Slowdown from lockdown

COVID-19’s path of death and analysis of Google mobility data

As the economic and social fallout from the “lockdown” measures takes its toll across the world the public debate has been increasingly focusing on the rationality of such drastic measures. This debate is both highly relevant and highly emotional. The problem is that everybody is prone to a personal bias on how we combine our risk perception and consequences of the restrictive public health measures. In the following blog we look at the data from different countries to quantify some aspects of this debate.

First, we look at the current trends in the number of deaths. In our previous blog post we explained how we approach this modelling and we gave some predictions in the early days of the rise of the number of deaths. The key dilemma was when will the “curve bending” (slowdown in the death trend) start and how fast is it going to “bend” (i.e. how fast the death rate will drop). Now that we can follow these curve bendings (sources of data: Johns Hopkins University, the New York Times, data.gov.uk), we see that our predictions were accurate. Keep in mind, though, that we work only with the officially recorded COVID-19 deaths, which we now know is not the total death toll (see the examples for the US here and here), but it represents a self-consistent dataset in the sense that it tracks individuals that were identified by the health system as COVID-19 patients.

Figure 1: Cumulative number of deaths for various countries, starting from the day of 10 recorded deaths. Thick lines are the data, thin lines are models (see our previous blog for the methodology). Interactive version available HERE.

Italy is now headed toward about 33,000 deaths (we initially said it will be close to 40,000; the reason for an overestimation of the death toll here is a stronger effect of the measures put in place), France to 32,000 (we said it will be very close to Italy without giving an exact number), Spain to 28,000 (we said it will be slightly lower than 30,000), while the UK unfortunately leads the European countries with the projected final toll of 37,000 deaths (we gave it a big chance of being worse than Italy). Belgium is counting more carefully than other countries the probable deaths from COVID-19, which leads them to about 11,000 deaths. Germany is projected to reach about 9,000 deaths (we said it won’t go over 10,000), the Netherlands about 6,000, Sweden just over 4,000. However, all these projections, like the ones before, come with a caveat that the model assumes the social distancing measures to be as effective as they have been thus far. Under this assumption, those numbers will be reached during June. The next figure shows these projections in time.

Figure 2: Daily deaths for countries with the highest number of deaths. Thick lines are the data, thin lines are models (see our previous blog for the methodology). Interactive version available HERE.

The United States is above other countries in the number of deaths and the model converges to about 90,000 deaths by early July, which is consistent with the predictions coming from the White House. (EDIT: Since May 7th the US data include both confirmed and probable Covid-19 cases and deaths. This increases our projected death toll to 97,500 by July 2020). Canada will also almost stop the deaths in early July, but at about 9,000. Interestingly Canada shows the relatively longest curve bending, which means it is spreading the effects of the pandemic over a longer time period than typical for other countries. Also, the decreasing trend in the daily deaths is similar for several countries — The Netherlands, Germany, Belgium, France, UK — and this trend was almost the same in the Hubei province of China (where the pandemic started). The US, Italy and Canada have slower trends, but surprisingly Spain has a faster decline than Hubei. It is hard to decipher the exact reasons for differences in these trends (enforcement of strict measures, the demographics of deaths, the spread of virus in retirement homes, etc.) just by looking at the numbers.

Google Mobility data analysis

However, what we can do is to look at the Google Community Mobility data and compare it with the COVID-19 trends. This mobility data is derived from the aggregated data of Google users who opted in to Location History. It tells us how many people changed their behavior and reduced the travel beyond their homes. Comparison of this mobility with the COVID-19 trends for one country or one US state does not tell us much. But if we do that exercise for a bunch of countries or states then a pattern should emerge.

The debate over the lockdown can be broadly divided into three questions:

  1. Did we need a lockdown in the first place?
  2. What kind of a lockdown should we (have) implement(ed)?
  3. When and how to exit the lockdown?

The patterns that we look for cannot answer the second and third questions because this requires a detailed analysis of social, cultural, demographic and economic differences between countries. But they can give us at least a partial answer to the first question.

