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Early Spread of COVID19

Many tested negative for the flu in January and February. Could this have been COVID19?

Short answer: Yes.
Long answer…

We have to outline the first “original” cases:

• Jan 21-Feb 23: U.S. detected 14 COVID19 cases all related to travel from China

• Feb 26: 1st non-travel US case confirmed in CA (patient was ill starting Feb 13)

• Feb 28: 2nd non-travel US case popped up in WA

So, from this timeline, COVID19 started spreading in the United States on Feb 26 right? Nope.

CDC found that it was spreading earlier in the US by looking at four things:

1. Seattle Flu Study: Some scientists, during this time, just happened to be conducting a Seattle Flu Study. They were basically monitoring the flu from Nov 2018- March 2020 by testing people randomly. After the pandemic started, they went back to test these samples for COVID19. From Jan 1-Feb 20, none of their tests were positive. Their first sample was positive on Feb 21; the following week there were 8 positives; the following week there were 29 positives.

2. Gene analyses: Genes from early cases suggest that a virus imported directly or indirectly from China began circulating in the US between January 18 and February 9, followed by a COVID19 strain from Europe.

3. CDC has found other cases before Feb 26

• Jan 31: CA women became ill. Died 6 days later. She did not travel internationally. Postmortem, COVID19 positive.

• Feb 11: An infected passenger boarded the Grand Princess in Seattle leading to two outbreaks.

• Feb 13: CA man died at home. He did not travel internationally. Postmortem, COVID19 positive

4. Surveillance: ER records did NOT show an increase in visits for COVID19–like illness until February 28. CDC thinks this is because there were too few people with the disease to see an increase in ER visits in a meaningful way.


Translation? Community spread of the Chinese COVID19 strain likely started in January. Community spread of the European COVID19 strain started in Feb.


Why do we care? There are many reasons. One being that we can better estimate how many people truly died from COVID19 that were missed. We know from excess death analyses that we missed a lot in the beginning, meaning our current numbers are underreporting.


Love, your local epidemiologist

Data Source: Jorden MA, Rudman SL, et al. Evidence for Limited Early Spread of COVID-19 Within the United States, January–February 2020. MMWR Morb Mortal Wkly Rep 2020;69:680–684. DOI: http://dx.doi.org/10.15585/mmwr.mm6922e1external icon

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Leading Causes of Death

There’s no doubt that COVID19 will be a top 10 leading cause of death in 2020. This is quite impressive given that the other diseases on this list typically take YEARS to manifest and are NOT contagious.

The interesting question is… what will COVID19’s exact rank be at the end of the year? It’s been a while since I updated this chart. This graph always stirs up discussion, so I added some more sophisticated analyses to address concerns. Let’s see if I can explain it…

I estimated three COVID19 ranks for the US and 8 other states. I used COVID19 deaths (up to last night) and compared to 2019 causes of death.

Low estimate (green): This estimates COVID19’s rank if the pandemic ended yesterday (i.e. everyone with COVID19 was cured overnight). Unfortunately, we know this isn’t true, but this is the absolute MINIMUM rank COVID19 will be.  

Medium estimate (orange): This estimates COVID19’s rank if we continue on our death trajectory.

High estimate (red): This estimates COVID19’s rank if we continue on our trajectory AND we count ALL excess deaths as COVID19 deaths. I understand that, in reality, all excess deaths are not likely COVID19, but this is the HIGHEST rank COVID19 could be.

So, in the United States, COVID19 will lie between the 3rd (high estimate) and 6th (low estimate) leading cause of death in 2020. In reality, it will be somewhere in the middle. In March, we (epidemiologists) estimated it would be 3rd leading cause of death in 2020. Looks like that’s going to be about right.

In Texas, COVID19 will lie between the 3rd (high estimate), 5th (medium), and 9th (low estimate) leading cause of death. Again, it will likely fall somewhere in between by the end of the year.

For CA, FL, AZ, NY, LA, WA, and IL rankings, see the following graphs. This is all I could get done before my eyes started shutting last night.

Lately, the flu debate has come to surface again. In EVERY state, COVID19 lowest possible rank is still higher than flu. So, I’m not really sure why we are still having this conversation…

Love, your local epidemiologist

Note: Yes, the 2019 numbers will also change this year. But this is the best we got, as CDC only reports these in aggregate form at the end of the year. It will actually be interesting, though, how other ranks change. For example, we know car crashes (unintentional injuries) have decreased while suicide has increased.

Data Sources: 2019 data is from the National Center for Health Statistics at the CDC. COVID19 deaths is from Johns Hopkins (US). Excess deaths is from the Weinburg lab. Graphs/analyses by yours truly.

