Deaths FL Hospitalizations National changes Social Distancing Testing Texas update

Thanksgiving Surge?

It’s been 2 weeks since Thanksgiving and I was curious if we had a “surge upon a surge”.

In other words, did our acceleration (the rate of cases) change after Thanksgiving? Unfortunately, it’s a very simple question with a very complicated answer. If this blog were my day job, I could statistically figure this out. But it’s not, so I triangulated a few other data sources instead. This is what I found…


TPR is now 20.3% in the United States. It increased 15% since Thanksgiving. TPR is particularly concerning in the Southeast, where it’s increasing at higher rates since Thanksgiving than the rest of the country. While the Midwest finally seems to be moving past their peaks, their TPR’s are increasing again (likely related to Thanksgiving), which will slow their decline. The WHO has stated that countries need a TPR below 5%. While testing does not have a direct benefit because there is no cure, there are a number of indirect benefits: 1) public health officials know the “true” rate of infection and can deploy resources to the right areas to stop spread; 2) psychologically if someone tests positive then they are more likely to quarantine (hopefully).


Cases have increased 21% since Thanksgiving. Cases increased 22% two weeks before Thanksgiving. New hot spots have popped up since Thanksgiving, particularly along the Sun Belt (southern CA, AZ, TX) and the Northeast. Boston, in particular, has surpassed 100 daily cases per 100,000. And while Vermont and Maine have been more than impressive this entire pandemic, they too are seeing doubling rates.


Not enough time has passed since Thanksgiving to see the impact on hospitalizations/deaths. But there is no reason to believe they will not continue to mirror case trends. Fatality rate (number dead out of the number with positive COVID19 tests) continues to remain steady in the United States at ~1.9%. We should continue to see this, unless out health systems are strained too much. Then hospitals will have to start making hard decisions on who to save and who not to save. In April, Italy had to make these decisions (they decided not to treat those 80+ years) and fatality rate increased.


  1. Airports. The CHOP Policy Lab found circumstantial evidence that the most concerning areas of the country post-Thanksgiving are adjacent to our busiest airports: Los Angeles, Boston, DC, Atlanta, and Dallas. In other words, Thanksgiving air travel led to increased local transmission. For example, in Clayton County, Georgia (home to the Delta Hub and Atlanta airport), cases are doubling compared to surrounding counties.
  2. Distance Traveled. Interestingly, distance traveled did not change, on average, by much. We see an increase right before Thanksgiving, but honestly not as high as I would have expected. This only means people, on average, didn’t travel far. This doesn’t mean that family wasn’t close by and people didn’t get together. It was also very obvious that distance traveled varied by states too (see Figures). Wish I had more time to look into this. But still adds a little piece to the puzzle.

Conclusion: Right before Thanksgiving we were starting to see a plateau in cases. Then, after data reporting caught up, our cases continued exponentially increasing after Thanksgiving. So, I don’t think we saw a surge upon a surge. But we definitely didn’t stop our original surge. The pandemic continues to ravage our communities across the United States.

Love, YLE

Data Sources: I triangulated many sources of data for this report. I couldn’t have done it without the beautifully clean and workable data and graphs from the following sites:

COVID19 Tracking Project:

CHOP Policy Lab:


Deaths Drug treatments Hospitalizations

COVID-19 drug update!

Alright, here we go! You may (or may not) remember that on August 4 some brilliant scientists created a “live meta-analysis” for COVID19 drug treatments. They basically want to know which drugs are effective and not effective. Well, their findings have been updated!!

What is a meta-analysis? One massive study that combines the results of ALL previous studies. This is a really powerful tool because it takes into account whether past studies were “strong” or “weak”. It also takes into account different populations (think different genes, different environments, different cultures, different confounders). Basically, this allows us to get an overarching idea of what is working and what is not working. This meta-analysis ONLY includes RCTs- the gold standard for epidemiological drug studies.

What is a “live” meta analysis? The scientists proposed that as new studies come in, their meta-analysis would be automatically updated. This has NEVER been done before (but is such a brilliant idea). AND they updated their analysis for the first time!

What is new in this update? They added 12 new randomized control trials (RCTs) from the last iteration, making the total number of pooled studies= 27 (which is 11,006 people).