Figure 3 shows a comparison of mobility data between countries. We selected the mobility at transit stations as the most representative for comparing different countries. We can see that all countries exhibit a drop (notice that Italy has two drops: the first is when Northern Italy went under lockdown, the second is when this was extended to the whole country). Sweden was the most “liberal” in the regard, while New Zealand was the most radical in its social distancing implementation. Spain and Italy, two countries that were fighting a large health crisis, also show a drastic reduction in mobility of their citizens.

Figure 3: The Google Community Mobility data for different countries. The data are shifted vertically to start in average at zero. We find the middle of the declining trend for each country and set this as day zero. Interactive version available HERE.

From the mobility data we find day zero for each country — the day when the mobility drop was happening. Now we can take the COVID-19 data and calculate how quick was the increase in the number of infected and deaths. In Figure 4 below we plot the doubling time of confirmed COVID-19 cases. It shows a consistent pattern — the doubling of the cases took days before the moment of mobility drop (day zero) and then started to slow down dramatically after the introduction of the lockdown, no matter the country. Two weeks after day zero all countries have lowered the doubling time to between 5 days and 2 weeks. Three weeks after day zero this time was between 1 and 3 weeks. Nowadays the doubling is happening every 3 weeks or more.

Figure 4: The doubling time of confirmed COVID-19 cases on days relative to the drop in mobility (day zero) for each country. Interactive version available HERE.

Death trends lag behind the trends in confirmed cases since the disease takes its course. It is thus not surprising that a similar analysis for the doubling time of the number of deaths shows the same story (Figure 5 below). Notice that even with the deaths that happened after the mobility drop (day zero) the doubling time remains within the overall pattern. This has a sad consequence illustrated by one detail that we added to the plots — the line thickness corresponds to the total number of deaths. Countries that did not introduce the lockdown early enough ended up with a lot of deaths. This is why lines before and around day zero are so thick. Those countries had the virus spreading like wildfire meaning that the pool of infected was already enormous by the time they introduced radical public health measures.

Figure 5: The doubling time of COVID-19 deaths on days relative to the drop in mobility (day zero) for each country. Line thickness illustrates the total number of deaths so far. Interactive version available HERE.

Mobility analysis of US states

We did the same analysis for US states, with the exception that we took the Google mobility data on retail and recreation instead of transit stations as before. It shows more consistent behavior in the US than public transport trends which is a consequence of different utilization of public transport across US states. Figure 6 illustrates this: mobility dropped almost identically across all 50 US states. This is a bit surprising giving the heated debate in the US about the lockdown policies, and different response strategies applied by different states. The data shows that the US population is far more unified on this issue than what the daily news would imply.

Figure 6: The Google Community Mobility data for different US states. The data are shifted vertically to start in average at zero. We find the middle of the declining trend for each state and set this as day zero. Interactive version available HERE.

With day zero extracted for each state we now show how the number of confirmed COVID-19 cases changes its trend. Figure 7 shows a story identical to the one we got from the cross-country comparison. The US had various issues initially with testing for COVID-19, which introduced a larger variation of doubling times before the mobility drop at day zero. Nonetheless, the overall pattern is very clear — the “lockdown” measures have consistently reduced the spread of COVID-19 across the US.

Figure 7: The doubling time of confirmed COVID-19 cases on days relative to the drop in mobility (day zero) for each US state. Interactive version available HERE.

The doubling time of COVID-19 deaths paints the same picture as before. It is a sobering visualization of how New York went from almost daily doubling of deaths at the time of lockdown to a 4 weeks doubling period today, which is one of the slowest in the US.

Figure 8: The doubling time of COVID-19 deaths on days relative to the drop in mobility (day zero) for each US state. Line thickness illustrates the total number of deaths so far. Interactive version available HERE.

What comes next?

The big question now is what comes next. Our analysis illustrates that the public health measures focused on social distancing worked, however the way how this has been achieved varies between countries. Now the main topic is the relaxation of those measures. This does not necessarily mean that social interactions will bounce back to their pre-COVID-19 levels. If people change their behavior and maintain distancing in their daily contacts the virus will not be able to return with its full strength into circulation. But if the relaxation of the social distancing measures goes too far, the number of infected and dead will start to rise.

Oraclum is a data company led by a team of scientists that builds prediction models. https://www.oraclum.co.uk