Author Archives: arguably wrong

This preproof from Nathan Grubaugh (Yale, PhD in microbiology, 2016), Bill Hanage (Harvard, PhD in epidemiology), and Angela Rasmussen (Columbia, PhD in microbiology, 2009) came across my desk. It’s about the spike protein variant that, as Bette Korber at Los Alamos demonstrated, has become the dominant strain of the coronavirus in the last couple of […]

I think it’s become fairly apparent that effective federal and even state responses to this pandemic are not going to happen in the short term. I had a discussion over at Greg’s blog where I pointed out that effective local responses are still perfectly possible in most places. So let’s take a brief look about […]

There’s some foolishness going around about this recent preprint from Los Alamos.   They’ve spotted several mutations in collected SARS2 sequences from around the world that look as though they may be under positive selection.  We’re going to look into one in particular, a D614G mutation in the spike protein.  This mutation looks as though it’s increasing […]

The state-specific projections from IHME have been widely disseminated and used for federal, state, and hospital planning. For example, here’s an interview with one of the UW professors on the project describing how their projections are being used by the feds. They have done yeoman’s work in collecting appropriate data, but their actual model has […]

The previous model I built was a toy model: the simplest possible dynamics that would give you a feel for what’s going on, and parameter selection that was educated guesswork. There was sufficient interest in it, though, that I decided it would be worth building a more sophisticated and hopefully accurate model. State dynamics and […]

One point that’s come up a couple of times is the expense of imposing serious infection controls. China probably lost hundreds of billions of dollars on controlling the outbreak in Wuhan. Let’s try to make a guess of how much it would cost us, and use that to figure out what the best path forward […]

A basic SIR model for the epidemic. Total population 327 million, with a single initiating infection. 12 day course of disease, initial R0 of 3.5 (as per the stats from China that Steve linked.) Assume 5% of cases are critical, 2% of those critical cases die with ICU care, 5% with general hospital care, and […]

Suppose we have a dominant mutation in the population with some prevalence . That means that a fraction of the alleles at that locus in the population are our mutation, while the rest are wild-type. We’d like to know how that prevalence changes over time. Some of those changes will be random, but we can […]

Introduction What happens to a new mutation? Every person has some new mutations in their somatic genome, mostly caused by errors in DNA replication. They can pass that mutation on to their descendants. We know of a couple of historical mutations that look like they are all from a single common ancestor – for example, […]