Stats Chat

Team Ratings for Round 2

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 7 matches played, 3 of which were correctly predicted, a success rate of 42.9%.
Here are the predictions for last week’s games.

Predictions for Round 2

Here are the predictions for Round 2. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for Round 1

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Predictions for Round 1

Here are the predictions for Round 1. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for Finals Week 1

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 192 matches played, 139 of which were correctly predicted, a success rate of 72.4%.
Here are the predictions for last week’s games.

Predictions for Finals Week 1

Here are the predictions for Finals Week 1. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for the Currie Cup Final

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 37 matches played, 23 of which were correctly predicted, a success rate of 62.2%.
Here are the predictions for last week’s games.

Predictions for the Currie Cup Final

Here are the predictions for the Currie Cup Final. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

The world’s Covid response has benefited from the twenty-odd years of large-scale genetics research that preceded it: inexpensive, widely-available PCR and sequencing; mRNA synthesis and delivery.  None of that was the plan, though.  Genomics was supposed to produce widely-applicable treatments for diverse medical problems, and revolutionise medical diagnosis and treatment. It didn’t: there have been genuine breakthroughs, but mostly in the  form of expensive treatments for rare diseases.

Today in Britain, there was definite progress.   NICE, who make recommendations for medication subsidy decisions, have pushed for the funding of inclisiran in people who have high cholesterol and who’ve already had a stroke or heart attack.  Inclisiran lowers LDL (‘bad’) cholesterol a long way, by a different mechanism from the current ‘statin’ drugs, and it can be given by twice-yearly injection at a GP’s office. The drug would usually cost more than it’s worth, but the NHS has a Pharmac-like secret deal to pay less than the £2,000 sticker price.

I’m not sure this is huge news from a public health point of view, but it’s interesting to someone who has worked in genetic epidemiology.  Inclisiran inhibits a gene called PCSK9.  The function of PCSK9 was originally fairly obscure; mutations in it were found by genetic linkage analysis to be related to familial high cholesterol in a group of families who didn’t have mutations in the known high-cholesterol genes.  Research in the Dallas Heart Study, a cohort study of risks for heart disease, found that several people with unusually low cholesterol also had mutations in PCSK9, suggesting that blocking the gene’s action would lower cholesterol.  Now, we actually need some cholesterol, so you’d worry that blocking the gene could be dangerous — but the Dallas Heart Study also found one woman who had natural mutations in both her copies of the gene, and who had extraordinarily low LDL cholesterol and no apparent adverse health effects.  All this came from largely correlational research that relied on inexpensive, large-scale gene sequencing — exactly what genomics had promised.

The other genetic aspect of the new treatment is that it works by silencing the gene, rather than the more-usual approach of blocking the activity of the enzyme after it has been produced.  Inclisiran is a ‘small interfering RNA’ molecule that binds to messenger RNA from the PCSK9 gene and triggers the cell’s recycling mechanisms to chop it up. The protein never gets produced.  This idea has been through hype and disappointment cycles — a small piece of RNA injected into the body looks remarkably like a virus, and the immune system tends to disapprove — but this time it seems to work, and to work on a common risk fact for a common disease.

The return on genetic ‘precision medicine’ has still been rather disappointing compared to the hype, but it’s nice to have the occasional example where it does basically work as promised.

Newshub has a commendably restrained story about some biochemical research into possible starting points for Covid treatment

Brazilian researchers have found that a molecule in the venom of a type of snake inhibited coronavirus reproduction in monkey cells, a possible first step toward a drug to combat the virus causing COVID-19.

Not everyone is so calm about it: The Hill says Brazilian viper venom shows promise as drug to combat COVID-19, the Daily Express says Covid breakthrough as deadly Brazilian snake venom 75% effective in stopping virus, and Indian site Zee News says Jararacussu pit viper, found in Brazil, can be the answer to Coronavirus, says study.

The research paper is here.

