Reopening strategies, mobility and COVID-19 infections in Europe: panel data analysis

Abstract

Objectives: Characterise the reopening policies of European countries after the first wave of infections and evaluate how these policies affected economic activity and subsequent infections.

Study design: Using publicly available data, we construct a database of reopening policy announcements by country authorities and develop measures related to the speed and timing of reopening. Using panel data regressions, we then assess how a country's reopening action subsequently affected its mobility and COVID-19 infections. Samples of 22 European countries used in the study comprise: Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Ireland, Israel, Italy, Netherlands, Norway, Poland, Portugal, Romania, Russia, Spain, Switzerland, Turkey, Ukraine and the UK.

Main outcomes: Mobility index as well as COVID-19 case and death counts.

Results: Reopening policies are associated with a 1.5 percentage point increase in mobility and a 4% increase in subsequent infections after 2 weeks. However, some reopening strategies are associated with lower infection risk. In particular, early and fast reopeners saw 5%-10% increases in infections relative to those that opened later and adopted a gradual approach. The sequencing of sectoral reopenings matters, with infection amplification effects larger for some sectors (like retail and events) than others (like schools).

Conclusions: Findings suggest some merit of gradual and late reopening strategies with a careful sequencing of sectoral openings based on their infection amplification risks.

Keywords: COVID-19; epidemiology; health economics; health policy.

Figure 1

The graph in the top panel A plots the growth of daily deaths from peak to reopen. Note that the sign for the peak to reopen change in death is reversed for countries that open before the peak is reached. The graph in the bottom panel B plots the number of days since reopening for all countries in the database. The shaded area shows the number of effective days open which is measured as the cumulative (daily) extent of reopening across sectors. The diamond markers show the ratio of effective to actual days open. AUT, Austria; BEL, Belgium; CHE, Switzerland; CZE, Czech Republic; DEU, Germany; DNK, Denmark; ESP, Spain; FIN, Finland; FRA, France; GBR, UK; GRC, Greece; IRL, Ireland; ISR, Israel; ITA, Italy; NLD, Netherlands; NOR, Norway; POL, Poland; PRT, Portugal; ROU, Romania; RUS, Russia; TUR, Turkey; UKR, Ukraine.

Figure 2

The graph in the top panel A is a boxplot which shows the distribution, across countries of the phase in which each sector opened. The graph in the bottom panel B plots the cumulative changes in the reopening index as of 30 June. The index ranges from 0 to 18 based on the opening status of school, retail, service industry, public/events, travel with 0 (open), 1 (open with restrictions), 2 (partially open), 3 (closed); dots show per cent of restrictions removed. AUT, Austria; BEL, Belgium; CHE, Switzerland; CZE, Czech Republic; DEU, Germany; DNK, Denmark; ESP, Spain; FIN, Finland; FRA, France; GBR, UK; GRC, Greece; IRL, Ireland; ISR, Israel; ITA, Italy; NLD, Netherlands; NOR, Norway; POL, Poland; PRT, Portugal; ROU, Romania; RUS, Russia; TUR, Turkey; UKR, Ukraine.

Figure 3

The graph shows point estimates (solid line) of reopening measures and lagged daily deaths on mobility with 90% CI (shaded area) adjusted for heteroscedasticity and autocorrelation.

Figure 4

The graph shows point estimates (solid line) of reopening measures on the (log) 7-day moving average of cases and deaths with 90% CI (shaded area) adjusted for heteroscedasticity and autocorrelation.

Figure 5

The graph in the top panel A shows the percentage difference in daily cases between fast and slow reopeners (blue dot with 90% CI line) and early versus late reopeners (red triangle with 90% CI line). The bottom panel B shows the same differential for mobility; in panel B, the y-axis shows the effect size in percentage point (p.p.t).

Figure 6

The graph in the top panel A shows the predicted path of the mobility index based on estimating the effect of reopening for each type of strategy. The bottom panel B shows similar predicted paths for daily cases.

Figure 7

The graph shows the percentage difference in daily cases (21-day horizon) between fast and slow reopeners (blue dot with 90% CI line) and early versus late reopeners (red triangle with 90% CI line), depending on whether there was a low level of reopening in peer countries (one-third or less of the economy reopened) or high level of reopening in peer countries (more that one-third reopened).

Figure 8

The graph shows the total effect (in percentage change) on daily cases from a one-unit easing of sectoral restrictions based on whether it was sequenced to open early (few other sectors opened), in the middle or late (most other sectors opened). Industry is omitted as it was always sequenced early.