Using estimates from the Surgo Foundation and Ariadne Labs, Stuart A. Thompson for NYT Opinion shows how many people are in front of you to get the coronavirus vaccine. Just enter your age, if you’re an essential worker, and the county you live in for an idea of where you are.
Category Archives: coronavirus
Prompted by a tweet about scented candles without smell and Covid-19, Kate Petrova plotted Amazon reviews for scented and unscented candles over time. Notice the downward trend for scented candles after the first confirmed case for Covid-19.
Interesting if true. I’m imagining a bunch of people opening their new scented candles, taking a big whiff, and not smelling anything.
But I wonder if there are outside forces (a.k.a. confounding factors) at work here. For example, Petrova only looked at reviews for the “top 3” scented candles. What do we see with other candles? Maybe a higher demand for scented candles from more people staying at home put a strain on the manufacturer. Maybe there was a shortage of some scented ingredient, which led to less potent candles. Maybe new scented candles customers have unrealistic expectations of what candles smell like.
I don’t know.
Maybe the decreasing average review really is related to Covid-19 symptoms.
Petrova put up the code and data, in case you want to dig into it.
As we’ve talked about before, it can be hard to really understand the scale of big numbers. So when we hear that over 250,000 people died because of the coronavirus, it can be hard to conceptualize that number in our head. Lauren Tierney and Tim Meko for The Washington Post provide a point of comparison by highlighting counties that have have populations under 250,000.
Whole counties, or whole clusters of counties, that would be completely wiped out.
It’s a lot.
Reporting for NPR, Sean McMinn and Selena Simmons-Duffins on staffing shortages:
On data availability:
This is the first time the federal agency has released this data, which includes limited reports going back to summer. The federal government consistently started collecting this data in July. After months of steadily trending upward, the number of hospitals reporting shortages crossed 1,000 this month and has stayed above since.
The data, however, are still incomplete. Not all hospitals that report daily status COVID-19 updates to HHS are reporting their staffing situations, so it’s impossible to tell for sure how much these numbers have increased.
The first time.
It was back in March, a few lifetimes ago, when we were talking about flattening the curve so that hospitals could provide care to those who needed it. This federal dataset is just coming out now in November? Obscene.
A small gathering of 10 people or fewer can seem like a low-risk activity, and at the individual level, it’s lower risk than going to a big birthday party. But when a lot of people everywhere are gathering, small or large, the collective risk goes up. For FiveThirtyEight, Maggie Koerth and Elena Mejía illustrate the reasoning.
The collective part is where many seem to get tripped up. “Flattening the curve” only works when everyone works together. Lower your risk, and you lower the collective risk. You’re helping others. You’re helping those you care about.
Then, collectively, we all get out of this mess.
The University of Oxford’s Blavatnik School of Government defined an index to track containment measures for the coronavirus. For The New York Times, Lauren Leatherby and Rich Harris plotted the index against cases and hospitalizations:
When cases first peaked in the United States in the spring, there was no clear correlation between containment strategies and case counts, because most states enacted similar lockdown policies at the same time. And in New York and some other states, “those lockdowns came too late to prevent a big outbreak, because that’s where the virus hit first,” said Thomas Hale, associate professor of global public policy at the Blavatnik School of Government, who leads the Oxford tracking effort.
A relationship between policies and the outbreak’s severity has become more clear as the pandemic has progressed.
States with more restrictions tend to have lower rates.
From these plots, it seems clear what we need to do. But I think most people have made up their minds already, and the interpretation of the data leads people to different conclusions.
With the holidays coming up, I just hope you lean towards clarity.
In a previous post, Guido constructed trees for coronaviruses in the SARS group to search for evidence of recombination. He also constructed unrooted data-display networks using SplitsTree. Here, we discuss our attempts to construct rooted genealogical phylogenetic networks for the same dataset  but with some modifications.
In particular, we deleted some sequences, giving a smaller data set with only 12 taxa. These taxa include, next to SARS-CoV-2 (the virus causing COVID-19) and SARS-CoV (responsible for the SARS epidemic in 2002/2003), the viruses MP789 and PCoV_GX-P1E sampled from Malayan pangolins from two different Chinese provinces and several viruses found in different bat species in the horseshoe bat genus (Rhinolophus), all from China.
This research was done by Rosanne Wallin, an MSc student at VU Amsterdam and UvA. Her full thesis as well as all data and results can be found on github.
The first algorithm we applied to this data set was the TreeChild Algorithm , which is one of the methods that take a number of discordant (rooted, binary) trees as input and finds a rooted network containing each input tree, minimizing the number of reticulate events in the network. To filter out some noise, we contracted some poorly-supported branches and then resolved multifurcations consistently across the trees (using a tool within the TreeChild Algorithm). This gave the network below. Note that the method is restricted to so-called tree-child networks, meaning that certain complex scenarios are excluded (where a network node only has reticulate children). Also note that this is not necessarily the only optimal tree-child network and not all topological differences can be distinguished based on the trees .
|Figure 1: Phylogenetic network constructed by the Tree-Child algorithm (blocks_A_len0.01_supp70).|
The network shows no reticulation in the SARS-CoV-2 clade (the bottom four taxa) and puts SARS-CoV-2 right next to RaTG13. Furthermore, it shows a reticulation between an ancestor of HKU3-1 and a common ancestor of SARS-CoV-2 and RaTG13 leading to bat-SL-CoVZC45. However, it cannot exactly identify which common ancestor of SARS-CoV-2 and RaTG13 is the parent, leading to multiple branches (in red) leading into this reticulation. All these observations are consistent with previous research .
Importantly, we cannot directly conclude that each reticulation corresponds to a recombination event. See Table 2.1 of David’s book  for a nice overview of possible causes of reticulation. Nevertheless, based on , it does look like at least the reticulation leading to bat-SL-CoVZC45 corresponds to a recombination event.
The second algorithm we applied was TriLoNet , which constructs a rooted network directly from sequence data. It is restricted to so-called level-1 networks, meaning that it cannot construct overlapping cycles. This method produced the network below.
|Figure 2: Phylogenetic network constructed by TriLoNet.|
|Figure 3: Phylogenetic network constructed by TriLoNet, after omitting bat-SL-CoVZC45.|
Coronavirus cases are rising (again), which includes prisoners and prison staff. The Marshall Project has been tracking cases since March and provides a state-by-state rundown:
New infections this week rose sharply to their highest level since the start of the pandemic, far outpacing the previous peak in early August. Iowa, Michigan and the federal prison system each saw more than 1,000 prisoners test positive this week, while Texas prisons surpassed 2,000 new cases.
The microCOVID Project provides a calculator that lets you put in where you are and various activities to estimate your risk:
This is a project to quantitatively estimate the COVID risk to you from your ordinary daily activities. We trawled the scientific literature for data about the likelihood of getting COVID from different situations, and combined the data into a model that people can use. We estimate COVID risk in units of microCOVIDs, where 1 microCOVID = a one-in-a-million chance of getting COVID.