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:


Behaviors Deaths

Superspreading Events (SSE)…

…are fascinating to me. Maybe because they are so difficult to predict, and therefore, challenging to prevent. They are also not new. In 1997, scientists estimated that 20% of the population contributes to 80% of transmission of infectious diseases. This has been seen with TB, measles, SARS, MERS, and Ebola. For example, during SARS, one hotel guest caused 4 national and international clusters. For Ebola, 3% of cases were responsible for 61% of infections.

Several SSEs have been described during the COVID19 pandemic (see my previous posts). Last month, there was another one at a Maine wedding. This wedding took place in Millinocket, Maine, a rural town with a population of 4,300. Before the wedding, there were NO cases of COVID19 in the town. The wedding turned into the state’s largest outbreak.

What happened? See figure. Briefly,

  • 65 people attended the wedding; 56 caught COVID19 (86% attack rate)
  • This has now spread to 270 people over 500 miles
  • 8 people have died, of which, none attended the wedding

Small events can add up to a lot. In fact, the “smallness” can cause a false sense of security. What does matter is… 1) how infectious that index case is; 2) how many close contacts the index case had at the event; 3) over what time period; 4) and where (indoor vs. outdoor).

A psychologist at Princeton recently stated, “When you live in a war zone, after a while, everyday risk becomes baseline. Our neurons are wired in such a way that we only respond to change. People have gotten used to being in this new state of danger, adapting to it, and therefore have not taken enough precaution anymore”. The wedding was inside. No one wore masks. No one social distanced. It exceeded Maine’s 50-person limit. This is yet another example of pandemic fatigue. Also, an example of the importance of coordinated and timely contract tracing, testing, and quarantining during a pandemic. Speed is essential.

Love, YLE

Data Sources:


20/80 rule:


Maine CDC briefing:

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:


COVID19 Death Rate

Typically reported as case fatality rate (CFR) because it’s easy to calculate (# of deaths/# of cases). CFR is around 3% in the US. In other words, 30 people of 1000 infected will die. However, we know that 3% is higher than the “real” death rate because we have mild or asymptomatic COVID19 infections. These people never get tested, and thus not included in CFR.

So, epidemiologists and policy makers use Incidence Fatality Rate (IRF). IFR estimates the death rate among detected AND undetected cases. COVID19 doesn’t make this easy to calculate. We need the “true” COVID19 infection rate in a community (which we don’t really know). So, we have relied IFR’s calculated from cruise ships or from mathematical stimulations.

Until now. A study was published on September 2.

Scientists in Indiana used statewide data and calculated an IFR of 0.26%. So, on average, 2.6 people of 1000 infected in Indiana will die. This number ranges dependent on age and other factors (see Figure). It’s important to note that this IFR excludes kids (less than 12 years old), prisoners, and nursing home cases and deaths.

In comparison,

• CDC’s best IFR estimate is 0.65% for ALL Americans. So, on average, 6.5 people of 1000 infected will die.

• The global COVID19 IFR ranges between 0.52% and 0.82%.

Why is this number so important? It puts the severity of this novel virus into perspective (at least in terms of mortality). The number impacts how we determine our public health response strategies. We are lucky that this number is relatively low. Rabies is 100%, ebola is 50%, smallpox is 30%, polio is 15%, chicken pox is 0.8%. The problem is that COVID19 is a sneaky little thing; it spreads way too easily without us knowing (unlike the more lethal diseases). This has resulted in a far COVID19 reach and, thus, 188,000 deaths in the US.

Love, YLE

P.S. Before you ask, the highly cited flu IFR estimate is 0.1%. This number is calculated from hospitalizations. It excludes asymptomatic people or people who never get tested (which is about 65%-85% of flu cases). So, 0.1% is typically considered high for the flu. Nonetheless, if we take these imperfect numbers, COVID19 is between 2.6-6.5 times more lethal than the flu.

