No One Can Prove that Thanksgiving didn’t Accelerate COVID, but the Data Indicates that it is Highly Unlikely that it Did.

Since the Thanksgiving Holiday season I’ve seen a number of major media outlets leading with stories about how Thanksgiving led to an increase in cases. Examples from WebMD, NPR, and others. So is this real, or is it an example of Confirmation Bias? It’s hard to know and even harder when one looks at just short-term trends.

Parts of the justifications of these publications for their assertion that Thanksgiving led to an increase in cases is that contact tracing has discovered a number of cases that can be traced back to Thanksgiving gatherings. The NPR article reported that:

“We are seeing a tremendous surge in cases in many locations around the United States that are associated with the Thanksgiving dinners, family get-togethers and social events,” says Michael Osterholm, an epidemiologist and director of the Center for Infectious Disease Research and Policy at the University of Minnesota. Much of the evidence comes from health departments that are tracing clusters of cases, but Osterholm suspects that hospitalizations and deaths — “lagging indicators” — will reveal the full impact in a few more weeks.

https://www.npr.org/sections/health-shots/2020/12/21/948809129/epidemiologists-urge-a-cautious-christmas-after-thanksgiving-surge-in-some-state

So, can we determine that the Thanksgiving Holiday gatherings were causal for increased case counts?

Lets start by looking at the data. Below, I picked a few different states to compare their case rates in one chart. Since I’m normalizing the raw case counts by the population of the state (actually, per 1000 persons), I’m able to compare cases in a relatively “apples to apples” way. Therefore, we see a number of things in the chart below…

1. The Dakotas ran up to the highest numbers of cases per 1000 residents in the country. Their surge started around mid- to late-August where it appears that they transitioned from linear case growth to non-linear (3rd-degree polynomial) levels of case growth. During this latter stage, the growth rate increased every day (it accelerated, actually) until somewhere around mid-November when the cases began decelerating. You can see when this happens by looking at where the upward curve switches to a downward curve. Nebraska seems to have started decelerating around the same time. The Dakotas’ period of case acceleration appears to run from mid-August to mid-November (3 months) whereas in Illinois it ran from late-September to late-November (2 months). We see a similar outbreak range of 01 October to the end of November for New Mexico, which has had some of the strictest COVID policies in the nation. I’m curious if this is a sign of an effect of the stronger government COVID policies in Illinois and New Mexico, but this would take much more analysis to prove.
2. While the Dakotas were surging, California (the light blue line) was maintaining linear case growth. However, sometime around mid-November, California’s linear growth began accelerating and you can see that their rate of acceleration (the highest in the country right now) is starting to approach that of the Dakotas from mid-November.
3. We can also see a handful of other places where states transitioned from linear case growth to non-linear case growth. I’ve tried to eyeball these and place a blue diamond where I think the transition occurred. After the transition, as a reminder, every day the case growth rate increases. I did a quick peek over at Accuweather.com to see what the high and low temperatures were in the largest city in each “blue diamond” state during the timeframe the transition from linear to non-linear growth occurred. In most to all of these cases, the non-linear transition occurred during a notable weather shift where the night-time temperatures went from somewhere in the 60’s or above to 50 or below (degrees F). In some cases, the low temperatures dropped more than 5 degrees in a day or two.
4. I’ve marked the Thanksgiving holidays with a blue rectangle. At least of the states represented here, none of them had a linear to non-linear transition after Thanksgiving.

Since the NPR article mentioned a surge in the Southeast, I re-ran the code that generates the above chart using different states, mixing southeastern states with other warm states as well as NY and Mass. See below. Tennessee has a very high rate of acceleration right now (almost as high as California), so you can see that it is curving strongly upward. It seems like it’s inflection point between linear and non-linear happened sometime in early November. Looking at the other SE states, I see inflections in similar timeframes. I don’t really see any states here that were linear until Thanksgiving and then go non-linear (signal of a major outbreak). Since I live in Arizona, I paid special attention to the Arizona curve. You can eyeball on the green line below that all was fairly linear until mid- to late- October. Guess what, accuweather (see image below the curves) tells us that Phoenix had it’s daily low temperatures crash from 69 degrees to 54 degrees on October 26th.

