Testing remains inadequate to determine how we should be addressing this virus. But recent data suggests that the IFR (infection fatality rate) is lower than originally thought. To understand this you must understand the difference between the CR (Case Rate) and the IR (Infection Rate) as well as the difference between the CFR (Case Fatality Rate) and the IFR (Infection Fatality Rate).
Case Rate: # Positive tests/ population
Infection Rate: # actual people infected/ population
Case Fatality Rate= #deaths/ # positive tests (# deaths/known cases)
Infection Fatality Rate= # deaths from the virus/ # infected (# deaths/ known and unknown cases)
Ideally everyone would be tested with a perfect test. A perfect test would be positive for everyone infected ( no false negatives-whether symptomatic or without symptoms) and it would be negative if the virus is not present (no false positives).
No test is perfect, but even with imperfect tests we would know much more with greater numbers of people tested, including those without symptoms.
To calculate an accurate estimate for the Infection Fatality Rate, we must widely test people without symptoms in a hard hit area such as New York City. Only then will we understand whether this virus is significantly more lethal than other viruses such as the flu. Early estimates were based on very imperfect data. Remember, Fauci stated before congress that COVID 19 is “ten times worse than the flu” based upon all the information available at the time. Fauci is arguably the most informed/knowlegable/reasonable professional we have to help guide us through this very uncertain time. The more data (testing) we obtain, the better-informed will be our plans going forward.
Shelter-in-place is most effective when started early, before the disease spreads widely and buys time to let hospitals prepare and expand capacity so that the system is not over-whelmed. Flattening the curve is important. It buys time and saves lives primarily by avoiding a situation where health care capacity is exceeded by demand (when that happens people who could have been saved do not stand a chance). But during the time-bought, we should have implemented widespread testing of people with and without symptoms to gain a better understanding of the epidemic. We did not do that. Testing remains inadequate for proper assessment of when and how we might begin to return to “the new normal”. Testing remains inadequate for understanding the risks of lifting various restrictions.
Early in an epidemic, lives are saved by testing, contact tracing, and isolation in combination with social distancing measures and the extreme measure of shelter in place. Unfortunately, we still do not know where we stand, primarily because of inadequate testing.
Below is the link to a long interview with a respected epidemiologist who explains that his recent study suggests the IFR for Covid 19 is similar to the Flu. This does not mean that shelter-in-place did not provide benefit. COVID 19 is clearly much more contagious than the Flu. But it does mean that provided we INCREASE TESTING and follow closely the impact of GRADUAL REDUCTION OF SOCIAL RESTRICTIONS, we may soon be able to allow the return of certain activities in an incremental fashion. The ideal strategy will depend on the specific local and regional circumstances (rates of infection and deaths, availability of hospital beds, ICU beds, PPE, health care workers, rural vs urban, reliance on public transportation (subways/buses vs cars), population density, degree of at-risk population, etc.)
If you choose to watch this long interview, be careful to take everything with a grain of salt. One study of IR (infection rate) in one community does not automatically translate into national policy implications. The difference between Santa Clara County CA and the New York Metropolitan area is enormous for many reasons. This should not lead to anyone dropping their cautions, throwing away masks, and resuming activity with abandon. But it should lead to understanding the completely inadequate data that we presently have to make decisions AND the great need for caution as we move forward.
The person conducting this interview clearly is biased, believing that stay-at-home was not necessary. He is constantly pressing Dr. Ioannidis to draw that conclusion. Remember, one small epidemiologic study is not enough to draw conclusions about the fatality rate of infection. We need more data. But there is a glimmer of hope.
This data has not been peer-reviewed yet.
“This article is a preprint and has not been peer-reviewed [what does this mean?]. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.”
Excerpts from the study referenced in this interview:
We report the prevalence of antibodies to SARS-CoV-2 in a sample of 3,330 people, adjusting for zip code, sex, and race/ethnicity.
Under the three scenarios for test performance characteristics, the population prevalence of COVID-19 in Santa Clara ranged from 2.49% (95CI 1.80-3.17%) to 4.16% (2.58-5.70%). These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50-85-fold more than the number of confirmed cases.
Here is the link.
Now, whether you chose to sit through that very long interview, here is another quote from a study by the same author, also a “Pre-Print” not yet peer-reviewed.
