Wednesday COVID-19 Briefing

Special Note: I’m away this weekend. The next briefing will be next Wednesday. Send me your COVID-19 questions while I am gone.


Top news, reports and insights for today:

  1. Curated headline summaries for Wednesday:
  • Two recent peer-reviewed studies show there has been a “sharp drop” in the mortality rate among patients hospitalized for COVID-19 and those with pre-existing conditions. The main drivers of this decline appear to be standardized treatment protocols, better control of blood clots, and because mask wearing means people are acquiring less severe infections. Although good news, the CDC says hospitalized COVID-19 patients are still more than 5-times more likely to die than those hospitalized with influenza (Axios)
  • New survey shows that while COVID-19 test results are coming back faster than in the Spring, results are still too slow to optimize contact tracing and infection control procedures (2.7 days on average in September). Half of those who test positive are never contacted about who they might have exposed (NPR)
  • The World Health Organization (WHO) releases preliminary results of the largest randomized clinical trial in the world to study existing drugs for treating COVID-19 in the hospital. The results indicate that remdesivir, hydroxychloroquine, lopinavir/ritonavir and interferon regimens appeared to have little or no effect on 28-day mortality or the in-hospital course of COVID-19 among hospitalized patients. Many are now criticizing the details and design of the trial (WHO News)
  • Residents were tested in skilled nursing facilities (SNFs) in 20 states. Of those found to be infected, 41% had no symptoms (asymptomatic) and another 19% were presymptomatic (developed symptoms after testing). Many believe nursing home residents, being more vulnerable, are much less likely to have asymptomatic infections. This study shows that is wrong and tells us why screening nursing homes for COVID-19 is so important (JAMA Internal Medicine)
  • A study not yet peer reviewed looked at the impact of temperature and humidity on the viability of SARS-CoV-2 virus indoors. The virus survives better when it is colder and humidity is extreme. Compared to the heat of summer, the virus remains viable on a plastic surface more than 10 times longer when it is cold and dry. If confirmed, this adds to concerns that infections will significantly intensify in the winter (BIORXIV)
  1. U.S. daily cases continue to surge; 7 states remain “white hot”. Putting recent case surges into context requires juggling at least 4 other balls
      Good disease detectives must simultaneously consider multiple intersecting clues to understand the dynamics at play. It’s a scientific case of keeping multiple balls in the air (I do love juggling analogies). As the epidemic moves along, keeping a cool head gets harder, not easier. Today’s briefing is a case in point. Consider our old friend, Figure A, which shows continuing surges in cases. We aren’t there yet, but daily cases are heading back to the previous swell in July; the 7-day moving average peeks over the 55,000 mark. These numbers are alarming, especially in a handful of states where new daily cases are over 40 per 100,000 a day (Figure B): Idaho (43), Montana (60), Kansas (44), North Dakota (an astonishing 102), Nebraska (43), South Dakota (77) and Wisconsin (57). The states currently under control (<5 cases per day per 100K) are now down to three (Maryland, Maine and Vermont).
     To put these numbers into context however, a good disease detective pays equal attention to the other balls aloft. Here are two really important figures that help us to make sense of this. Figure C is my modification of a graph from the COVID Tracking Project showing two weekly average curves: one for daily cases (red) and one for currently hospitalized (blue). In my opinion, there are three distinct epochs, or periods in this figure, shown here as green boxes I added. Period I from late March through mid-June was characterized by twice as many people in the hospital as there were lab-confirmed cases. How could there be more in hospital than cases? Some of this is lag time. But mostly it is because we were just testing people who came to the hospital with symptoms and were already sick. During this period, there were probably 50-500 times more actual cases than we knew about. Our testing fraction was really low given the imaturity of the testing regime. During Period II from July 1 till the end of August, daily cases and hospitalized patients were tracking closely. Our testing had broadened over the summer and we were capturing a bigger fraction of total cases. The current phase (Period III) started in the middle of September, and saw, for the first time, daily cases exceed hospitalizations. This shows our testing is maturing further. The main point here is to notice that during Period II, the case curve is now rising faster than the number of hospitalized patients.
     To make further sense of this, we have to consider two more flying balls. The first is depicted in Figure D showing daily U.S. tests. On top of the CTP data I have imposed the same three “periods”. During Period I, testing ramped up steadily from 100,000 a day to half a million. While that was real progress, we never got close to the critical mass needed to shed light on population dynamics. During Period II, testing rose in July but flattened in August and actually went down for a few weeks. Who could blame us for letting our foot off the gas pedal during the summer when so many people thought we had rounded a corner. Thankfully, we have reversed that trend in Period III. Since September 11, we have boosted testing from 750,000 a day to over a million. When combined with stability in daily deaths (the final ball we are juggling) the overall rise in cases is neither surprising or as profoundly concerning on it’s own as would be the case 6 months ago.