Researchers in Brazil were already studying the properties of a fragment of a protein from the venom of the jararacussu, a South American pit viper. This fragment blocks a protease, a protein-snipping enzyme, that is needed by the SARS-Cov-2 virus.  The protein fragment isn’t a drug on its own — and the protein it comes from definitely isn’t; it’s in the snake venom for a reason, and that reason isn’t to benefit animals that get bitten. However, this genuinely is one of the ways we get new drugs. A protein fragment from the venom of a related South America pit viper, which blocked a human protease enzyme,  was the starting point for developing ACE inhibitors, an important class of medications for high blood pressure and heart failure.

A few more things to  point out, though. First, the research paper is studying the ability of the SARS-Cov-2 virus to infect lab-grown hamster kidney cells in a Petri dish. These aren’t particularly realistic targets; they’re just convenient. The paper describes the use of a ‘positive control’, a chemical that they know is effective at stopping infection of these hamster cells under lab conditions. You might have heard of this chemical; it’s called chloroquine.  And finally, the tweet from The Hill that pushed me to write this post has a picture of a pretty green snake. It’s not the jararacussu. It’s an African snake that’s not especially closely related and whose venom hasn’t been studied all that much. They have the picture handy because a snake of that species bit a handler at the San Diego zoo in April. Zee News also use a pretty green snake picture, and it’s even less closely related.

This graphic, in a tweet by @heyblake, struck a chord in a lot of people. On the one hand, data together with stories that personalise the statistics can be a very powerful way to communicate.

On the other hand, this story is a lot whiter and greener than the data, and that’s definitely a thing that can happen.

Team Ratings for Round 25

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 184 matches played, 134 of which were correctly predicted, a success rate of 72.8%.
Here are the predictions for last week’s games.

Predictions for Round 25

Here are the predictions for Round 25. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Team Ratings for the Semi Finals

The basic method is described on my Department home page.
Here are the team ratings prior to this week’s games, along with the ratings at the start of the season.

Performance So Far

So far there have been 35 matches played, 21 of which were correctly predicted, a success rate of 60%.
Here are the predictions for last week’s games.

Predictions for the Semi Finals

Here are the predictions for the Semi Finals. The prediction is my estimated expected points difference with a positive margin being a win to the home team, and a negative margin a win to the away team.

Having been exposed for the past year and half to stories about the ‘basic reproduction number’ and ‘effective reproduction number’ of Covid, you might ask ‘what is R_eff at the moment?’

It’s hard to say. Firstly, it’s not really Covid that has an effective reproduction number but SARS-Cov-2; not the disease, but the virus.  The reproduction number is a feature of models for infection, not models for illness or even for confirmed cases.  Trends in illness or in the number of confirmed cases are important, but they are separated from trends in infection by the whole process of diagnosis, testing, and tracing. Right now, testing is on overdrive: people with minor symptoms are tesing (yay them!) and people with no symptoms but even minor contact with a case are testing (yay them, too!). As a result, cases are much more likely to be diagnosed than they were, say, two weeks ago.

In the long run, under constant conditions, the outbreak will have exponential growth or decay. In the long run, even quite large changes in the diagnosis, testing, and tracing process will be swamped by the much larger changes in the underlying infection rates. In the long run it will be obvious if total infections are going up or down and the rate can be estimated fairly well from confirmed cases. But in the long run we are all in level 1, so that’s not very satisfying.

At the moment, we have a reasonable hope that the population is effectively partitioned into bubbles, with much lower spread between bubbles than within bubbles.  If so, new confirmed cases will mostly either be new diagnoses of cases infected a while ago, or cases who got it from someone in their bubble.  For example, a lot of people who work in the same building as the Stats Department were being tested yesterday, in case they had been infected on August 17.

The number of cases like these is important, because we care about their health, but doesn’t really tell us about the effectiveness of level 4 lockdown, which is about the relatively small number of new between-bubble transmissions from people who were not yet diagnosed.  Calculating effective reproduction numbers from the number of observed cases isn’t going to be very accurate.

All this goes to say that, yes, we have good reason to hope the out-of-bubble reproduction number is well under 1, but the actual value genuinely is hard to estimate — and it’s particularly hard to estimate just from public data on numbers of newly confirmed cases.