Individuals with age <65 account for 5%-9% of all COVID-19 deaths in the 8 European epicenters, and approach 30% in three US hotbed locations. People <65 years old had 34- to 73-fold lower risk than those ≥65 years old in the European countries and 13- to 15-fold lower risk in New York City, Louisiana and Michigan. The absolute risk of COVID-19 death ranged from 1.7 per million for people <65 years old in Germany to 79 per million in New York City. The absolute risk of COVID-19 death for people ≥80 years old ranged from approximately 1 in 6,000 in Germany to 1 in 420 in Spain.
So there are huge differences in mortality rates from country to country and region to region, including for different age groups.
WE DO NOT YET UNDERSTAND THESE DIFFERENCES NOR CAN WE SAFELY EXTRAPOLATE THESE NUMBERS TO MAKE PUBLIC HEALTH DECISIONS.
WE NEED MORE DATA.
OK, now here is an update to this post. The Stanford Study described above and discussed in the video of Dr. Ionnidis SHOULD BE WITHDRAWN. I have read serious methodological criticisms of this study. Here are a few of the major problems.
- The study assumed a test specificity of 99.5% ( false positive rate of 0.5%) BUT an independent test of the test that was likely used (Chinese lab test vendor:Hangzhou Biotest Biotech) revealed 87% specificity (13% false positive rate). That is a huge problem as described in this analysis.
- The sample was not truly a random population, they advertised on facebook for participants at a time when testing was not very available in Santa Clara County. If you had symptoms or had been exposed and heard about a free test would you enroll in the study? (you bet).
Very thoughtful and knowledgeable scientists have been analyzing how America can return incrementally to less restricted activity. It is very complicated, will vary from region to region, locality to locality, and will need constant assessment and modification. You can read one excellent report here.
That report, prepared by Johns Hopkins School of Public health, is titled Public Health Principles for a Phased Reopening During COVID-19: Guidance for Governors.
Here is an important excerpt.
The majority of models have shown that, in the absence of social distancing, COVID-19 has a reproduction rate of between 2 and 3 (though some models have shown it to be higher). This means that every person with the disease will spread it to 2 to 3 others, on average. To end an epidemic, control measures need to drive that number as far below 1 as possible. A vaccine can do that if and when it becomes available. But in the meantime, social distancing measures, combined with case-based interventions, are the key tools to maintaining the reproduction rate below 1. If the reproduction rate rises above 1, this means that epidemic growth has resumed. If that occurs, it may be necessary to reinitiate large-scale physical distancing. It is important to recognize that states will need to actively manage COVID-19 cases with great vigilance for the entire duration of the pandemic until a safe and effective vaccine is widely available.
There are still many gaps in scientific understanding about the transmission dynamics of SARS-CoV-2. But initial published data suggest that transmission of SARS-CoV-2 occurs primarily through prolonged, close contact. In studies that have monitored people with a known exposure to a confirmed case, household members, those who report frequent contact, and people who have traveled together or shared a meal are found to be at highest risk of infection. Other studies that attempt to reconstruct transmission chains among confirmed cases have also found that prolonged close contact is the source of most new infections. Some special settings have also been identified. Superspreading events have been linked to religious services, choir practice, and large family gatherings, among others. Congregate settings like cruise ships, institutions of incarceration, and long-term care facilities have also been the source of large outbreaks. These findings suggest that settings where close contact is minimal will be lower risk than settings with prolonged close contact.
The precursor to the report cited above can be read here.
Clearly, at-risk individuals (elderly, anyone with chronic illness) will need to have greater restrictions for longer periods of time. Everyone will need to be careful to follow guidelines to prevent infecting themselves and others. People living in densely populated areas that rely heavily on public transportation (example: New York Metropolitan area) have suffered the most and will continue to be hot zones until herd immunity is achieved. Large gatherings of people, particularly in confined areas with close proximity, remain high risk for contracting the illness. Remember, sitting at a table and eating or playing cards with an asymptomatic but infected person can result in everyone at the table getting infected. Remember the choir rehearsal in Washington where everyone likely became infected.
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Eat clean, drink filtered water, love, laugh, exercise outdoors in a greenspace, get some morning sunlight, block the blue light before bed, engage in meaningful work, find a sense of purpose, spend time with those you love, AND sleep well tonight.