Data Sources: Indiana Study: IFR:…/10.1101/2020.05.03.20089854v4CDC IFR:…/…/hcp/planning-scenarios.htmlFlu IFR:

California Deaths National changes Testing

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: icon

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 


Hand sanitizers

Alcohol (called “ethyl alcohol” on the back label) is different than methanol. Alcohol is good in hand sanitizers; methanol is bad.

COVID19 has a protective layer around it. Alcohol dissolves this outer layer of proteins and disrupts COVID19’s metabolism. This is good.

Methanol is a wood alcohol, which is typically used industrially as a solvent, pesticide and alternative fuel source. Methanol can also dissolve COVID19’s protective layer. BUT when methanol is absorbed through the skin it causes toxic effects: nausea, vomiting, headaches, blindness, seizures, hospitalizations, and death. Methanol should NOT be in hand sanitizers. However, lately, the FDA has found 115 hand sanitizers in the US to contain methanol (see link below for list).

The CDC just published a case study in Arizona and New Mexico, which shows the negative impact of ingesting methanol-based hand sanitizers.

Other tips:

• I can’t believe we have to say this, but please do not drink any hand sanitizers (and especially those with methanol)

• The FDA does not “approve” hand sanitizers. So, anything that says “FDA approved” should not be trusted

• Hand sanitizers can have less than 60% alcohol, so double check this number before buying. If something is labeled as 15%, this is the same thing as putting water on your hands.

• Some hand sanitizers are sold with false information, like “prolonged protection (up to 24-hours)”. This is an unproven claim. Do not believe it.

• After multiple uses, you can start diminishing the effectiveness of hand sanitizer (please use soap and water when you can)

• Hand sanitizers do expire (3 years after manufacture date)

Love, your local epidemiologist

Data Sources: CDC figure and study
The FDA list of methanol hand sanitizers can be found here. Here is the Spanish version.

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.

Children Daycare Deaths Innovative Solutions Long-term effects

COVID19 and child abuse

Thought I would sprinkle some of my own research in this blog because…why not?

For those of you that don’t know, I am a violence and injury epidemiologist. My research lab focuses on how violence is contagious (just like infectious diseases) and predictable. Because if it’s predictable, then it’s preventable.

As we ALL know by now, COVID19 has not only caused major medical problems in our community but has caused social problems. The strains and stresses of the COVID-19 pandemic (like job loss, financial struggles, food insecurity, mental health, and lack of social support) have exacerbated the risk of violence at home.

My colleagues and I are continually working to understand how the stay-at-home orders/school cancelled impacts child abuse.

Figure 1 shows the impact of COVID19 on child abuse hospital visits in 2020 compared to 2019. Briefly, we found less kids are going to the hospital for child abuse after stay at home orders compared to last year. Unfortunately, though, we hypothesize that this isn’t because child abuse is getting better, but rather because kids are interacting less with mandatory reporters (i.e. teachers, daycare teachers) and the public.

Among kids that ARE going to the hospital for child abuse, physicians are reporting even MORE severe injuries (traumatic brain injuries, intentional burns) than before the pandemic. Typically, a hospital system has 5-10 child abuse deaths per year. It is not uncommon for a hospital to now report 2 child abuse deaths in one week.

The realities of this crisis are immediate. Researchers and clinicians are working hard to urgently address this public health crisis in real-time. 

Prevention can also start at home. The Prevent Child Abuse America posted some fantastic resources for parents, children, educators and everyone else. This includes tips for staying connected to the community, tips for staying engaged as a family, and tips to manage stress and anxiety. Check it out:

These efforts are especially relevant given that a lot of schools are delaying in-person school. While this delay is desperately needed medically, it will have an impact on kids’ health and safety. 

Love, your local (violence) epidemiologist

Source: Data comes from my lab in which we are working directly with pediatric hospitals. Data is not published; this is only a high-level preliminary report. We are working on it!