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

Compartmental models are often used to understand and predict the progression of an infectious disease such as COVID-19. The most basic of these models consider the total population of a region to be closed. Many incorporate human mobility into their transmission dynamics, usually based on static and aggregated data. However, mobility can change dramatically during a global pandemic as seen with COVID-19, making static data unsuitable. Recently, large mobility datasets derived from mobile devices have been used, along with COVID-19 infections data, to better understand the relationship between mobility and COVID-19. However, studies to date have relied on data that represent only a fraction of their target populations, and the data from mobile devices have been used for measuring mobility within the study region, without considering changes to the population as people enter and leave the region. 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. We use the telecoms data to both measure mobility within the country and to provide a real-time census of people entering, leaving and remaining in the country. We develop multiple SEIR (compartmental) models parameterized on these metrics and show how dynamic population metrics can improve the models. We find that total daily trips did not have predictive value in the SEIR models while country entrances did. As a secondary contribution of this work, we show how Andorra's serology testing program was likely impacted by people leaving the country. Overall, this case study suggests how using mobile phone data to measure dynamic population changes could improve studies that rely on more commonly used mobility metrics and the overall understanding of a pandemic.

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

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 spatio-temporal 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 trip-making, 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.

Mass SARS-CoV-2 serological screening, a population-based study in the Principality of Andorra.

BACKGROUND: Andorra is a small country located in the Pyrenees attracting millions of visitors for tourism, mostly associated with skiing, and nature-related activities. As its neighbouring countries, Spain and France, it has been heavily affected by the COVID-19 pandemic. We estimated SARS-CoV-2 seroprevalence in the entire country by universal serological testing under a lockdown environment. METHODS: A total of 77,543 inhabitants of Andorra were invited to participate in the study. From 4-28 May, 2020, two cross sectional serological surveys were conducted using a rapid serological test (nCOV IgG/IgM) on a finger prick blood sample in 59 drive-through or walk-through checkpoints, all over Andorra. We calculated seroprevalence of antibodies against SARS-CoV-2 and analysed the main sociodemographic factors associated with being seropositive. FINDINGS: 70,494 inhabitants (90.9% of the population) participated in at least one survey. Overall seroprevalence was 11.0%. The most affected age groups were those over 90 years old (15.2%) and 80-89 (13.8%), followed by adults 50-59 (13.6%) and adolescents 10-19 (13.7%). Most seropositive participants, 6,061 (95.1%), were asymptomatic before the surveys. The multivariable analysis showed that the odds of being seropositive was higher among seasonal workers (OR 2.41; 95% CI 1.07-5.45) or in the population living in La Massana region, a popular ski-related area (OR 2.66; 95% CI 2.44-2.89). A higher seroprevalence was observed in those familiar nuclei with greater numbers of cohabitants: 18% in families with 6 household members or more; 13% in medium size families (3/4/5 people) and 12% in small size (1 to 2 people) nuclei. INTERPRETATION: The prevalence of antibodies against SARS-CoV-2 in the population of Andorra was high during the first wave of the pandemic. Seasonal workers and inhabitants based in La Massana presented a higher seroprevalence. Mass antibody screening allows to identify infection hotspots and should contribute to the design of tailored interventions to prevent SARS-CoV-2 transmission in Andorra. FUNDING: Andorran Ministry of Health, Andorran Health Services.