COVID-19 Watch

Phase 3 trials are suddenly ending now because the large spike in cases makes it easier to gather statistically significant data. "The companies said that out of 170 cases of Covid-19, 162 were in the placebo group, and eight were in the vaccine group. Out of 10 cases of severe Covid-19, nine had received a placebo." That's amassingly effective, far better than I would have hopeed. It will take time, but the end of this nightmare may at least be in sight... https://www.nytimes.com/2020/11/18/health/pfizer-covid-vaccine.html?referringSource=articleShare 

From the very start of the pandemic, the most important unknown was the number of mild or asymptomatic cases that were being missed by testing.  If the number of missed infections was high, then the disease was less deadly, and population immunity levels were higher.   If the number of missed infections was low, then the disease was more deadly, and population immunity levels were lower. We did our best to figure this out, and you can look back through my blog history to see how I wrestled with the question.  During the summer, cases simply did not rise at the rate I would have expected, and I began to ask questions like whether it was possible that cross immunity to other coronaviruses had created a large fraction of the population that had pre-existing immunity. I simply could not understand why cases weren't rising faster than they were (given our behavior). Yes, we saw cases go up in NEW locations that had not yet hade major outbreaks. But places with large outbr...

Flu season is about to begin in the Northern Hemisphere. I got the flu vaccine this week to get ready.   It seems likely that catching both the flu and COVID-19 at the same time would increase risk of both mortality and of serious side effects. Worse, an increase in flu patients could increase the pressure on an already stressed healthcare/hospital system.  Several news outlets have been predicting a crisis as the flu and the coronavirus collide.  While I think that there are risks associated with the convergence of COVID-19 and the flu, I was skeptical that there would be a massive flu burden in the US this season all the way back in March when this fear was first mentioned. I am even more skeptical now. Why is that?  There are many reasons, but the most significant is that the level of physical distancing needed to keep COVID-19 under control should crush the flu into near nonexistence. And recent data from the Southern Hemisphere that just finished their flu ...

COVID-19 Weekly Update:  Graphs for my estimate of the % of the population that are actively infected are now finally available. They are not yet in the user interface for the web page, but you can view them from the git repository here: https://github.com/jlc42/jlc42.github.io/tree/master/figs/PercentActive I believe that this is the most useful statistic for determining the risk of a given activity. For example if you live in a location with 3% active, and you participate in an activity with 100 randomly chosen people, then on average, three people in that activity could be contagious.  Some examples: In the USA (taken as a whole) about 1.25% of the population is actively infected. So in a group of 100, you would expect just over 1 person to be contagious.  Some places in the US are significantly better than others: In Utah, the % infected is nearly 3%, so in a group of 100, we would expect around 3 people to be contagious.  In New Mexico, the % infected is 0.5%, s...

Utah is a good data point for the impact of school openings on COVID-19 case spread.  Utah opened schools in person mid August. At the time there was debate about whether opening schools would actually increase the spread of COVID-19 because children have more mild symptoms, and it was hoped that this also meant that they didn't spread the virus as well as adults.  We knew that this was likely true for children under 10, but we also knew that children over this age spread the virus as well as adults. Therefore, opening schools for older children, as well as colleges, should cause an increase in spread.  This is in fact what we can clear see in the Utah data: This is not a single one day noise spike. This trend is clearly visible in the average, over several days. Notice also that cases are rising faster than tests, so this is not the result of increased testing. Cases are now above tests in my figure. The scale is set such that when cases are above tests, that means that ...

Over the last week we have been talking about the mystery of second wave resistance across the US. After about 25% of the population is infected and recovered, there is remarkable resistance to a resurgence of the virus, even after states "reopen". This is mysterious because immunity thresholds should be around 66% if the R0 of SARS-CoV-2 is really around 3 as we believe. One likely explanation for this is that personal behavior is providing the additional protection. In the past I have used Google mobility data to characterize the level of physical distancing. But this only captures how much we are moving around, and can't capture how careful people are being. However, there is now some interesting polling data available that may capture some of these effects. Ryan Burge has provided excellent visualizations of the steps the American public is taking from the DFP COVID-19 Tracking Poll: (see: https://twitter.com/ryanburge/status/1301887909675569152/photo/1 ) Approximate...

 Today the USA passed 200,000 deaths.  World wide we are nearly at 1 million deaths.  Source: https://www.worldometers.info/coronavirus/country/us/   Early predictions back in March were that between 100,000 to 200,000 people might die from COVID-19 in the USA if we did nothing.  We didn't do nothing. But what we did was just as ineffective.  Now over 200,000 are dead, worse than the worst of the projections. There is a narrative that these projections were wrong... too "high".  It turns out that they were actually far too low.  They were wrong, in other ways. The assumed fatality rate was too high in the early models, but they under-estimated how many would be infected. They over estimated how quickly the disease would decline, and they severely over estimated the quality of our response. We failed, beyond the wildest imaginings of the worst case models from March.