    The bottom line: U.S. cases are on the rise again, spiking especially in seven hot spot states in the Midwest and West. The rise in cases must be seen in the context of rising testing, hospitalization rates, test positivity and mortality. Increasingly, the key barometers of progress against this disease will be the latter metrics. Thankfully deaths remain stable, test positivity is only nudging higher and hospitalizations are increasing in hot spot states, but not as fast as cases. Tracking an epidemic is about juggling, not staring too hard at any one ball.
Figure A
Figure B
Figure C: Daily cases and currently hospitalized in the U.S. (COVID Tracking Project) and three hypothesized periods (green boxes)
Figure D: Daily testing in the U.S. (COVID Tracking Project) and three hypothesized periods (green boxes)
  1. The holidays are coming and it’s time to plan. Is it a good idea to have a family gathering?
     If you are like me, you are thinking about the holidays and wondering if and how to have family gatherings. I don’t have the answer for you, but as I think through things for myself, I can tell you what factors are on my mind. As an epidemiologist and disease detective, I look at family gatherings as risky situations during an epidemic. Infectious disease transmission is all about mixing. How does the virus get from transmitters to those who are susceptible? The purpose of infection control measures is to limit mixing. Social gatherings like weddings, funerals, graduations, and holiday gatherings, involve an especially hazardous kind of mixing because many bubbles interact, exchange pathogenic companions, and then disperse, bringing newly acquired organisms back home. Family gatherings tend to be multi-generational, mixing older or more vulnerable family and friends with young people who themselves do more mixing (have bigger bubbles) and think themselves low risk.
     It is increasingly clear that the recent surge in U.S. cases is being driven by infections in young people that spill over to more vulnerable groups. This, in turn, is driven by students returning to school, pandemic fatigue, and rebellion and distrust among those who perceive themselves to be low risk. A series of reports in the CDC’s MMWR are worth highlighting here. The first comes from an October 16 report looking at changes in test positivity in 767 hot spot U.S. counties in June and July. Overall, it shows the biggest jump in the percentage of tests that were positive before and after the hot spot was detected among the 18-24 year old age group (Figure E). More so than other age groups, test positivity rose in young people prior to the onset hot spots where older people got sick and died. The jump in TPR was much more pronounced in the Midwest and South, the regions that have been on fire in recent months (Figure F). Together, these data strengthen the case that young people are driving outbreaks even though they themselves are not as likely to get severely ill or die. It also shows the social distancing message is not getting through in the Midwest and South.
     The second MMRW from October 6 interviewed young adults in July after a series of outbreaks in Winnebago County Wisconsin, now a super hot spot state. Researchers found young people feel peer pressure not to wear masks and get “odd looks” if they do. Most perceive themselves to be low risk for severe disease but worry about passing the disease to loved ones. Most have received confusing messages and misinformation about the virus. Young people say they wear masks in public or at work but also admit to attending social gatherings with peers where they feel pressure not to mask and distance.
     A third MMWR report from October 9 is about a 3-week family gathering this summer during which a 13 year old girl (who was previously infected but had a negative test) passed the disease to 11 other family members (Figure G). Take home message from this case: 1) a family gathering attended by a child with a runny nose led to 11 secondary infections and an unknown number of tertiary infections once those people returned home. 2) All the secondary infections happened in those who shared the same house and did not wear masks. Other family members who stayed outside and wore masks remained uninfected. 3) The index case came to the event with a negative test that was wrong. This highlights the importance of following up antigen tests with PCR tests and why a 14-day quarantine period is critical for those who have symptoms.
    The bottom line: a series of MMWR reports shed further light on how young people are driving the epidemic. Family gatherings are inherently risky if they connect many bubbles across generations. If you are planning holiday family gatherings, wearing masks, sanitation procedures, good ventilation and staying outdoors will lower the risk. Ultimately, keeping our distance is inherently harder with loved ones when celebrating special occasions.
Figure D: From https://www.cdc.gov/mmwr/volumes/69/wr/mm6941e1.htm#F1_down
Figure E: From MMWR, https://www.cdc.gov/mmwr/volumes/69/wr/mm6941e1.htm#F1_down
Figure F: MMWR: https://www.cdc.gov/mmwr/volumes/69/wr/mm6940e2.htm
  1. Quirky Qorner: Keeping score on the American spirit: Wienermobile 1, Coronavirus pandemic 0!
     I am not sure I would call this a “feel good” story given how I feel after eating them, but let’s put one in the win column for the American spirit. Oscar Mayer has been sending a fleet of Wienermobiles across the country promoting their meat tubes since 1987. Actually, thanks to a delicious story I found in the New York Times, the mobile meal actually began rolling down the streets of Chicago in 1936 during another time of national calamity: the Great Depression. When the pandemic exploded, the company was forced to rest their rambling red-hots for the first time in 33 years. In a testament to the resilience of the American spirit, the company announced in August they would send their fleet of 6 mobile mouth pleasers back on the roads, albeit with some additional safety modifications. Over 7,000 people applied for twelve positions piloting the mechanical meat mobiles. Other fun facts about the “Weenie-bagos”: a) no, they don’t actually serve hot dogs, and b) no, there is no bathroom in the “back” of the bun. Groan!
From: https://www.nytimes.com/2020/10/20/style/inside-oscar-mayer-wienermobile.html

