This post takes a look at Covid data with a particular focus on the number of
new daily cases and the growth (or reduction) of those daily cases over time. If
this were physics, we’d be looking at speed and acceleration, rather than the
total distance traveled. I won’t try to convince you of anything, but rather
just try to build an understanding of where we’ve been, where we are, and what
to expect in the next few months.
Let’s start with the growth in daily cases for US states since March 10th, for
states reporting at least 20 cases:
Each dot represents the growth in the number of new daily cases for a US
state on a given day. I discuss methodology further at the end of this post if
you’re interested. [1]
We can clearly see a few crucial trends in this chart. Growth was furious for
all states in mid-March (20% daily growth means doubling in 3.8 days, as you’ve
surely heard) and showed a lot of variance. Then nearly all states issued
stay-at-home orders between March 23rd and April 3rd. [2] These
orders, no doubt coupled with some amount of anxiety and precautions from the
population, quickly reduced growth rates, which were clustered around 0% by
mid-April. This was a significant accomplishment. Sadly, we were unable to
improve from there, and never brought growth figures consistently or
substantially below zero. Here’s the same data seen by week in a slightly
different way:
We started out red-hot and worsening in mid-March, but that gave way to slower
growth and calmer colors. The initial success was followed by stagnation, and
slight worsening in the last two weeks. Let’s look at our nationwide figures:
New daily cases peaked on April 10 in the US at about 32,000 cases/day. They
have since fallen to 21,500 cases/day. [1:1] Growth peaked at 40% on
March 24, shortly before the lockdowns started, then fell sharply hitting 0% on
April 15.
Now consider this: we had about 7,000 cases/day on March 25, as we headed into
lockdowns, and we have 21,500 cases/day now, as we are leaving them. That might
feel a little disheartening. What happened? Was there any point to this whole
thing? Did we just destroy countless jobs, businesses, and dreams for no good
reason?
There are three good answers here. The first is that the precipitous fall in
growth brought about by the lockdowns was a major win that probably averted
total disaster. However, unless you look at a plot of growth rates, or at least
look at daily cases and appreciate the trend, this win is somewhat hidden.
I hope the charts so far have done a decent job of showing this aspect of our
journey.
The second is that the lockdowns were indeed somewhat pointless. Not because
they are inherently so, but because we’ve done a bad job and failed to
significantly bring down case numbers while we had a perfect opportunity to do
it. We bought the lockdown with trillions of dollars and untold sacrifice, and
then squandered it.
The third answer is that we have to consider states separately to really analyze
the situation, because national data is just too blunt. States had varying
levels of success and peaked at different times, and to understand what worked
we need to factor that in.
Let’s look at what other countries achieved with their lockdowns:
Those curves show the kind of drastic reduction in the number of daily cases
that well-organized societies can achieve. They are able to push growth
significantly below zero and keep it there long enough to bring case numbers
down an order of magnitude or more. A smaller outbreak is then more amenable to
containment by well-design policies while economic and social activity is
restored.
Let’s look at more countries for better context. Here are the ten countries most
successful at containing the pandemic from a peak of at least 70 cases:
I have excluded China from the list due to controversies around their data. They
would have been 4th place with a 99.8% reduction from a peak of 4,687 cases/day.
We see some islands in there, some smaller populations, and also small peaks.
It’s worth pointing out that neither islandness nor a small population are any
guarantees, as the history of smallpox in Iceland can attest.
[3] Still, countries like Switzerland and Austria vanquished
pretty large outbreaks and are not islands last I checked. Social cohesion and
good policies seem like the overriding factors. But let’s look at a more
diverse group of places:
Sweden is the only wealthy country in this list doing worse than the US. This
was not cherry picked: that remains true when you look at the whole world, where
the US ranks 62nd by this metric. In the last week Sweden’s top epidemiologist
has admitted mistakes in their strategy. [4]
[5] However, the overall number of infections is low in Sweden,
and their growth has been kept mostly in check, never spiraling out of control.
They are a highly conscientious society that took a daring (and often
misrepresented) approach with a clear understanding of the trade-offs involved.
The situation in the bottom countries is catastrophically different. They all
have strong growth of already sizable outbreaks, with Brazil in an especially
dire situation, no doubt the worst in the world, having recently overtaken the
US for the top spot in daily cases amid continued growth. Their president is now
attempting to censor Covid numbers, and it’s possible Brazilian data will no longer
be reliable over the next few weeks. [6]
Even if we ignore any mistakes made before mid-March, it is clear from this data
that the US has not done a great job containing the pandemic. Despite remaining
in a fairly strict lockdown for weeks, we performed worse than all but one rich
nation in reducing case numbers. But let’s not yet worry about whether we’re
a failed
state
or have been made great again.
