Category Archives: Public Health/Coronavirus

Contact tracing apps do not track surface and air contacts across time

First:

Contact Tracing of Surfaces Across Time

Another problem with contact tracing apps is they cannot detect contacts across time – nor aerosolized virus and droplets handing in the air.

Some one sits on a bus seat or commuter light rail seat, coughs, putting droplets on the seat and in the air around the seat. Then gets up and leaves. Another person boards and sits in that seat and touches it with their hands. The BLE model is unable to detect this contact. Considering that the NYC subway is now thought to have been a major vector for diseases transmission, this is a serious short coming.

Another example – some one sits at a table at Starbucks, coughs at the table a few times and then leaves. 1 minute later, someone else sits at that table and touches their hands on the table, or just breathes the aerosol or droplet cloud, and later scratches their nose. The contact tracing app cannot detect surface or air contamination scenarios across time. While Starbucks might clean tables frequently, there is no guarantee.

There is no way to solve this problem using a BLE contact algorithm that does not store actual location data – the BLE tracking app only detects instantaneous “moment in time” contacts.

This week the CDC said that may be surface contamination is not a big source of infections so perhaps this is no longer a big factor.

Does it Matter? May be not?

And then perhaps none of this matters. In Utah, where they have traced the source of most Covid-19 patients’ contacts, about 60% were traced to close family contacts that had the disease and about 25% to close social contacts. That’s 85% of all traced contacts. There were not many random connection contacts leading to becoming infected. Originally, China thought about 60% were due to random contacts – but it might be appropriate to extrapolate that – from congested cities where much of the population travels via public transit – to many U.S. states (like Utah) where public transit use may be just 1-2% of the population.

Moving Further Apart Can Increase Signal Strength!

Another interesting problem with BLE-based tracking apps. They rely on the BLE 1 mw discovery process which can send a signal out to about 10 meters. They combine this with the Received Signal Strength Indicator to estimate that a contact is within about 6-7 feet. In a pure “free space” environment, signal strength can approximate distance. But the real world is not a “free space” environment.

First, if the phone is in your left pocket and you stand several feet from another person on your right, whose phone is in their right pocket, the direct path may have lost 20 db of signal strength due to your body blockage. The software thinks the two of you are far apart when you are actually standing next to each other.

Second, in the real world, radio signals do not travel in a straight line from transmitter direct to receiver. Instead, signals reflect off of objects in the environment. Some times the reflected signals arrive at the receiver in a way that increases their apparent strength and in other cases they arrive in a way that decreases their apparent strength. This is known as “multi-path”.

Think of a pond of calm water. Toss in a rock and see how the waves move across the surface. What happens when the waves strike the shore or another rock – they typically reflect or bounce back creating a new wave front. In a complex environment, there are many wave fronts traversing that water. In some places, wave heights may combine momentarily to create a higher wave, while in others they may combine to create a deeper trough.

The effect of this in the Bluetooth contact tracing app scenario is that – and this has been tested in the real world – there are real scenarios where people moving further apart see a stronger received signal strength!

The software erroneously thinks these two people have increased their risk when they are moving further apart!.

The opposite can also occur – as people move closer together, due to multipath, the devices may sense a drop in signal strength and erroneously think there is less risk.

The Need for Controlled Trials

This is why this technology must be tested in the real world before it is rolled out to entire populations. The bottom line assessment is: Does it detect actual contacts we need to worry about – without missing contacts we do need to worry about?

The Gold Standard: Does it result in an actual, measured decrease in the spread of Covid-19 and a reduction in mortality?

To answer those questions requires controlled trials, just as controlled trials are required for the use of hydroxchloroquinine in the treatment of Covid-19.

Some will argue no controlled trial is necessary as the use of the app is harmless. However, if you are periodically placed in 14-day quarantines – unnecessarily – harming your income and mental health, is that truly harmless?

If not tested, deploying smart phone contact tracing apps is a mass population medical experiment without informed consent – which is illegal in the United States. But given we are dealing with public health, laws no longer matter, of course.

Why “Covid-Tracking Apps” will probably not work

I previously wrote about covid tracking apps and that what a “tracking app” is varies tremendously by country. Please read that first.

There is another issue that crops up – and that’s the effect of conditional probability (or Bayes’ Theorem). I am still reviewing this to see if I did something wrong (I think I did do some things wrong – see Afterwordthe idea is probably sound but the calculations may be wrong) – with reasonable assumptions, phone-based covid-19 contact detection apps seem unlikely to provide a benefit.

