Top medical journals the Lancet and New England Journal of Medicine have retracted articles that called into question the effect of experimental anti-malarial drugs hydroxychloroquine based on third-party data source that has now been brought into question (StatNews)
U.S. COVID-19 cases have been slowly ticking up since Memorial Day (see graph below) (CNBC)
Reopening resurgence? New COVID-19 cases surging in the South and West, deaths continue in hotspots across all regions Across the U.S. more than 147,000 new COVID-19 cases were reported in the previous 7 days, a cumulative rise of 9%. Over the last week, an average of 21,095 new daily cases were reported. This marks a flattening of the overall trend which had been falling in previous weeks. In my opinion, we are beginning to see the uptick in cases that had been predicted following state reopening. The top graph below shows percent rise in new cases in each state. We now have 4 states that added more than 25% to their case totals in the last week, there were no such states in the previous two weeks. In the South, notable rises were seen in Alabama (+18%), Arkansas, which along with Arizona leads the nation with 29% increase in new cases, Mississippi (+16%), North Carolina (+25%), South Carolina (+19%), Tennessee (+17%), Texas (+18%), and Virginia (+17%). In that region, only Louisiana saw cases rise less than 10%. In contrast, 9 of 12 states in the Northeast added 10% or fewer; states on the rise were Maryland, Maine and New Hampshire. The midwest, which had been the hot zone in previous weeks, had lower transmission intensity with 3 states higher than 10% more cases (Minnesota, Nebraska and Wisconsin). New cases continue to spike in Alaska (+24%), Arizona (+29%) and Utah (+21%). Turning to deaths (lower graphic), no state added more than 20% to their total death tally, however because deaths always lag behind cases, this is further evidence that a reopening-related resurgence has begun and that deaths will also turn higher in coming weeks/days. All regions had a mix of results both over and under the 10% threshold. The midwest and South had the higher fraction of states with bigger increases in deaths. States above 15% increase include Arizona, Iowa, Minnesota, Nebraska, Arkansas, Mississippi, North Carolina, Maine and New Hampshire. The bottom line: While I cannot say for sure, the overall trends this week suggest a reversal of the declining trend in new cases, suggesting the possibility that reopening-related resurgence of cases is beginning to be apparent. Regions of greatest concern are now the South and West.
Question from an alert reader: what’s the difference between CFR, IFR and crude mortality ratio? I got a very thoughtful question from a visitor the other day. I thought I would report that here in the hopes it may be helpful to others. Here is the question: I’m confused by terms IFR and CFR. I like your “crude death rate” but no one else seems to use the term. And my confusion comes you use CFR the way most others use IFR (IFR seems to be defined by most studies as the real infection rate over the real death rate). The CFR seems to be defined, at least in the media, as the confirmed cases over confirmed deaths. I’m not an epi person, so looking for some clarity. Is your use of those terms idiosynchratic? 😉 thanks, JK. My answer: Thanks so much for your insightful and alert question. Epidemiology has always been what I call a watering hole rather than a formal field. It’s a thought style more than a particular discipline. We are a loose federation of oddballs from medicine, statistics, social science and virology that gather around common problems more than around solidified doctrine. We get used to the idea that important concepts may go by many different names. We acknowledge that sometimes it’s worth fussing about names and sometimes it’s not. In this case, I haven’t yet decided. The key conceptual challenge underlying this naming issue is simple. When estimating a population risk parameter (the probability of death given disease), we seek an estimated probability or rate where the error in measurement of the numerator and denominator are similar within reason. In this case, the numerator is deaths from COVID-19 and the denominator is all people who have had COVID-19 and were thus at risk for death. With a pathogen that sickens almost every case/infection, such as Ebola or MERS, the distinction between CFR and IFR is rather academic. That’s because the ascertainment rate (the probability that all cases/infections are captured by surveillance) is high, or is at least about the same as the ascertainment of who has died of the disease. However, with a disease in which there are a large number of inapparent or hidden cases/infections, then the error in the numerator and denominator are no longer in the