Did this update change our understanding of anything? No. It’s “only” strengthened our understanding of effective (and not effective) drugs:

  1. Glucocorticoids was the ONLY intervention that reduced mortality and the need for mechanical ventilation
  2. Remdesivir reduces duration of symptoms and probably does not increase adverse effects
  3. Hydroxychloroquine does not reduce risk of death or mechanical ventilation (is this still a point of contention?)

This live meta-analysis will continue to update for 2 years. And there are already 6 new RCT’s in queue for the next iteration.

They have an interactive tool on the website, that I highly suggest playing around with too!

Love, YLE

Data source:

Children Hospitalizations

COVID19 vs. the flu among kiddos

We know that symptomatic COVID19 infections are rare among kids (compared to adults). However, there has yet to be a study comparing rates of hospitalizations, ICU admission, and ventilator use among kids with COVID19 compared to kids with the seasonal flu.

Yesterday, a paper was published in JAMA (highly reputable journal) comparing the two. Scientists used healthcare records from one pediatric hospital system in Washington, DC. They compared 315 kids diagnosed with COVID19 between March 25-May 15, 2020 to 1,402 kids diagnosed with the flu between October 1, 2019-June 6, 2020.

What did they find?

  • Kids with COVID19 had similar hospitalization rates (17% vs. 21%), similar ICU rates (6% vs. 7%), and similar ventilator use compared to kids with the flu (3% vs. 2%)  
  • Kids with COVID19 had more fevers, diarrhea/vomiting, headaches, body aches, and chest pain compared to kids with the flu
  • Among COVID19 hospitalizations, more kids were older than 15 years or had underlying medical conditions compared to flu hospitalizations
  • 0 kids were hospitalized with BOTH COVID19 and flu
  • 0 COVID19 deaths and 2 flu deaths

THIS is an interesting paper. It will be very useful for our clinicians (and maybe parents) this Fall to know how signs and symptoms may differ between the two respiratory infections. This can possibly help guide clinicians for the prompt identification and treatment of each.

This is also a beacon of good news for our kids and COVID19! But, it’s important to note that we still don’t know a lot about COVID19 among kids, like long-term outcomes and transmission rates from kid-to-kid and kid-to-adult. So proceed with caution.

It’s also important to note that we have a flu vaccine (hint, hint). Our hope is that more kids (and adults) will get it this year so we can avoid trying to differentiate and diagnose the two diseases at the same time.

Love, YLE

Data Source:

Hospitalizations Leading Cause of Death Long-term effects National changes


In February 2020, the WHO reported that we’re not only fighting COVID-19, but also an infodemic. “An overabundance of information—some accurate and some not—that makes it hard for people to find trustworthy sources and reliable guidance when they need it.” Information overload.

Which can (and does) cause anxiety, even if the information is true. The problem is if people get the wrong information from unreliable sources we are going to have a hard time stopping this virus. And we are in the United States.

In fact, scientists just published an article showing how the infodemic (and specifically misinformation) has impacted mortality, public health interventions, and treatment. They examined rumors, stigma and conspiracy theories circulating on social media between December-April 2020.

What did they find?
• Misinformation was present in 87 countries and 25 languages
• Of this misinformation, 89% were rumors, 8% were conspiracy theories, and 4% were stigma
• 24% of claims had to do with transmission and mortality; 21% public health interventions; 19% treatment and cure; 15% origin of the disease
• Countries with the highest rate of misinformation (in order): India; United States; China; Spain; UK

Their conclusions?
• Misinformation can have severe implications on public health if prioritized over science
• “Health agencies must track misinformation associated with COVID19 in real-time, and engage stakeholders to debunk misinformation”

Love, your local epidemiologist

Data Source: Islam et al., (2020) COVID19 related infodemic and its impact on public health: A global social media analysis. Am. J. Top. Med. Hyg. 

Children Deaths Hospitalizations

Hospitalizations among kids

Not groundbreaking, BUT really important for physicians and parents to make data-informed decisions.

The majority of childhood COVID19 cases are asymptomatic or mild. For that reason, we really haven’t known what happens to kids that DO get hospitalized due to COVID19. Are they hospitalized at a similar rate as adults? What are their symptoms? What percent have underlying conditions? CDC published a report today! Here are the numbers:

Hospitalization rate: 8 kids per 100,000 are hospitalized due to COVID19. This is MUCH lower than the adult hospitalization rate (165 per 100,000). Kids younger than 2 years old (25 per 100,000) had the highest rate of hospitalization compared to kids older than 2.

