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. 
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.  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.  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.   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. 
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. 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. ↩︎ ↩︎
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. ↩︎