and inter-community trip-making, and a weaker correlation for total trips (including intra-community trips) and stay-at-homes rates.

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 [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.…”