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Case Fatality Rates

On July 7, I posted five reasons as to why CFR may be decreasing while cases are increasing. One of which was lag time.

In other words, deaths today aren’t indicative of spread today, but rather a reflection of case severity 20-30 days ago. It’s been 27ish days since exponential growth started across several states. We should start seeing an uptick in CFR if this hypothesis is correct.

And we are. This is obvious in TX and CA. Doesn’t look like there is change in FL, AZ, or GA (yet). Given the spread among the younger population, this lag time may be even more than 30 days.

It’s still too early to see the impact of this recent uptick in TX and CA on cumulative CFR (Figure 2).

So, what’s causing this increase in TX and CA? Either we have reached hospital capacity (which we haven’t). OR COVID19’s reach is so wide it’s starting to reach vulnerable populations. OR we are increasingly testing those that are more sick (indicative of a high test positive rate). It’s likely a combination of the latter two. CFR is a difficult measurement because it’s highly dependent on the number cases we catch. For example, if we are only testing high risk populations (like nursing homes), the CFR will be high. It’s typically missing asymptomatic or mild cases that just never get tested.

Because of this, public health decision makers are starting to use Infection Fatality Rate (IFR). IFR estimates the fatality rate among those infected (detected AND undetected cases).

In the US, the CDC’s best IFR estimate is 0.65%. So, on average, 6.5 people of 1000 infected will die of COVID19. A recent publication pooled global IFR; IFR ranged between 0.53% and 0.82%. IFR is a more direct measure of disease severity, although highly dependent on place.

Understanding the true fatality rate has implications for public health planning. Unfortunately, if you thought the CFR was “low”, you are really not going to worry about 0.65% IFR. Given the reach of COVID19, this is still very much a leading cause of death in the US. The morbidity of COVID19 should still be of great concern too.

Love, your local epidemiologist

Data source: COVID19 tracking project. Graphs by yours truly.
Pooled IRC: https://www.medrxiv.org/conte…/10.1101/2020.05.03.20089854v4
CDC report: https://www.cdc.gov/coronavir…/…/hcp/planning-scenarios.html

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Cell Phone Tracking

Using cell phones to track movement.

Tracking the way in which humans moved before and during the pandemic has been a very innovative way in which epidemiologists have been able to describe (and predict) COVID19 spread. Specifically, many scientists are using cell phone data to track movement.

Yesterday, the Lancet (a highly reputable scientific journal) published a study in which they wanted to answer… HOW strong IS the relationship (i.e. correlation) between movement and COVID19 spread. Spoiler: VERY strong.

We can see this visually too. For example, as of today, there are 14 hot spot states. These states have very similar patterns in movement to non-essential businesses (Figures). The blue line indicates change in movement to non-essential buisnesses. For example…

In Texas, at the peak of the stay-at-home orders (April 8), there was a 70% reduction in movement to non-essential places. In other words, people moved 70% less to non-essential buissness than before the pandemic. Which was great; it worked to curve spread. However, since then, people have been moving more and more to non-essential businesses. In mid-June, Texans only moved 15% less than before the pandemic. This means they were almost back to “normal”. This was followed by exponential increase in COVID19 cases.

We see the same with AZ, FL, and CA (although CA is not as dramatic).

This is CA… forgot the label

As a comparison, I also included NY. Movement to non-essential businesses stayed constant for almost 2 months, then once cases were down, SLOWLY started to increase. The highest NY has gone is 55% reduction in movement. They haven’t even gotten close to the 15% reduction like we see in Texas.

Translation: Your movement to non-essential places MATTERS! We can all reduce our movement to keep this pandemic under control.

Love, your local epidemiologist

Sources:
Lancet study: https://www.thelancet.com/…/PIIS1473-3099(20)3055…/fulltext…

Mobility data and graphs: From UnaCast. A really fun site to play around with: https://www.unacast.com/covid…/social-distancing-scoreboard…

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COVID19 projections

Things are looking grim in Texas folks.

Here are COVID19 projections for each major county in Texas. The projections take into account social distancing, population density, testing capacity, and combined temperature and humidity lagged over the prior 14 days. Each county’s effects are standardized by population demographics.

Location is labeled at the bottom of each figure. Also, pay attention to the Y-axis, this is different for each map.

Translation: The ONLY way to change these projections is changing behavior. Or we all move out of state. We can’t change temperature and humidity.

I also included projections for other cities in the United States. Texas’ projections are NOT the way it HAS to be.

Love, your local epidemiologist

Data source: CHOP Policy Lab