ICU rate: 33% of hospitalized kids got sent to the ICU.

Ventilation rate: 6% of kids in ICU required a ventilator.

Underlying conditions: 43% of hospitalized kids had an underlying medical condition. The most common condition was obesity, lung disease, and prematurity.

Symptoms: 54% of hospitalized kids had fever/chills, followed by nausea/vomiting, abdominal pain, or diarrhea (42%).

Death rate: 1% of children died during hospitalization. This is compared to 29% of hospitalized adults.

Translation? Children can (and do) develop severe COVID19 illness, but the vast majority survive to hospital discharge. Hospitalization rates have increased among kids since March, which is indicative of the virus’ incredible reach right now.

Love, your local epidemiologist

Data source: Kim et al. (August 7, 2020). Hospitalization Rates and Characteristics of Children Aged <18 Years Hospitalized with Laboratory-Confirmed COVID-19 — COVID-NET, 14 States, March 1–July 25, 2020. MMWR 

Deaths Hospitalizations Innovative Solutions

Meta-Analysis: COVID19 drug treatment

Alright, now we are talking! I knew this had to be coming out soon… A meta-analysis on COVID19 drug treatments (yes, including hydroxychloroquine).

What is a meta-analysis? One massive study that combines the results of ALL previous studies. This is a really powerful tool because it takes into account whether past studies were “strong” or “weak”. It also takes into account different populations (think different genes, different environments, different cultures, different confounders). Basically, this allows us to get an overarching idea of what is working and what is not working.

Why aren’t more meta-analyses done? It takes a LOT of time to do this. Typically years (sorry graduate students). We also need “enough” studies on one topic so we can combine them.

Fortunately, with the onslaught of COVID19 publications, we already have enough COVID19 drug treatment studies that we can do a meta-analysis.

This was published over the weekend. These scientists combined ALL randomized control studies (the gold standard) on COVID19 drug treatments. They looked at which drugs impact mortality, ventilation use, and symptoms by combining 23 studies. 

What did they find?

  1. Glucocorticoids was the ONLY intervention that reduced mortality and the need for mechanical ventilation
  2. The effects of hydroxychloroquine, remdesivir, and lopinavir-ritonavir is highly uncertain because studies were small, have serious imprecision, and have concerning limitations (like lack of double-blinding)

Considering this, though, authors concluded…

  • Hydroxychloroquine, remdesivir, and lopinavir-ritonavir MIGHT reduce symptom duration
  • Hydroxychloroquine might INCREASE the risk of adverse events when coupled with other interventions
  • Remdesivir might NOT increase the risk of adverse events

Translation? We need more studies. Specifically ones with more than 100 patients. Not very exciting, I know, but a stark reality of science. The science is not strong enough to say that, as a population, we can use anything but glucocorticoids to improve COVID19.

Next Steps? The even cooler thing about this publication is that the scientists created a “living” meta-analysis. I’ve never seen this before. As new studies come in, this analysis will be automatically adjusted each time. Giving us a better and better picture of COVID19 drug treatments in real-time. 9 studies are already in the queue to be included in the next update. Check out the study link for their visualization.

Love, your local epidemiologist

Data source: Table and meta-analysis conducted by the brilliant: Siemieniuk et al. (2020). Drug Treatments for Covid-19: Living Systematic Review and Network Meta-Analysis. BMJ.

Deaths Hospitalizations National changes Testing


Well, July wasn’t pretty…either.

In order to get the best comprehensive picture, I triangulated several constructs:

Cases (i.e. incidence): In the month of July, 23 states jumped to a higher CDC COVID19 risk category. For example, OK jumped from orange (13.7 daily cases per 100,000) to red (26.8 daily cases per 100,000). Shout out to VT…the only state that got better (jumped from yellow to green). VT is the first state to make it to the green risk category.

Deaths: Because of increased incidence, cumulative and daily deaths have increased in July for the majority of states. The figure includes daily deaths July 31 compared to July 1. NJ is looking good! CFR or IFR are incredibly difficult to estimate (and take a lot of time), so I didn’t include. See earlier posts:;

Testing (i.e. test positivity rate [TPR]): 41 states look like they have testing under control (under 10% TPR). Although we really need to get this to below 5%. There are 10 states that need some serious help (over 10% TPR). We are looking at you AL, AZ, FL, GA, ID, LA, NE, SC, TX, and VA.