Singapore is the only country to have used a Bluetooth-based contact app so far. At the time of this writing, the U.K. is running a trial of BLE-based tracking apps.

Singapore achieved only a 12% participation rate. (Update: as of end of May it was up to 25%, which is still too small. Consequently, Singapore cancelled the smart-phone based contact tracing app).

Continue reading Why “Covid-Tracking Apps” will probably not work

Apple-Google Bluetooth “contact tracing app” depends on widespread adoption to be useful

The biggest challenge for these apps going forward is adoption. The more phones that opt-in to the system, the more successfully it can detect how the virus spreads. Apple and Google say that getting the public to trust the apps and opt-in is critical to the effort.

Source: Three states commit to Apple-Google technology for virus tracking apps

As I explain in detail here, the Apple-Google approach will unlikely have sufficient users to be particularly valuable. If 50% of all smart phone users install the tracking apps, we will have the potential to detect just 16% of close contacts. Is that sufficient to have an impact on the spread of Covid-19?

About 80% of adults have a smart phone. If we assume all 81% have phones capable of running the software (which is not realistic as many people continue to use older phones and may not have compatible Bluetooth Low Energy hardware), then 50% of all users means 40% of adults have the app installed.

The probability of a person having the app is then 0.4. The probability of a 2nd person you meet having the app is also 0.4. The probability that you and a random person you meet both have the app is 0.4 x 0.4 or 16%. Even if all 81% have the app installed, the probability of a detectable contact is at most .81 x .81 or about 65% of the adult population. This does not include children, lowering the contactable percent even more.

Most other countries that have used phone-based contact tracing – so far – have used network-based tracking, not app-based. In network-based tracking, the network tracks the location of all cellular phones – both smart phones and dumb phones with a 100% coverage/participation rate. These systems identify contacts within about a 100 meter radius, which is too broad. But these countries follow up with a robust public health in person contact tracing operation and offer Covid-19 tests so that people are not needlessly placed in 14-day quarantines.

As you can see, the Apple-Google model is unlikely to have sufficient usage to detect many contacts.

Experts criticize ICL’s Ferguson’s Covid SIM model as garbage

Those of us who have seen Neil Ferguson’s ICL Covid sim model have the same views as this computational epidemiologist:

As Ferguson himself admits, the code was written 13 years ago, to model an influenza pandemic. This raises multiple questions: other than Ferguson’s reputation, what did the British government have at its disposal to assess the model and its implementation? How was the model validated, and what safeguards were implemented to ensure that it was correctly applied? The recent release of an improved version of the source code does not paint a favorable picture. The code is a tangled mess of undocumented steps, with no discernible overall structure. Even experienced developers would have to make a serious effort to understand it.

I’m a virologist, and modelling complex processes is part of my day-to-day work. It’s not uncommon to see long and complex code for predicting the movement of an infection in a population, but tools exist to structure and document code properly. The Imperial College effort suggests an incumbency effect: with their outstanding reputations, the college and Ferguson possessed an authority based solely on their own authority. The code on which they based their predictions would not pass a cursory review by a Ph.D. committee in computational epidemiology.

Source: Britain’s Hard Lesson About Blind Trust in Scientific Authorities

Continue reading Experts criticize ICL’s Ferguson’s Covid SIM model as garbage

Covid tracking apps summarized

When people mention “Covid tracking apps” it would be useful to first define what is meant by “Covid tracking app”. There are many approaches in use and many that are proposed. The various methods are remarkably different. When you hear that “Country X used a tracking app and they have fewer cases”, this does not mean they used a tracking app like you have in mind.

Most apps use location data provided by the cellular network itself or on GPS/Wi-Fi position fixes stored on the phone and shared directly with public health authorities.  Some use the data for contact tracing, coupled with free Covid-19 testing, while others use location data to enforce strict geo-fenced quarantine procedures that if violated, may result in arrest and imprisonment. Few existing apps use  close contact tracing based on Bluetooth.

Contact tracing apps, by themselves, appear to provide little value. As we will see, to be useful there needs to be supporting infrastructure outside the app – such as Korea offering Covid-19 testing to those in close contact. And the app must be installed by nearly all smart phone users (and this will miss about 15% of phones that are not smart phones). Most countries are not using  phone-based apps to track location – they are using the phone network to report locations on 100% of phones in use, which is very different than voluntary installation of a tracking  app.

Consequently, when you hear someone refer to “contact tracing app”, you need to ask them to define what they mean by “contact tracing app”.

What follows is a review of various “contact tracing” apps used in different countries.

Continue reading Covid tracking apps summarized

Inflation: Why is the stock market shooting up?

This is not perplexing but by design:

Stock market has the richest valuation in 18 years even as profit outlook worsens

Source: Surge in layoffs is unlikely to help profits, no matter what the market thinks

Update – I see one financial analyst thinks “It’s different this time” and there is little risk of inflation. So there is that view too. Deflation is certainly likely in the shorter term due to suppressed demand (retail sales and services fell by over 16% in April). 

Why the stock market rise? Probably because of future inflation. The U.S. government is printing money like crazy – calling it an “economic stimulus”.

Chart of the U.S. Money Supply from U.S. Federal Reserve:

How will these trillions in spending be paid for? Inflation.

Inflation devalues the dollar making today’s debt’s cheaper to pay off in the future. Governments have always done this. Will this be what happens this time? Who knows for sure?

Inflation taxes everyone simultaneously by lowering the purchasing power of the dollars they hold.

Continue reading Inflation: Why is the stock market shooting up?

ICL Covid-simulation source code

I will not comment on Covid-19 and only make a few comments on the publicly available source code.

  • This is not a comment about whether models should be used – or not.
  • This is not a comment about whether this model’s output is correct – or not (we have no way of knowing either way). Even with the model output being off my very large amounts, we still have no way of knowing.
  • This is not a comment on whether there should be a lock down – or not.
  • This is not a comment on whether a lock down is effective – or not.
  • This is a review of a software project.
  • The review findings are typical of what is often seen in academic software projects and other “solo contributor” projects (versus modern “production code” projects). The issues that often arise in academic projects are due to the nature of individuals or small groups, not trained in software, tinkering with software code until it grows out of control. This likely occurs in other fields but seldom do such works become major components of public policy.
  • When software is used for public policy it needs to be publicly reviewed by independent parties. Until the past month, this code had apparently not been reviewed outside of the ICL team.
  • Models are a valuable tool, when properly used and their limitations are understood. A reasonable model can enable planners to play “what if” scenarios and to adjust input parameters and see what might occur. For example, consider a model for complex manufacturing – we might look at productivity measures, inventory, defect rates, costs of defect repairs, costs of dissatisfied customers, impacts on profits and revenue, supplier issues and so on. If we choose to optimize for profit, then we use our model to find optimal values for each parameter to achieve maximum profit. Or perhaps we optimize for customer satisfaction instead -what happens to our profits if we do that? That is a What-if question. For this purpose the model need not be perfect but at least needs to be “within the ball park”. The key is “within the ball park”. If the model flies off the rails in many cases, it is not a good and accurate model and there is a risk we make seriously wrong decisions.
  • A model may also be used to compare scenarios. We may not need precise future projections for that – instead, if we say, increase X, our model shows high profits, but in another run, we decrease X and show losses. We may not need to know the exact dollar value – only that one path leads to profit and one leads to losses. In this way, precise projections are not always essential.

This code – placed on GitHub – is apparently a revision released by the University of Edinburgh, based on the original source code by Neil Ferguson of the Imperial College of London. They are said to have asked Microsoft and others to help and clean it up and fix defects. Consequently, this is not the exact same code that Neil Ferguson was using to create is models two months ago, but code that has been since updated by others.

This code is thousands of lines of very old C programming.

First thing I noticed was how so much code has been placed in one gigantic source file – 5,400 lines in a single source file. Ouch.

This explains much:

The “undocumented C” argument comes when the author is the only one working on a project and sees no need to document their work. After all, it’s just me! There are two problems with this thinking: (1) over time, even personal projects like this one, grow in size until they become thousands of lines of code. Years later, our understanding of our own original code may not be as good as we think it is. We forget why we made particular design choices. We forget why we assumed certain conditions or values. Bottom line: over time, we forget. And (2) personal projects like the Covid-19 simulation eventually became the basis of major public policy and others are asked to review, check or modify the code base. No documentation puts the entire model at risk. This is not the right way to do these kinds of software projects, particularly when this is the basis for advising world leaders on major public policies that impact billions of people.

Continue reading ICL Covid-simulation source code