Weekend COVID-19 Briefing


Top news, reports and insights for today:

  1. Curated headline summaries for Saturday/Sunday:
  • The U.S. Centers for Disease Control and Prevention (CDC) on Saturday reported more than 8 million confirmed COVID-19 cases and 217,918 deaths (Reuters)
  • A small randomized trial shows that the Chinese coronavirus vaccine is safe and does elicit an antibody response. This is a key preliminary stepping stone to showing that the vaccine prevents COVID-19 in the population (Lancet)
  • Ukrainian fitness guru, Instagram influencer, who thought COVID-19 was a hoax, dies of the virus (Daily Mail)
  • In an open letter, more than 1,000 current and former CDC officers criticize U.S. COVID-19 response, decried political interference and called for the CDC to have a larger role (Wall Street Journal)
  • CNN anchor Jake Tapper says the White House refused his invitation to allow coronavirus experts including Anthony Fauci and Robert Redfield to appear on his show (Axios)
  • Rural Midwest hospitals struggling to handle COVID-19 surge (ABC news)
  • Pharmaceutical giant Pfizer delivers “final blow” to President Trump’s hope for a pre-election vaccine. The company, considered a frontrunner in the vaccine race, says it won’t seek an emergency use authorization (EUA) for it’s candidate vaccine before late November (Politico)
  1. U.S. daily COVID-19 cases return to near explosive growth. Deaths stay steady
     The end of this week saw a continuation of a trend toward sharp escalation in daily U.S. COVID-19 cases. Thursday and Friday numbers spiked over 66,000, a number not seen since the end of July (Figure A). The rate of increase is not as steep as the explosive growth seen from June 22 to July 15, but it is steeper than it has been since then. Record high daily case totals were broken on Thursday in Montana (723), Michigan (2,030) and Ohio (2,069) as well as on Friday in Idaho (1,094), New Mexico (812), Wyoming (248), Indiana (2,283), Minnesota (2,280), North Dakota (864), Nebraska (1,286), Wisconsin (3,861) and West Virginia (358). In just 2 days, 12 states recorded more cases than any other day in the outbreak making this one of the most disconcerting days thus far. That brings the total number of states with 100,000 lab-confirmed cases to 26, half of all states. Of those, six states now have at least a quarter of a million cases and three, California (864,000+), Florida (751,000+) and Texas (822,000+) now exceed a three quarters of a million. To put this in perspective, California and Texas would now rank in the top ten most impacted countries with more cases than the entire United Kingdom and about as many cases as Peru, Mexico and France.
     Thankfully, there is little indication yet that the trend in cases over the last month is matched by a trend toward rising deaths (Figure B). The 7-day moving average has been steady between 600-800 a week since September. Based on what we know about the time lag between cases and deaths, most experts expect deaths to begin to rise in the coming days or weeks. In my opinion, that expectation may be tempered by continual improvement in medical management strategies that have gradually lowered the lethality of the disease. However, a major caveat is that there will be an inflection point as the cases rise where the capacity of the hospital system to maintain the highest standard of care will be jeopardized. We are already seeing signs of that in states like South Dakota and Wisconsin (see headline 6). Death rates will soar if thresholds are exceeded in the coming months as most experts now predict.
     The bottom line: Daily cases are now spiking at a rate not seen since the summer crush. The U.S. has now passed 8 million cases, the most of any nation and more than a quarter of the 40 million cases world-wide. The incidence of COVID-19 is now 5 times higher in this country (25,171 cases per 1 million population) than for the entire planet. In my view, this is not a second wave and not a third wave, but another wavelet within the tidal undulation of the first epidemic wave of COVID-19 in the United States. The prognosis is not good heading into colder temperatures, lowered humidity, more time spent indoors and continuing social and economic pressure to return to “normal”. The march of suffering and death continues apace and the U.S. still refuses to do what must be done to quell this viral firestorm.
