There is a significant amount of information and data regarding the world-wide coronavirus pandemic that has accrued over the past 3 months. This permits some interim observations and speculations:
1. The coronavirus is worse than a bad flu. The mortality in various locations and wide spectrum of disease does not permit honest comparison with seasonal flu, or even some of the more recent pandemics such as the 2009 H1N1 flu. The experience with influenza includes experience with numerous vaccines, an understanding of virulence factors, and at least some immunity protection from prior infection with related strains.
2. Paradoxically, coronavirus does not spread easily. The numbers of infected people in varied populations is low, with no sizable population yet above 2% diagnosed with infection. The model of infection in which an infected person encounters an uninfected one and this is associated with a high probability of transmitting the virus is overly simplistic. A more reasonable model treats transmission as analogous to a key fitting in a lock, with a number of competing factors promoting and inhibiting transmission at any one time. These factors are time and environment-dependent. Given favorable conditions, the virus spreads rapidly; given unfavorable ones, it wanes. This is consistent with the experience of SARS and MERS.
3. One of the great unexplained questions arising from the pandemic is the huge variance in death rates, even without medical system depletion, and in the presence of extreme government-imposed limitations on activity. This, as above may be understood with the key and lock analogy. There is no single explanation for the differences in attributed deaths-to-diagnoses ranging between more than 13% in Italy and 2% in Japan or 3% in Germany. The same is true for the 7.6 percent rate reported for NewYork and the 2.5% rate in Texas. Whether coronavirus kills a particular patient depends on a constellation of conditions that happen to be very unfavorable in New York and Italy. One possible factor, although not sufficient to explain the entire phenomenon, is that New York is doing something that increases the risk of infection among those most likely to die from it, like admitting patients known to be infected to nursing homes.
4. The haphazard approach to determining which data are significant and for what purposes will prove to be a missed opportunity. One may wonder what some of the downstream effects are. For example, there are currently cancer therapy trials ongoing in which the endpoints are death. What becomes of the carefully managed data in these trials if a death is presumed to be due to coronavirus?
5. There is no guarantee that there will ever be a vaccine to protect against COVID-19. Decisions that assume the contrary are neither competent nor serious.
6. Likewise, assumptions about the utility of "testing" are of unclear validity. Different tests will have different false negative and false positive rates. It is possible that there may be cross-reactivity between SARS cov-2 antibodies and antibodies produced by previous, less virulent coronaviruses. There is the practical problem of a person contracting infection the day after testing negative. There is also an issue with a rare but not insignificant pattern of positive-negative-positive testing. What to do with the last test result? There are assumptions, but no proof that a person who has a negative coronavirus test is not infectious; is this assumption accurate? Is it true for everyone? Most people?
7. Data that would be useful, and questions that occur:
- How many people who are put on a mechanical ventilator die before hospital discharge?
- Is there a post-coronavirus syndrome, possibly affecting heart muscle function, causing lung scarring or affecting the kidneys or bone marrow?
- Do abandoned therapies such as activated protein C, that were previously used in treating sepsis have a role in treating COVID-19?
- What percentage of people in multi-person households test positive for the virus?
- Is high concentrations of inhaled oxygen detrimental to the lungs of COVID-19 patients?
- Are the risk factors for acquiring infection different than those for severe disease?
- Do government mandates and forcible closures have a significant impact beyond that produced by providing reliable advice and letting people use common sense?
- Does the severity of disease decrease as the number of new infections tapers off?
8. Although it is not claimed that there is a phenomenon that produces a feed-back effect that influences spread and mortality of the virus, as contemplated in the post "What if...?", the virus appears to behave as though there were. Such a phenomenon might explain not only the relatively low numbers of infections (as was also true with SARS and MERS) but also the stark differences in transmissibility and mortality, the fluctuations in the daily new case counts, etc. It might also address the inquiry that may have set off the whole episode: why don't bats that are infected with coronavirus get sick?
9. So, does social distancing work? The evidence would seem to indicate that the answer is "a little." The experiences in "hot spots" like New York and Italy indicate either that the virus is more resilient than thought and persists for more than a couple of exposures, or that it is not possible to enact social distancing measures strict enough to have a dramatic effect, or perhaps both. This conclusion takes into account the experiences in South Korea and Taiwan who do have social distancing measures, but which do not include the types of isolation that would prevent most transmissions. Social distancing decreases the likelihood of transmissions across the board, but there are other factors that also have large effects, both positive and negative, on disease spread.
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