After all, the US is a large and heterogeneous place, and looking at national
aggregate data obscures a lot of the story. States like Alaska, Montana and
Wyoming never had more than 25 cases/day, while New York reached 9900 cases/day,
a peak greater than every nation’s except for Brazil and Russia. Having seen
what other countries look like, here is what happened in US states with a peak
of at least 70 cases/day:
A handful of states managed substantial reductions in daily cases, including New
York, which had by far the largest outbreak in the US. That’s cup half full.
Still, at 91.3% decrease New York is behind most developed countries. It is
striking that none of our states have managed to do as well as Spain, Italy,
or Germany when it comes to reducing case numbers.
And then there are the states at the bottom of this list. When you see 0% that
means no reduction: these states are currently at their historical maximum and
growing, and we don’t know when and where they’ll peak.
Keep in mind the decreases in the chart above show the reduction in each state’s
daily cases measured against its own peak. To get an idea of how states
changed since the national peak, and how the outbreak decreased in some areas
and increased in others, here are the most substantial deltas in daily cases by
state since the US peaked on April 10th:
Since we peaked nationally on April 10, we have reduced daily cases by about
10,500/day, with most of the reduction coming from New York (9,000 cases/day)
and New Jersey (3,000 cases/day). It might strike you as odd that the national
decrease (10,500 cases/day) is smaller than the decrease from just New York and
New Jersey (a combined 12,000 cases/day). And sure enough, if we exclude those
two states, daily cases have actually increased in the rest of the US since
our national peak. Without NY and NJ, on April 10 we were at 18,300 cases/day,
then we peaked on May 6 at 21,400 cases/day, and are now at 20,000 cases/day,
for a reduction of 7%.
So let’s talk the future and make some predictions. Think about these two
questions:
- How many states will see a daily cases peak at least 30% greater than any
peak they’ve had so far? - How many states will be forced back into lockdown?
Then consider these facts: compared to other developed nations we have done
a much worse job reducing our outbreak; we did not use our lockdown period to
develop comprehensive policies to fight Covid; we have not used leadership to
galvanize the population to fight the pandemic and adopt practices that mitigate
spread – quite the opposite, we have started a culture war around wearing masks,
social distancing and whether to even take Covid seriously; many American
leaders undermine mitigation by deed and word; even while in lockdown, we have
only been able to achieve modest daily reductions in case numbers; people feel
like they have done their duty and should now be able to resume life, being
generally sick of hearing about Covid and all its controversies and
conspiracies; places highly prone to spread, such as gyms, churches, and
restaurants, will resume operations; domestic travel will resume so that any
counties with larger outbreaks might seed those with fewer cases; finally, if
daily growth increases even to a modest 5%, cases will double in two weeks under
the inexorable march of exponential increase.
Offsetting these is the fact that a large part of the population is much more
careful and attuned to the spread of Covid. Humans are remarkably adaptable, and
maybe smart on-demand interventions at the county and state levels can curb
local outbreaks.
Before answering those two questions, let’s take a look at the familiar
case-and-growth plots for the 40 states with cases/day currently over 70:
Many of those curves don’t look great. Keep in mind some of the spikes we see
mid-graph are due to specific incidents like outbreaks at a prison.
But enough of the charts, let’s try our hand at divination. Only eight states
have managed a decrease of 70% or more in their daily cases (nine if we count
Pennsylvania at 69.8%). These are the states most likely to keep things under
control: most have seen a serious situation, all have been effective by US
standards, and they are further down from their peaks. I’ll round up and say 10
states will avoid a greater peak in the future. The other 40 will see a peak at
least 30% greater than their current peak. And of these 40, at least half will
adopt lockdown measures before the end of the year that affect a majority of
their population.
This is all the data I’ve got for now, but if you’ve read this far, you might as
well stick around for a few broader considerations.
First, the trade-off between economic outcomes and epidemiological outcomes has
become grossly overstated. The more infection we have, the more the economy
will be affected as people shy away from economic activity.[7]
A failure to intelligently fight Covid is an economic failure as well. Brazil is
a sad example of this, as the out-of-control outbreak has wreaked havoc in the
economy.