The Rate of Deaths per 1000 – the Lagging Metric

Below is a different metric that might give us an insight. These are the top 8 states by Cumulative Deaths per 1000 persons. The initial states that were hit hard by COVID back in May experienced much more than their shares of deaths for reasons that are probably fairly obvious… the virus was new and these states were first up to bat. They made mistakes as well as breakthroughs in how a community would respond to this virus and that resulted in higher death rates. But note that after June their death rates flattened off or at least became linear. The Dakotas are a very interesting comparison, however. They experienced very few deaths during the first six or so months of the COVID pandemic but then saw pretty high death rates (which are still increasing at a fairly high rate) ever since. In just the last month or so, though, the northeast states have seen a transition from flat or linear death rates to non-linear. But the slope of the current increase is pretty low. So what might all this tell us?

1. I suspect many of the people who died in the Northeast during May and June contracted the disease before anti-COVID policies (Masks, Lockdowns, Improved Retail cleanliness policy, etc.) went into place.
2. I also imagine that ND and SD didn’t have a whole lot of COVID floating around early on. The weather was nice and people likely were outdoors, where evidence is showing that transmission is less likely.
3. I hear anecdotally that ND and SD had no official policy about Government COVID intervention. I haven’t checked this, but it is what I heard and that seems to make sense as those states have a more independent streak to them. So what we see on their death rates is what happens absent a defined policy. My suspicion is that like most other states, their first death wave is in the susceptible community of people who have susceptible immune systems.
4. Right now the death increases in the Northeastern states appear like they will be much less severe than their earlier deaths.
5. As the Dakotas’ case rates have already slowed down and are decelerating further, I presume that their surge is over for a while. At some point I’d imagine that their deaths would flatten off too.

Takeaways

1. Though the articles state that contact tracing data indicates that a high percentage of current cases stems to Thanksgiving gatherings, I can’t see any evidence of a surge of cases in any state that started after these holidays. What might this mean? First, as with any subjective human measurement and data collection system, I don’t think contact tracing is anywhere near 100% accurate. COVID is everywhere these days and there may real difficulty determining if new cases were acquired during a holiday meal (or if they were acquired at the grocery store, or the office, or the Starbucks that one stopped in at on their way to the gathering). Second, if Thanksgiving led to a surge and the existing transmission rates just before Thanksgiving held constant, then we would see it in an increase in the existing case acceleration. I think that would be a hard case to make looking at these curves.
2. COVID is very complex because it is interacting with a highly complex society. As such, attempting to find one causal reason for anything to do with COVID is probably going to be frustrating. That said, there does seem to be a strong correlation with temperature and COVID transitions to non-linear growth. I haven’t checked each one of these states (feel free to go off and check the others and report back!), but in the cases where I did, it seemed to be where there was a sharp fall in the nighttime temperatures.
3. The concept of seasonal outbreaks of influenza has been investigated for years, but recently there is consensus around the causality of temperature and humidity for influenza outbreaks (see paper from the Journal of Virology). The temperature number that the linked paper references as being ineffective for influenza transfer is 30 degrees Celsius (86 degrees F). The paper also states that influenza transmission is highly efficient at 5 degrees C (41 degrees F). I’m not aware of any top-notch papers on the effect of temperature or humidity on COVID, but the NIH has a nice summary of around 20 primarily non-peer-reviewed papers on the subject, most of which found that COVID has higher transmissability in colder weather and less humid conditions. One of the papers they summarize indicates that COVID survives and transmits most effectively between 13-19 degrees C (55 to 66 degrees F) and 50 to 80 percent humidity. This seems to line up nicely with the weather during the times where states transitioned from linear case rates to non-linear case rates. It would make sense that a healthier, happier virus would be more effective at infecting its targets (us!).
4. The Oxford Dictionary defines Confirmation Bias as “the tendency to interpret new evidence as confirmation of one’s existing beliefs or theories.” As such, the observations that most states were already in non-linear growth regions well before Thanksgiving and lack of any real evidence that any change in these acceleration rates occurred after Thanksgiving makes me qualify most of these articles about the Thanksgiving outbreaks as likely colored by confirmation bias (I’m sure we all saw lots of articles before Thanksgiving on how it would result in significant case surges). No one can prove that Thanksgiving DIDN’T create any increase in case growth, but there’s really no good evidence to indicate that it did.

Bonus – Top States by Case Acceleration and by Case Deceleration

Note that California has the highest case acceleration rate in the country. This means their IROC_Confirmed Case Slope (New Cases per 1000 residents per Day) will increase by .1211 or higher tomorrow. Note that North Dakota’s case acceleration is still decreasing and appears to be near to the point where they have just a handful of new cases per day.