same ball park. What is paramount is the need to keep two types of estimates in mind and keep them separate. One is for the death rate in the cases we know about given the surveillance we are doing. The other is the “real” death rate among all those who have the disease regardless of our surveillance. Both numbers have value but we can’t make the mistake of assuming one is an estimate of the other. For myself, I find it more useful to distinguish the case fatality rate (deaths among all cases/infections) as against the crude death ratio (cases among those we know about conditional on incomplete testing). I like this language because the two labels make it clear just how different these numbers are and makes clear how inferior the latter is (it’s called “crude” after all). Others distinguish between CFR and IFR under the premise that “cases” are what we know about in hospitals and testing labs and “infections” are the larger domain. Call me old school but all cases are infections and all infections should be cases. The big challenge is finding a label that captures not only the mild cases but the sizable fraction (perhaps 25%) of infected persons who are entirely asymptomatic. If that’s what people mean by “infections” then I am ok with that, but infection seems to presuppose some sign of illness. I taught for 30 years and generally speaking, the term cases has always meant disease events regardless of ascertainment. So, I find the distinction between cases and infections to be insufficiently clear to rely on the CFR/IFR distinction. The real problem here is that the WHO and CDC have both contributed to the problem by referring to the crude mortality ratio as the CRF. That has been a big mistake in my view. In classic infectious disease epidemiology, the CFR has been the preferred term for what others call IFR. The result is that many people lost confidence in epidemiologists because they knew that the WHO estimate of “CFR” (at 5.4%) was wrong due to the undercounting of infections in the denominator. That’s something that we have yet to recover from. I believe we should reserve the term CFR for the better, more important and reality-based estimate of population risk. But as usual, I am in the minority. I’m happy for the time being to assume that what others call IFR is CFR for me and what others call CFR I will call crude death ratio (CDR). Confused yet? Perhaps this is a debate we should be having. Thanks for your keen insight. I apologize if my answer is not entirely satisfying.
Maryland reports largest rise yet in COVID-19 cases 4 days after reopening. The most recent test positivity rate is 25% suggesting testing is still selective; cases no doubt under-estimated (NPR)
Tracking SARS-CoV-2 using genetic analysis: The virus jumped from animals to humans, then spread through human-to-human contact; circulation started in China as early as October 9, 2019; the virus mutated minimally before March 1; multiple “seeding” events occurred in the U.S. but most infections in Washington State likely came from one individual (Scientific American)
Wildcat strikes, walkouts and protests erupt across U.S. “essential” industries over low pay, lack of safety and protections for workers (The Guardian)
U.S. COVID-19 cases remain flat, deaths dip On Monday, the U.S. reported 21,287 new cases, a rise of 1.4%. The 7-day moving average has been declining generally, but flattening in recent days. Last week, 159,270 cases were reported nationwide, a 12% rise. The 1-week growth factor was <1 (0.96) as there were 166,000 new cases the week before. The U.S. is poised to pass 1.5 million cases tomorrow, and continues to have the most cases of any nation, with more than 1 million more cases than Russia (now at #2) and Spain (at #3). States with more than 25% case growth in the last week include Arizona (+25%), Minnesota (+39%), North Dakota (+27%), North Carolina (26%) and Maine (+29%). On Monday, there were 792 COVID-19 deaths reported, a rise of less than 1% and the second day of fewer than 800 reported fatalities. Last week, 9,484 Americans died of COVID-19, a rise of 13%, compared to 12,125 the week before (growth factor=0.78). The declining trend in deaths is a positive development, remembering that we would anticipate a rebound in reported deaths tomorrow given the weekend reporting lag. States reporting 25% or more increase in total deaths include Arizona (+28%), New Mexico (30%), Iowa (+31%), Nebraska (+25%), South Dakota (+29%), Delaware (+32%), and New Hampshire (+29%). What this means? Because deaths lag behind cases, and cases lag behind infections, it is plausible that the dip in deaths will be temporary. In my opinion, we are now waiting for signs of the conveyor-belt of infections to become visible over the next few weeks as the impact of reopening moves like a ‘pig in a python’.
