Yet Another COVID Data Story: What Do Mobility Data Reveal on Lockdown vs Non-Lockdown Policies?
By: Olivier Schmitt
Tags: Matplotlib, Data, Data Visualization, Data Science, Covid 19

As a french data engineer interested in data science I thought I could try to get my head around one of the most debated topics in France using data science techniques.

Disclaimer

The goal of this article is not to assess COVID policies but to try to use the available data to assess a common belief, which by the way has driven and drives a lot of anger in France.

Lockdown vs self-responsibility

During the first wave, the french government has deployed a policy in which lockdown was the cornerstone. Each country had its own policy and some countries did not implement formal lockdowns enforced by law.

In the non lockdown category we can easily put Sweden and Switzerland, whereas we can of course select France but also Italy, Spain, Portugal, UK, Ireland, Denmark in the lockdown category. In the grey zone, Germany, Finland, Netherlands at least.

It’s not very easy to compare countries but Sweden and then Switzerland are surely seen as the most flexible countries in this matter.

In those two countries self responsiblity was the cornerstone of COVID policies: people at risk were asked to stay at home and life went on as usual or supposedly so. We shall see this later.

A well-anchored belief

One common belief in France was and still is: since Sweden did not enforce a lockdown, swedish people were living a normal life although they did not experience so many deaths in return.

This assumption is based on another one: in northern Europe people trust their government so much they won’t suppress their own rights if not required to do so explicity.

Basically, this belief means no gap exists between the government policy and how people actually behave on the ground.

Data to the rescue

Apple and Google have released mobility data recently and those data are a treasure chest.

In this article I will demonstrate some use I made of Google mobility trends to try to get my head around this belief.

First of all the Google mobility trends come with some notes which need to be understood:

data are the location history of users when the feature is on

data measure the change in % from the pre COVID baseline

baseline day is the median value from the five‑week period 3 Jan — 6 Feb 2020 and it’s not a perfect baseline

changes are distributed amongst location categories such as workplace, park, residential, transit, ...

demographic metadata are not available

data can vary a lot depending on the lifestyle (urban, …)

Starting from there I decided to pick many urban areas and to compare them to Paris:

Rome

Barcelona

Stockholm

Lisbon

Copenhagen

My assumption is big urban areas are likely to host the same kind of population, mostly working in the service industries and likely to be well equiped with smartphones.

Big cities are also often associated to what we call in France metro, boulot, dodo which can be translated as a transportation, work, home lifestyle.

Since the workplace, transportations are associated to the most risky places to get the virus I kept only the following fields from the data:

retail_and_recreation_percent_change_from_baseline

transit_stations_percent_change_from_baseline

workplaces_percent_change_from_baseline

Retail and recreation changes are there to assess the economic impacts on the real world: in our digital western world one can perfectly work from home and no economic damage is to be seen. But retail and recreation exist in the physical world and are impacted when there is a new normal.

Then, my choice was to compute the mean of each day per field (up to 7 measurements) and then to compute :

a rolling mean of 7 days for transit and workplaces

a rolling mean of 14 days for retail & recreation because it’s a very spiky and dynamic feature and I want to understand trends

The idea was to filter out the noise in order to get the high level mobility trends.

Paris versus northern Europe

In the following charts, the red areas map to the frenck lockdown periods.

Sweden might not have choosen to confine people but they did it anyway to a certain extent

Semi confined Amsterdam is close to Paris which was completey confined

Semi confined Copenhagen (Denmark)

Semi confined Helsinki (Finland)

Countries from the south behaved very similarly to Paris

When no confinement means confinement

Switzerland compared to France is not that far which was a topic of amusement for some swiss comedians.

Please note the french transit exploding in the middle of the summer

Please enjoy your summer holidays

In this “Le monde” newspaper article the french government is said to strongly encourage french people to go on their usual summer vacations:

Edouard Philippe unveiled a “massive” tourism recovery plan, representing “a commitment of 18 billion euros for public finances”.

18 billion! Well it seemed to have worked pretty well when you compare France to countries where people are used to move a lot especially to… France:

The NL “travel smart” official page for summer 2020

In Belgium a quarantine was more or less mandatory during the summer 2020

Conclusion

To me, policies when they are not enforced by law are not necessarily applied by people.

In the COVID case, despite limits of the data at hand, the gap seems to be quite huge and people should carefully look at the data before jumping to conclusion.

According to the IMF, Sweden real GDP growth should be at -4.7%, worst than in 2008 (-4.3%) which of course is far from what one could call business as usual, but better than other countries. Please note Sweden has adjusted its strategy recently to fight the second wave and we will only understand the GDP impacts later.

In a next article I might try to understand the mobility trends impacts on COVID metrics and I might attempt to build a predictive ML model.

Technical side notes: