World affairs

Latest European Covid: getting it back under control?

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This is a follow-up to my discussion of high Covid incidence (cases per population) in Europe. As of Nov. 23, France has turned things around. Other European countries are at least not getting worse. The US is still growing rapidly. But new cases are still high in all of these countries, and they have a long way to go to return to the low rates of the Summer. China, Korea, and Taiwan are still invisibly low by comparison.

Deaths increased in October/November less dramatically than cases, reflecting large improvements in treatment of serious Covid. Deaths lag cases by approximately 2 weeks, which may explain why France’s recent turnaround in infection rates does not show up yet.

The last figure is supposed to show today’s data from Our World In Data, but WordPress seems to have trouble rendering it.

https://ourworldindata.org/coronavirus-data-explorer?zoomToSelection=true&time=2020-03-01..latest&country=IND~USA~GBR~CAN~DEU~FRA&region=World&casesMetric=true&interval=smoothed&perCapita=true&smoothing=7&pickerMetric=total_cases&pickerSort=desc
Latest Covid numbers from selected advanced countries

Other countries now have even more Covid-19

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For much of the Covid-19 pandemic, the USA was the worst off by many measures. Total number of cases, number of deaths, incidence rate (new cases per capita) — we were the highest large country on all of them.

That is no longer true, to my surprise. Much of Europe is now as badly off as we are. The relevant comparison is per capita, in order to adjust for country size. I selected 4 large European countries: Italy, France, UK, and Germany. Three are now in the same range of misery, although Germany has been consistently a bit better. This figure shows daily incidence. (New cases per day.)

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Smarter contact tracing

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Not just another cell-phone idea

Can Contact Tracing Work At COVID Scale? Amit Kaushal and Russ B. Altman JULY 8, 2020 10.1377/hblog20200630.746159

You cannot trace everybody, so be smart about who you trace. This article points out the impracticality of massive contact tracing, and how to build a learning system to make it useful anyway. Contact tracing is hard, and when there are too many cases it starts to break down. But we need to figure it out, especially in high-priority settings and in places with limited outbreaks. There are also many idiosyncrasies in Covid infection patterns. A well-executed learning system can gradually make smarter judgments about where to look for cases, who to test, who to quarantine, and when to lift the quarantine.

As we build our nation’s tracing operations, we need to ensure that they are effective at identifying contacts while attempting to quarantine as few people as possible, for as short a duration as possible. To ensure contact tracing remains viable at scale, we must develop data-driven metrics to evaluate and adapt our contact tracing efforts. Historically, successful contact tracing has been measured by its sensitivity [based on more is better]. However, at scale “more is better” breaks down. We must have corresponding metrics for specificity, to … exclude from quarantine those people who have not themselves become carriers of the virus.

Can Contact Tracing Work At COVID Scale? | Health Affairs

But will America’s current political decision-making paralysis, chaos, and suspicion allow the systematic tracing program that would be required? At the national level it seems unlikely. But this approach can be done by states or smaller units. There are probably some states with enough leadership and public willingness to be serious about suppressing Covid before it wipes out another 6 months of jobs and education!

Memorial Sloan Kettering’s Season of Turmoil – The New York Times

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America’s health care research system has many problems. The overall result is poor return on the money spent. The lure of big $ is a factor in many of them. Two specific problems:

  • What gets research $ (including from Federal $) is heavily driven by profit potential, not medical potential. Ideas that can’t be patented get little research.
  • Academic career incentives distort both topics of research (what will corporate sponsors pay for?) and publication. The “replicability crisis” is not just in social sciences.

This NYT article illustrates one way that drug companies indirectly manipulate research agendas: huge payments to influential researchers. In this article, Board of Directors fees. Large speaking fees for nominal work are another common mechanism. Here are some others:

Flacking for Big Pharma

Drugmakers don’t just compromise doctors; they also undermine top medical journals and skew medical research. By Harriet A. Washington | June 3, 2011

I could go on and on about this problem, partly because I live in a biotech town and work at a biotech university. I have posted about this elsewhere in this blog. But since it’s not an area where I am doing research, I will restrain myself.

Some U.S. police departments dump body-camera programs amid high costs – The Washington Post

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Smaller departments that struggle with the cost of equipment and storage of data are ending or suspending programs aimed at transparency and accountability.

Source: Some U.S. police departments dump body-camera programs amid high costs – The Washington Post

My comment: this was predictable. Video data gets big very quickly. See my discussion 3 years ago.

does the Harvard Business School, Michael Porter, teach the essence of business strategy is the elimination of competition, by regulation if possible. Is this legal? Is this basically socialism or communism? – Quora

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Original question on Quora: Why does the Harvard Business School, Michael Porter, teach the essence of business strategy is the elimination of competition, by regulation if possible. Is this legal? Is this basically socialism or communism?

My response: Trying to pin this on Michael Porter is ridiculous. He says no such thing. Based on the way the question is phrased, I wonder if there is an ideological purpose in asking it.

But in any case, there is a serious issue behind the question, namely an increasing level of oligopoly (decreasing levels of competition) among companies in many US industries. See, for example, “Big Companies Are Getting a Chokehold on the Economy Even Goldman Sachs is worried that they’re stifling competition, holding down wages and weighing on growth.”  or.

“America Has a Monopoly Problem—and It’s Huge”.

One theory about this trend is that it is partly due to growing power of corporations in Washington. That, in turn, may be traced partly to the increasing role of money in elections, largely as a result of the infamous Supreme Court “Citizens United” decision. For example, the way Trump’s massive tax cuts were put together without any hearings and in a VERY short period of time, and the amount of “goodies” for many industries in the resulting package, would never have happened with previous massive changes in taxes.

An effective strategy in some highly concentrated industries is to persuade the government to selectively regulate your industry, in ways that favor large and established companies. That is, all companies may experience higher costs because of a regulation, but if your company can respond more cheaply than anyone else, it is still a net win for you. An example is pharmaceuticals. For example pharma companies increasingly use the legal system, regulations, and side deals to keep generic drugs off the market for years after drug patents expire. The industry has also been very effective at keeping foreign competitors out – e.g. blocking imports by individual citizens from Canada.

(I buy one medication at $1 per pill from abroad, when it costs $30/pill at the local Rite-Aid. But it takes a lot of research and effort.)

Source: (32) Why does the Harvard Business School, Michael Porter, teach the essence of business strategy is the elimination of competition, by regulation if possible. Is this legal? Is this basically socialism or communism? – Quora

Would ‘explainable AI’ force companies to give away too much? Not really.

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Here is an argument for allowing companies to maintain a lot of secrecy about how their data mining (AI) models work. The claime is that revealing information will put  companies at a competitive disadvantage. Sorry, that is not enough of a reason. And it’s not actually true, as far as I can tell.

The first consideration when discussing transparency in AI should be data, the fuel that powers the algorithms. Because data is the foundation for all AI, it is valid to want to know where the data…

Source: The problem with ‘explainable AI’ | TechCrunch

Here is my response.

Your questions are good ones. But you seem to think that explainability cannot be achieved except by giving away all the work that led to the AI system. That is a straw man. Take deep systems, for example. The IP includes:
1) The training set of data
2) The core architecture of the network (number of layers etc)
3) The training procedures over time, including all the testing and tuning that went on.
4) The resulting system (weights, filters, transforms, etc).
5) HIgher-level “explanations,” whatever those may be. (For me, these might be a reduced-form model that is approximately linear, and can be interpreted.)

Revealing even #4 would be somewhat useful to competitors, but not decisive. The original developers will be able to update and refine their model, while people with only #4 will not. The same for any of the other elements.

I suspect the main fear about revealing this, at least among for-profit companies, is that it opens them up to second-guessing . For example, what do you want to bet that the systems now being used to suggest recidivism have bugs? Someone with enough expertise and $ might be able to make intelligent guesses about bugs, although I don’t see how they could prove them.
Sure, such criticism would make companies more cautious, and cost them money. And big companies might be better able to hide behind layers of lawyers and obfuscation. But those hypothetical problems are quite a distance in the future. Society deserves to, and should, do more to figure out where these systems have problems. Let’s allow some experiments, and even some different laws in different jurisdictions, to go forward for a few years. To prevent this is just trusting the self-appointed experts to do what is in everyone else’s best interests. We know that works poorly!