From anosmia to ‘COVID toes’: What to do with the strange mix of COVID-19 symptoms Yesterday, there was a nice article in Scientific American summarizing the challenges we face in understanding the often baffling array of symptoms that have popped up during this pandemic. I have been yelling at anyone who would listen since the beginning about the importance of getting the case definition right. Until we have tens of millions more tests, we should be using clinical check-lists to screen everyone. That requires doing a lot of important work to determine how symptoms cluster together and how they map to test results. That itself is a big discussion for another time. In the meantime, lets consider why this has been such a challenge for us disease detectives. Any attempt to characterize the symptoms and signs of disease requires balancing two key features of any case definition: sensitivity and specificity. It’s a balancing act because we have competing needs: define the disease broadly enough that it captures all true positive cases, but narrowly enough that it captures all true negative cases. The first part is intuitive (what set of symptoms, when endorsed, identifies everyone who really has COVID-19). The second part is trickier and less intuitive (which symptoms, when endorsed, avoid identifying people with a similar respiratory infection as having COVID-19 when they don’t). The former is the essence of sensitivity, the latter is specificity. The balancing act gets especially tricky when the most common symptoms (fever, cough, fatigue, headache) are also signs of other diseases (ergo low specificity). That’s why my ears always perk up when a rare and mysterious symptom comes onto the radar. Examples include anosmia (loss of sense of smell) or chilblains (sore, red, swollen toes). From a measurement point of view, these symptoms are as good as gold because while they are rare, they are they can be used to differentiate COVID-19 infection from other illnesses. Problem is that requiring rare symptoms lowers our sensitivity and we miss true positives. We know a great deal about how to handle these measurement challenges, its simply a matter of gathering the right data and doing the leg work. To my knowledge, this hasn’t been done yet and is sorely needed. Another challenge is that the symptom profiles are clouded by the shear scale of the pandemic. With 5 million cases of a disease nobody had heard of 6 months ago, it is hard to separate the diagnostic wheat from the chaff. When huge numbers of people get a new disease, super rare symptoms will occur and get noticed. Some of these will be noise and some will be signal. There is an art to telling the difference. Another challenge is that there are symptoms that occur because of the pathogen itself, and those that occur as a result of host characteristics related to individual differences in immune response. For example, some of the sickest people end up in respiratory failure due to cytokine storm, an exaggerated out-of-control response by the body to a novel viral invader. Separating indicators of differential host susceptibility and symptoms of infection is a key challenge – and its often not a clear line of separation. A final challenge is more sociological than scientific. During a fast moving and scary epidemic, facts and opinions go viral, become memes and persist in the collective mindset, even when they no longer serve a purpose. Both the WHO and the CDC were stuck on an initial case definition from a single brief report from China based on a just 41 very sick ICU patients (Huang, Lancet). Long after we had learned that up to 30% of COVID patients don’t present with a fever, that fatigue and malaise were more common than shortness of breath, and that gastrointestinal complaints were important additions to the mix, it took weeks or months before officials moved past the “big 3” (fever, cough and SOB). Bottom line: A more complete and nuanced understanding of the diverse symptom profiles of this novel disease are emerging. It’s a fundamental task of the disease detective to gather the right clues and examine them through the right lens. That work still lies ahead. How can I help?
Article by Mark Kortpeter, biodefense and infectious disease and public health physician, scientist and author, Forbes, posted May 17, 2020 at 2:00 PM ET
A thought provoking big-picture overview of the way epidemiologists think about epidemics. Covers how disease spreads, modes of transmission, how outbreaks are contained, what the heck is a ‘fomite’, and what is similar and different about SARS-CoV-2.
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.