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 Afterword – the idea is probably sound but the calculations may be wrong) – but so far 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.
The probability that a person has the app on their phone is then p=0.12. The probability that a 2nd person has the app on their phone is also p=0.12. The probability that two strangers coming within close range both have the app installed is just .0144 or 1.44% – almost zero.
There is another problem with tracking apps due to Bayes’ Theorem and that the true incidence of Covid-19 in the sample population may be very low.
- Let’s assume that 1 in 500 people are currently carrying Covid-19 and could spread the infection.
- Let’s assume 100% of everyone has the Bluetooth app installed, and 100% of the population has a smart phone, and its on all the time.
- Let’s assume that the app is 90% accurate in finding a true contact, and 10% of the time, it flags a contact we do not need to worry about (a false positive). In reality, I suspect the accuracy is quite a bit less than 90%, but let’s work with the 90% figure for this example.
Out of 500 potential contacts, 10% or 50 might be flagged as false positives and 1 person might be flagged as a true positive. We have now flagged 51 people in the population. (But see Afterword for the problem with this calculation.)
But we know we expect only 1 true positive out of all those sampled – but we now have 51 positive results. We do not know which one of those 51 is the true positive. For any individual in that set, the odds that they are the actual true positive is 1/51 or just 1.96%.
Continue reading Why “Covid-Tracking Apps” will probably not work
Thousands of cooped-up Americans have been snapping up new bicycles or dusting off decades-old bikes to stay fit, keep their sanity or have a safe alternative to public transportation.
Source: Pandemic a boon for the bicycle as thousands snap them up | KOIN.com
Additional points left out of that story:
- Bicycling has a high incidence of crashes and injuries.
- Vehicle accidents are the only segment for which there is good data so this is what nearly all news stories cover.
- Vehicle accidents account for a fraction of bike related accidents and injuries.
- Most bike crashes (probably 90%) involve roadway hazards, debris, potholes, sewer grates, collisions with other bicyclists, pedestrians, dogs, other animals, bike break downs, etc – or alcohol on board the bicyclist, or other bicyclist or vehicle driver, or on board everyone.
- Fatalities are rare (less than 1,000 per year) but most involve a collision with a vehicle. (Pedestrian versus vehicle fatalities are about 8x more than vehicle versus bike-related fatalities.)
- About 1/2 million bicycle-related injuries are treated by ERs (ER’s collect and report this data). An unknown number are treated by individual physicians (not in ERs) and an unknown number are not treated at all. Some estimate total injuries in the 1-2 million per year range. Keep in mind – we have no hard data on how many people ride bikes, how many miles they travel, the nature of their riding nor a count of injuries-we have to rely on surveys. Hard data on crashes not involving vehicles is nonexistent. There is tons of research on fatal bike accidents and vehicle-related crashes but little on non-vehicle crashes. And like the data here, it is not weighted by participation rates.
- Almost all bike safety initiatives are geared to reducing car versus bike crashes. Almost none address bike versus roadway hazards, which account for most bike crashes. Bicycling advocates are focused on the bike versus vehicle category, unfortunately.
- In the U.S. the use of bike helmets is estimated to cut the risk of brain injury in half. Bike helmets distribute impact over a wider area of the head, reducing the incidence of bone fractures but may not reduce brain injuries by as much – brain injuries often come from the brain’s movement inside the skull.
An increase in bicyclists, as suggested by the article, if continued long term will lead to an increase in injuries and an increased demand for (expensive) medical care services. If the trend continues as post-pandemic response, this will lead to an increase in injuries and costs as a consequence of the pandemic.
There is support for that conclusion in published literature, including this example:
Continue reading Looming pandemic of bicycling-related injuries in post Covid-19 world?
Yes! This is a permanent elimination:
The University of California board of regents, in a landmark move that could reshape the college admissions process across the country, voted Thursday to drop the SAT and ACT testing requirement.
Source: UC regents unanimously approve plan to drop SAT and ACT from admissions – SFChronicle.com
My own state has also eliminated SAT and ACT tests for public college admissions, starting this fall.
As the victim of a GRE testing failure decades ago that shaped my entire life, I am thrilled to see the end of these absurd testing regimes.
In the case of the GRE, the year I took the exam they introduced a new section to evaluate a student’s “logical reasoning” ability. Even though I scored above the 90th percentile on English and Math, I scored 28th percentile on this new section. That’s a failing grade.
Years later I learned, hidden in a footnote, that colleges had been instructed to ignore the “logical reasoning” score the year I took the exam, as there were errors in the test. GRE never notified students of these errors in their test. Consequently, I made wrong decisions, with life time impacts, based on their incompetence.
Numerous graduate programs are now dropping the GRE requirement too!
This is fantastic news! Standardized testing grew out of tests given to military recruits in the early 20th century – the tests were designed to filter out minorities. Some think the tests have continued to do that – we know that SAT scores correlate very well with household income, for example.
The tests appear to filter out students based on cultural backgrounds and household income – something that is not good for anyone.
Don’t think it worked for me:
There’s mounting evidence that brain damage has the power to unlock extraordinary creative talents. What can this teach us about how geniuses are made?
Source: The Mystery of Why Some People Become Sudden Geniuses
Having had 5 mild TBI and one moderate TBI, it’s hard to think about this 🙂 Also, I do not recommend injuring your brain as an approach to becoming smarter.
While my life turned out well, many opportunities went missing due to dealing with gremlins of past brain injuries that were not diagnosed and treated until about 18 months ago.
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.
The shift to remote work has led some companies to come up with quick digital solutions for tasks that have become hard to tackle during the coronavirus pandemic.
Source: Emptying Offices Prompt Adoption of Low-Code to Build Work Apps
“Low code” means using “drag and drop” tools to create software applications. These systems make the creation of user interfaces easy, and provide functionality through similar drag and drop interfaces. We used to call this “Rapid Application Development” or RAD.
I have long though future software would be created with advanced tools that simplify the development process, particularly for straight forward applications of modest size. MIT App Inventor, and Scratch, are examples of drag and drop programming interfaces. Scratch is for teaching programming concepts to children. App Inventor leverages the Scratch concept into developing mobile apps for Android. You can learn about Microsoft’s Power Apps feature here.
According to the U.S. government:
Energy efficient. EVs convert over 77% of the electrical energy from the grid to power at the wheels. Conventional gasoline vehicles only convert about 12%–30% of the energy stored in gasoline to power at the wheels.
Source: All-Electric Vehicles
Of course, one must also include the conversion of the original fuel source into electricity but that depends on the original fuel source: oil, natural gas, coal, nuclear power, hydro, geothermal, solar, wind and so on – and can vary widely.
Regardless, gasoline engines are not very energy efficient in terms of turning the energy in the fuel into forward motion, as noted above and also noted here. EVs have a slightly different problem – while they use electricity efficiently, EVs weigh quite a bit more than a similar sized gasoline vehicle.
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
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