Figure A
Figure B
  1. Why do U.S. COVID-19 death totals differ wildly across sites tracking the pandemic? The disease detective goes to work.
      Above I reported the daily epidemiologic curves for U.S. cases and deaths as I always do using data compiled by Wikipedia. No data source is perfect. There is, I have argued, a big advantage to sticking with the same data source over time despite its limitations if the primary purpose of surveillance is to track patterns of change over time. That having been said, I am increasingly uncomfortable with my choice to stick with Wikipedia in light of growing discrepancies between this source and most of the high-visibility alternatives. Figure C shows you the current total U.S. COVID-19 deaths from my source (Wikipedia, medical cases) and 7 others. I won’t call these independent data sources because they are all tapping into the same underlying data streams coming from official state reporting agencies. I’ll call them data compilers instead. In theory, given the common data sources, you would think the grand totals should very similar. The first thing to say about Figure C is that no two of these compilers arrive at the same total as of October 16. The highest is from WORLDOMETER and the lowest is from Wikipedia. The average (median in this case) is just over 218,200 as of today (red line). Worldometer is reporting 17,952 more U.S. deaths than Wikipedia. That’s a big difference. As disease (and data) detectives, let’s dig into these numbers and see if we can figure out why.
     What we know based on detailed analysis of excess all-cause mortality is that about 68% of the actual COVID-19 deaths are being correctly attributed to the coronavirus. Based on that, one could assume that the compiler with the largest number is likely to be closest to correct since it’s much less likely that deaths are being incorrectly attributed to COVID-19 than they are being missed. So for this exercise, we will focus on the high vs. low estimate.
     After digging into the details, I learned that the Worldometer (WO after) is highest in part because they don’t rely just on numbers compiled from state agencies, but include also deaths among “U.S. military” abroad (100), and a category labeled “Veterans affairs” (3,711), Federal prisons (128) and Navajo Nation (571) along with three deaths from the Grand Princess ship. That accounts for about 4,000 of the deaths not reported in Wikipedia.
     Next, what states are responsible for the discrepancy? Figure D is my picture after a careful comparison between these two compiler. Aha! We now know that the two sites are in perfect agreement in about one third of states (N=15). About half of states are plus or minus five (N=27). The bigger discrepancies come mostly from a few select states. Wikipedia has more deaths in ten states (only 2 are more than 10 more), and most of these are because the Wikipedia numbers include the deaths from Saturday. Of the 23 states where the WO count is higher, 13 of them are more than 100 apart. The real story is the two numbers that really pop out: New Jersey (+1,917) and New York (+7,814). Three quarters of the “excess” COVID-19 deaths in the WO data are in these two states with 62 percent just from New York. Why are there almost 8 thousand deaths in WO that aren’t in Wikipedia? The difference arises from where the compiler gets the information. Wikipedia uses numbers from the New York State Department of Health COVID19 tracker while WO uses Johns Hopkins numbers. The Hopkins dashboard says they get their New York numbers from the Governor’s coronavirus newsroom. There is no hint there as to why the two data sources differ so markedly. This disease detective found an important clue on the WO’s page