Almost every containment strategy – personal behaviors, contact tracing,
widespread testing, effective quarantine of sick patients, etc. – ultimately
benefits the economy. Every leader who has mocked or sabotaged Covid containment
is hurting economic output. And plenty of economic activity can be encouraged
with low risk, especially if smart mitigation is applied. Even where a trade-off
seems obvious, say opening up restaurants without restrictions, things are not
so simple: the net economic effect needs to account for the consequences of the
greater spread of Covid, which unfortunately is very likely in restaurants.
The trade-off is much more direct when it comes to personal freedom. Church
services are a perfect example. They are simultaneously: 1) prone to spreading
Covid, 2) not responsible for a lot of economic activity, and 3) extremely
important to a large part of the population.
Or to pick a different demographic, look at skiing in Colorado. Plenty of
people here would be willing to risk infection in order to ski, yet this choice
was denied to them. This may seem like a trivial sacrifice, but to many it is
deeply meaningful. Skiing is a complex trade-off since it does involve a lot of
economic activity and also enormous Covid risk, as we saw when tourists started
various outbreaks in our ski towns. Yet there is also a strong personal freedom
component embedded in it. It is interesting that the restrictions which most
incensed Michigan protesters were related to personal freedoms, like the use of
personal boats.
The moral calculus around Covid trade-offs is complex. Risk to self; risk to
others you might infect; risk to society at large if we overrun the health
system; how to weigh death against hardship, enjoyment, and freedom; how much we
value the life of elderly people and those at greater risk of complications, and
so on.
But there are a lot of actions and personal decisions that remain invariant no
matter how you feel about trade-offs. God knows we are all sick of Covid, now
that the novelty wore off and this looks like a long haul. But stay as safe as
possible, and for whatever degree of risk-taking you decide on, mitigate as much
as possible.
I hope this has been useful and informative. Thanks for reading!
-
All of the data for this post comes from either the European CDC
or the New York Times state-level dataset for Covid. The Covid Tracking
Project dataset has also been extremely helpful, but is not used here. I used
7-day rolling averages for all Covid figures. The county, state, and national
reporting is very noisy with frequent spikes and troughs. They also tend to be
very sensitive to the day of the week and particularly to weekends. The 7-day
average smooths this out with the nice benefit of capturing exactly one week,
which further helps with the day-of-week variations. I also use a 7-day
interval to compute growth. This again smooths out noise and allows for more
meaningful comparisons. The growth figure is simply the seventh root of the
factor obtained by dividing a figure for day N by the figure for day N-7.
Whether to use cases, hospitalizations, or deaths is another interesting
decision. Cases and deaths data is more robust and widespread. Deaths are
a lot more sensitive to particularities of an outbreak: a high percentage of
deaths is linked to elderly care facilities, for example, so it is possible to
have high death figures that overstate the size of an outbreak. Deaths also
depend on quality of care, and are far more delayed, frequently happening
anywhere from 2 to 12 weeks after infection. Symptoms and detection of a new
case are much quicker and vary less. I feel that to understand the dynamics of
an outbreak, cases are more useful. Since these charts are all generated by
code, I did an experiment using deaths instead of cases and the trends held up
consistently, albeit delayed by 2-3 weeks. Cases are sensitive to the amount
of testing being done. If the amount of testing is somewhat constant, and the
percentage of detected cases is consistent, then at least the relative
changes in the number of cases will be meaningful, even if they only capture
a fraction of the total. But if testing is increased, this can show up as more
daily case numbers, when in reality only detection increased. Looking at the
percentage of positive tests vs. total tests can help detect that issue.
I have used the data from the Covid Tracking Project, which does provide
testing information, and also the figures for deaths, to see whether changes
in testing play a big role in these trends. That does not seem to be the case
looking at the data. ↩︎ ↩︎ -
U.S. state and local government response to the COVID-19 pandemic ↩︎
-
https://www.newyorker.com/magazine/2020/06/08/how-iceland-beat-the-coronavirus ↩︎
-
https://www.bloomberg.com/news/articles/2020-06-03/man-behind-sweden-s-virus-strategy-says-he-got-some-things-wrong ↩︎
-
https://www.theguardian.com/world/2020/jun/03/architect-of-sweden-coronavirus-strategy-admits-too-many-died-anders-tegnell#maincontent ↩︎
-
https://www1.folha.uol.com.br/equilibrioesaude/2020/06/governo-deixa-de-informar-total-de-mortes-e-casos-de-covid-19-bolsonaro-diz-que-e-melhor-para-o-brasil.shtml ↩︎
-
Morning Consult tracks how safe consumers
feel
and consumer
confidence
more broadly. It will be interesting to see the relationship between economic
recovery and successful containment in various countries. ↩︎
