The second half of October has seen average movement range drop a bit across most parts of the country. However, relative mobility in Georgian cities remains higher than in most European cities with similar Covid-19 incidence.
In a blog post ten days ago, I pointed out the continuously high levels of mobility observed in many parts of the country despite the ongoing pandemic. This cast doubt on whether it was possible to manage the coronavirus spread without imposing additional restrictions.
I also noted in my post that it was too early to assess the impact of the regional measures enacted in Tbilisi and Imereti on 16 October and Mtskheta municipality on 20 October. As it had previously done in Adjara, the government imposed a late-night curfew on bars, restaurants and entertainment facilities. As daily cases rose above one thousand, the government’s and health authorities’ communication displayed a hightened sense of urgency, which was accompanied by attempts to enforce measures more strictly and a “strong recommendation” to wear masks even outdoors in the biggest cities.
Average movement range has since dipped across the country, although an uptick in the days before the election calls into question how durable this trend really is. Mobility levels in Tbilisi and Kutaisi (Imereti) have fallen below the levels observed in February. Mobility in Batumi remains rather low despite the partial reopening of public transport on 19 October. Movement range in rural areas has dipped somewhat as well, but still tends to be higher than in winter.
The decrease in mobility during the third week of October seems to correlate well with a stagnation of daily new cases just below 2000 during the last week. However, caution should be exercised when insinuating a direct causal relationship. Mobility is only one of several factors influencing the overall infection rate. Moreover, the PCR test positivity rate has increased to around 20%, which could lower the overall fraction of infections that are being detected. It is unclear from publicly available data to what extent recently intensified rapid antigen testing is able to fill the gap.
How about mobility in Europe?
To put the mobility data into context, let us have a look at some other highly affected countries. The chart below contrasts relative average movement range during the past three months in Batumi, Tbilisi and some of the major European cities which currently have a similar or higher Covid-19 incidence than Georgia.
The relative mobility observed in Batumi in August was remarkably high in comparison with almost any other city. Indeed, Batumi was one of the first cities in Europe to be back on the dancefloor after lockdowns. It was bustling with life at a time when others, such as Madrid, were still cautiously approaching some sense of normality in the wake of a devastating first coronavirus wave in spring.
In the meantime, mobility in all major red zone cities has fallen below February levels, although the reductions are nowhere near the 30-80% decreases seen during the radical lockdowns in spring.
As of 28 October, relative movement range in Batumi and Tbilisi was still higher than in other red zone cities, but they were in good company: Zürich and Luxembourg were on the same trajectory.
In the past few days, many countries have tightened their restrictions further, which is not yet fully reflected in the data presented here. More so than during the spring wave, European countries are trying a variety of different approaches right now:
- On the tough end of the spectrum is France which has just returned to a full lockdown with all-day curfew. (With the exception that, unlike in spring, most schools remain open.) Mobility in Paris will decrease drastically as a consequence.
- Most countries have entered some form of partial lockdown, choosing from a palette of measures such as night-time curfews, internal travel restrictions, closure of specific or all non-essential businesses, restrictions on meeting non-household members, etc.
- On the moderate end of the spectrum we find Switzerland and Luxembourg. Although partial shutdowns have been imposed in some Cantons of Switzerland, the federal restrictions are similar to those currently in place in Tbilisi, Imereti and Adjara.
- Additionally, the idea of short fixed-term (2-4 week) so-called “circuit breaker lockdowns” is being floated in many countries. These are primarily aimed at reducing the strain on hospitals and allowing contact tracers to catch up with the virus. England, Wales and Northern Ireland are just embarking on such an experiment.
It remains to be seen which of these strategies are the most sustainable in terms of overcoming the winter with as little damage to human life, the economy, mental health and all the other aspects of society for which the coronavirus poses such a multi-faced challenge.
As things stand, Georgia is pursuing the ambitious path of managing the pandemic with as few intrusive restrictions as possible. One day before the elections, Georgian Prime Minister Giorgi Gakharia has stated again that, should his party form the next government, there would be no national lockdown in Georgia. On the same day, the Head of the National Center for Disease Control Amiran Gamkrelidze indicated that outdoors mask wearing might become mandatory.
Correction: In a first version of this post the bottom figure mistakenly displayed the incidence in USA, Europe and Georgia as of 30 October instead of 1 November. In particular, Racha-Lechkhumi-Kvemo Svaneti and Kvemo Kartli have since moved into the >250 red zone.
Methodological notes: The mobility data is from facebook data for good and was described in more detail in my last post. The data is organised by GADM level 2 regions. For some cities, this includes large regions which are not necessarily part of the metropolitan area (e.g. the data for Milano includes all of Lombardy and the data for Warszawa all of Mazowieckie).
The data presented in the figures above are trendlines computed by applying a Savitzky-Golay filter of polynomial order 1 and window size 7 days (which amounts to a sort of weighted moving linear regression). The last data point shown in each figure is October 28, which includes data ranging from October 25 through October 31.