Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study

Abstract

Background: On March 17, 2020, French authorities implemented a nationwide lockdown to respond to the COVID-19 epidemic and curb the surge of patients requiring critical care. Assessing the effect of lockdown on individual displacements is essential to quantify achievable mobility reductions and identify the factors driving the changes in social dynamics that affected viral diffusion. We aimed to use mobile phone data to study how mobility in France changed before and during lockdown, breaking down our findings by trip distance, user age and residency, and time of day, and analysing regional data and spatial heterogeneities.

Methods: For this population-based study, we used temporally resolved travel flows among 1436 administrative areas of mainland France reconstructed from mobile phone trajectories. Data were stratified by age class (younger than 18 years, 18-64 years, and 65 years or older). We distinguished between residents and non-residents and used population data and regional socioeconomic indicators from the French National Statistical Institute. We measured mobility changes before and during lockdown at both local and country scales using a case-crossover framework. We analysed all trips combined and trips longer than 100 km (termed long trips), and separated trips by daytime or night-time, weekdays or weekends, and rush hours.

Findings: Lockdown caused a 65% reduction in the countrywide number of displacements (from about 57 million to about 20 million trips per day) and was particularly effective in reducing work-related short-range mobility, especially during rush hour, and long trips. Geographical heterogeneities showed anomalous increases in long-range movements even before lockdown announcement that were tightly localised in space. During lockdown, mobility drops were unevenly distributed across regions (eg, Île-de-France, the region of Paris, went from 585 000 to 117 000 outgoing trips per day). They were strongly associated with active populations, workers employed in sectors highly affected by lockdown, and number of hospitalisations per region, and moderately associated with the socioeconomic level of the regions. Major cities largely shrank their pattern of connectivity, reducing it mainly to short-range commuting (95% of traffic leaving Paris was contained in a 201 km radius before lockdown, which was reduced to 29 km during lockdown).

Interpretation: Lockdown was effective in reducing population mobility across scales. Caution should be taken in the timing of policy announcements and implementation, because anomalous mobility followed policy announcements, which might act as seeding events. Conversely, risk aversion might be beneficial in further decreasing mobility in highly affected regions. We also identified socioeconomic and demographic constraints to the efficacy of restrictions. The unveiled links between geography, demography, and timing of the response to mobility restrictions might help to design interventions that minimise invasiveness while contributing to the current epidemic response.

Funding: Agence Nationale de la Recherche, EU, REACTing.

Figure 1

Phases of the COVID-19 epidemic in France, and its effect on mobility patterns (A) Coloured areas correspond to the different phases of the epidemic response, and red lines mark main government interventions; zones A–D are four government-defined geographical areas of France (Paris is in zone C); dashed vertical lines indicate announcements made by French authorities; school closures were to be implemented starting March 16; the announcement of closure of non-essential businesses was done with immediate effect; the announcement of lockdown was done on March 16, to be implemented the day after at noon, and the black solid vertical line on March 17, 2020, indicates the beginning of lockdown; the black dots track the temporal change of the total number of daily trips measured from mobile phone data in France from Jan 6 to April 12; each timeline is fitted with the training set (thin lines) going from Jan 6 to March 9, with extrapolation up to April 12; shaded areas represent 95% credibility intervals. (B) Maps show the variation in incoming and outgoing traffic compared with the unperturbed baseline predicted by the fit; the chosen dates were March 13, March 16 (day before lockdown), and March 18 (day after lockdown enforcement).

Figure 2

Mobility reduction during lockdown across user type, age, and time of day Reduction was computed as the mean over the week starting Monday, April 6, 2020, with respect to the mean over the first week of February (starting Feb 3). Relative reduction was broken down by residency status (A), age class (B), and time of day (C) and assessed for all trips and for long trips (>100 km). Horizontal dashed lines indicate relative reduction on all residents for all trips and for long trips.

Figure 3

Lockdown-induced mobility reduction across regions (A) Breakdown of mobility reduction by region in mainland France; horizontal lines indicate the corresponding averages across regions, according to type of trip. (B) Map visualisation of the breakdown of mobility reduction.

Figure 4

Reduction in outgoing mobility during April 6–12, 2020, versus demographic, socioeconomic, and epidemic indicators Correlation was evaluated between variations in the outgoing traffic and the four considered indicators: population in active age (24–59 years), portion of employees in the sectors mostly affected by lockdown, the ninth decile of the regional standard of living, and the cumulative number of COVID-19 hospitalisations per 100 000 inhabitants on April 5, 2020. Pearson's correlation coefficients and their p values are reported.

Figure 5

Network analysis (A) Number of mobility connections between French locations over time. (B) Link persistence probability, defined as the probability that a connection present during week Feb 3–9 was still present in one of the four selected weeks: before lockdown (March 9–15), during enforcement (March 16–22), and during lockdown (March 23–29 and March 30 to April 5). (C) Persistence probability and traffic reduction in relation with traffic; for a given value of traffic on link, solid lines measure the portion of broken links that used to have, at most, that weight in the baseline week; dashed lines report the portion of missing traffic that was lost on connections that used to have, at most, a certain weight in the baseline week.

Figure 6

Outgoing egocentric networks of the ten most populated cities in France during baseline week (starting Feb 3, 2020) and during lockdown week, starting March 30 Locations are coloured by incoming traffic from the selected city. Solid lines indicate links that persisted during lockdown. Dashed lines are links that disappeared. Both types of locations were selected to be the top ranked by traffic during the baseline week. The circles contain 95% of the outgoing traffic from the respective city.