describing the data where there is a very helpful and instructive footnote about U.S. data. This footnote announces a change in the death definition on April 14 to include “probable deaths” following updated CDC guidelines. Have a look at Figure E which is a screen capture of the part of that memo that relates to reporting in New York. The plot thickens!!! It tells us that the New York Department of Health has, for unknown reasons, not yet folded in “probable deaths”, but WO has combined them. A good chunk of the data gap arises from “probable” deaths, mostly in New York City that occurred during the high-mortality period in March and April. It looks like the New York state DOH numbers never included those that could never be directly linked to a positive test. The Hopkins dashboard, along with WO, have included those since mid-April.
     What have we learned: There are big differences in mortality numbers across data compiler sites. Wikipedia and the COVID Tracking Project are below average due, because they are more “conservative” and do not count all the “probable” deaths, relying more tightly on official state departments of health. WORLDOMETER reports the highest death totals for 2 reasons: 1) they channel the higher Johns Hopkins numbers that include more “probable” deaths, and b) they include deaths counted outside official state agencies (e.g., military, and Navajo nation). It is very hard to get to the bottom of these differences. Each data compiler should provide a clear and organized statement about their reporting policies, where the source data comes from. The lion’s share of the discrepancy comes from accounting differences in New York and New Jersey. Even the highest estimated deaths from the WO site represent about 70% of the true death toll from this disease.
Figure C
Figure D
Figure E: Screen grab from: https://www.worldometers.info/coronavirus/us-data/
  1. Quirky Qorner: The ‘coronavirus crush’ is a thing. Why a pandemic is the perfect moment for our infatuation with the unattainable
     What is being cooped up doing to us? Lots we all suspect. Some effects are obvious and others are more subtle and nuanced. I have been reading stories lately about the ‘coronavirus crush’. Apparently, being cooped up all day with just our cats and our thoughts has led many of us to become infatuated with someone not in our bubble. Once again, Vox comes to the rescue with this juicy piece by Terry Nguyen from October 16. Here are some highlights that struck my fancy:
    1. 2020 has been a “big year for yearning” that has translated into a deep, unattainable longing for all sorts of people, places and experiences. I hear that!
    2. Pandemic conditions make crushes lower risk for those already in committed relationships.
    3. Romantic yearning is a convenient and pleasant form of escapism and distraction.
    4. Most of us are craving the pleasant surge of oxytocin and dopamine that a crush can bring forth.
    5. Being on zoom all the time can make us feel like high school kids.
    6. With real relationships now off-line and verboten, our erotic energies are free to play in the realm of fantasy.

Top pick of the day: Wednesday

Is it the flu or COVID-19? How to tell the difference

Article by Janet Loehrke, and Karina Zaiets published online at USA TODAY October 14, 2020.

With all this talk about influenza overlapping with COVID-19, it would be good to have a clear idea of how they are the same and different and what we should be on the lookout for in telling them apart. I love this piece just out at USA TODAY. If you are a visual learner, print this out and put in on the fridge. For sure there is plenty of overlap between these two viral diseases. Telling them apart requires knowing a few simple facts. They are all in here.


Today’s bite-sized, handpicked selection of important news, information or science for all who want to know where this epidemic is going and what we should do.

Screen capture from: https://www.usatoday.com/in-depth/graphics/2020/10/13/flu-covid-19-how-tell-difference-seasonal-influenza/5880649002/