Hospitalizations: Not even going to try to compare July 31 to July 1 because of the data reporting switch. But hospitalizations did increase in July. We know this because incidence and deaths increase.

The good news is that it LOOKS like some states may have recently (like past two days) reached their peak. If you live in one of these states, DO NOT CHANGE A DARN THING. We need to be well down the curve to start opening strategically and changing individual behaviors.

Maybe August will be our month?? Here’s the BEST peer-reviewed scientific article of how to get out of this mess by October.

Love, your local epidemiologist

Data sources: Analysis and graphs (except the first one) by yours truly. Data came from many sources: COVID19 tracking project, Harvard , and CDC.

Hospitalizations Texas update

Texas Hospital Capacity

Texas hospital capacity.

Here is the updated data. Both per capita (COVID19 hospitalizations per population) and capacity per TSA region.

It’s been 9 days since my first hospital capacity post (June 28) and we can see dramatic differences in numbers (look at the orange in Figure 2 compared to Figure 3). This can change FAST.

As a reminder…
-COVID19 (for hospital data) is defined as confirmed and probable (see previous posts for the definition of probable)
-A hospital bed can only be “avaliable” if staffed
-Capacity INCLUDES open and staffed surge units. So we will likely never reach “100%”, unless we run out of stadiums or staff first. However, given that capacity is over 90% AFTER including surge units is… interesting
-Including surge units also probably explains why we DONT see changes in orange in some places, like DFW. This is likely due to surge units opening up at the same rate as COVID19 hospitalizations, NOT because hospitalizations have remained steady over the past 9 days.
-We are updating our dashboard to include this graph (along with other things). You will have this data soon (and I don’t have to keep re-posting updates).

Love, your local epidemiologist

Data Sources: DSHS, figures by me

Hospitalizations Texas update

Hospital Capacity

Texas hospital capacity.

Figure 1 are the official numbers reported to the state. There are several Texas regions over 80% occupancy: Dallas, Houston, Laredo, and RGV. But they seem to be holding their own since my last post. So, looking good… right?

Well, it’s really important that we keep in mind that hospital capacity is a moving target:

1) Hospitals have been opening surge units. In other words, they have been increasing their capacity. If we don’t account for this surge increase, we will never reach “100% capacity” and hospital numbers will continue to be around 80% (and looking great), when in fact, numbers are NOT “normal”;

2) Hospitals can ONLY report available beds to the state if they ARE staffed. So, if a staff member gets sick or takes a vacation (which they should!), the total number of beds will DECREASE for that day.

That brings us to Figure 2. I generated a new category “surge units”. This category accounts for the two moving targets since June 21. By doing this, we can see the impact of opening surge units. Some regions would be in BIG trouble if they didn’t. If the Houston region didn’t open surge units, they would be OVER 90% capacity. Even worse, the San Antonio region wouldn’t have any beds. In fact, they would be short 350 staffed beds.

Translation: Keep in mind these moving targets in mind when these numbers are reported by media.

Love, your local epidemiologist

PS. A few notes:
1. I realize Figure 2 only includes a few select TSAs. This is because I only recorded June 21 data for a certain number of TSAs (and the state does not make historic hospital data available). I’m kicking myself.
2. Figure 3 is TSA region. Texas is so large that epidemiologists use these regions to describe patterns. Counties are within TSA regions.
3. I know a lot of you are interested in ICU capacity. While I have the # of COVID patients in ICU, I do NOT have the total number of ICU beds in each region. So I cannot calculate ICU capacity in each region.

Hospitalizations Texas update


COVID19 hospitalizations and hospital capacity across Texas. Updated my graphs from a few days ago. These are confirmed COVID19 hospitalizations (not presumed).

There are some regions that are doing great! But others not so much. I would still be worried about RGV, San Antonio, Houston, and Dallas.

With the opening surge units, hospitals are doing a fantastic job of keeping bed capacity around 80%. This is just in terms of # of beds. We don’t have data about staffing.

Many (I mean many) of you had the great suggestion of adding hospitalization data to our dashboard. And we are…to an extent. Hospitals are keeping their data very close to their chests. And I would too. They have been operating in the red zone for the past 3 months. I was on a call last week in which one hospital system said they are losing $500 million per DAY. People are avoiding hospitals so they aren’t exposed to COVID19. However, some NEED to be going to the hospital and are not. This will have grave consequences on their overall health (and financial implications for hospital systems).

Love, your local epidemiologist