Exponential Growth Part 3 (Apr 27)

In past posts I’ve talked about big exponential growth. The scary kind. Covid-19 was like that for most of the world in March. Multiplying by 2 or 3 every 4 or 5 days makes rapid changes.

Fortunately, thanks to social distancing, the pandemic settled down in April. People are still being infected, but in most places the number of new cases is close to constant. That means the Rt growth rate is about 1 (with some exceptions).

Here is a chart of small exponential growth rates.

Back when humans went to work, parties, restaurants and sports arenas, the Rt growth rate for Covid-19 was mostly between 2 and 3. Those would be almost vertical on this chart. The more horizontal curves come from a world that still has social distancing. Their shape depend on how much it reduces the infection rate. If the rate is more than 1, cases increase. If below 1, they gradually subside.

Small changes in the rate have a big impact. For example, if a region has hospitals currently at half-capacity, then an Rt of 1.05 overwhelms them in June. 1.04 hits crisis in July, 1.03 in August, 1.02 in October, and 1.01 postpones it til early 2021. 1.0 prevents it entirely.

One challenge is, there’s no way to know the current exact Rt number. The Rt Tracker website calculates a value for each state based on testing data and other factors, but their model is based on a lot of guesswork. The site include error bars above and below each estimate, to show how much uncertainty there is.

Even worse, there’s no way to know the impact each policy decision has on Rt. If everyone wears masks in public, the infection rate surely goes down: but how much is just a guess. Ditto for every other change in daily life. In a year or two there will be tons of data to help calculate impacts, but right now we’re on brand new terrain.

Even more worse, the system is complex and sensitive. The drastic changes in March dropped Rt all the way from 2.7-ish to 1.0. Small decisions like mask-wearing or opening restaurants could easily shift the Rt value up or down by several or many times .01. After a few months, that makes a big difference.

Governors, mayors and local health departments will make policy decisions. Individuals and business owners will make personal decisions. The net result will be some Rt number that won’t be known until infection data comes in, several weeks or a month later. That feedback delay can make the ride even bumpier.

If you’ve ever lost control of a vehicle on snow or ice, you probably experienced a feedback/delay loop. Skid to the right so you steer left, but there’s reaction time so you over-compensate. That leads to a few left/right cycles of increasing magnitude, until you’re backwards in a ditch.

Covid-19 will probably be similar over the next year or two. Odds are good there won’t be a whole giant pandemic again, but expect lots of little outbreaks. Local ones on the scale of factories or cities. Maybe some on the scale of states or countries or continents. The rules will need constant tweaking. Fasten your seat belts, folks.

Dennis Kolva
Programming Director

Macintosh & ARM (Apr 23)

Bloomberg News reported today that Apple will start using their own A14 ARM chips in new Macintosh models. It will be a gradual transition away from Intel, starting in 2021.  This chip switch has been rumored for years.

Apple is “working on” a way to run current apps on the new chips. It’s almost certain to be similar to previous pivots (68000 to PPC, then PPC to Intel). First, there will be an emulator with mediocre performance. It gradually will get worse, then end after 5 to 7 years.

The future ARM Macs will be better at running iOS apps. That’s great if you want a big-screen, more powerful version of your iPhone or iPad. It’s not so great if you are running current Mac software.

Whether Goldenseal Pro will run on ARM Macs depends on Apple.

      • If they continue long-term support for Cocoa and Objective-C, then we can make it work with another year or two of effort. We’d start that when sure of Apple’s plans, and after the Windows version is done.
      • If Apple unveils something new for developers, then it depends on the details. Could be better, could be worse.
      • If Apple switches entirely to Swift and SwiftUI, there’s no way we can convert our code. After 33 years, it’ll be end of the line for our Mac version.
      • Ditto if Apple sorta supports Cocoa, but are half-assed about it. Cocoa is already a nightmare, and our staff is not eager to go back there. Since this is the likeliest case and Swift-only is 2nd, I’m glad we stopped work when we did. A waste of only 3 years instead of 4 or 5.

To keep current Mac users, Apple will need to support Microsoft Office and Adobe products for sure. Everyone else doesn’t matter very much. Specialty apps like ours don’t matter at all.

So, we just have to wait and see.

Dennis Kolva
Programming Director


More Coronavirus Resources (Apr 21)

As a math nerd, I have a huge crush on Vi Hart. Usually she makes fun math videos, but she just released one that talks about how to unwind from Covid-19. The timeline is probably too optimistic, but otherwise it seems pretty sensible. Political will isn’t there yet to do it nationally, but maybe a few states can start that way.

The founders of Instagram just made a website that tracks RT, the current rate of increase for Covid-19 in each state. If it’s above 1, infections will increase exponentially. If below 1, they’ll decrease. Just one number for each state, with graphs of how it has changed over time.

Reddit has a few sub-reddits that cover the virus, with interesting links and lively discussions. r/Coronavirus is the most active, with many armchair epidemiologists. r/Covid19 is more science related, with research papers. r/CovidProjects is about mask production and similar pandemic efforts. For random, interesting science, r/Biology and r/Science are also good.

Science magazine has free access to its Covid-19 research articles. Research papers are hard to digest, but they usually have an abstract that is more readable.  Science also publishes articles for the general public, such this one about the way Covid-19 sickness plays out.

Meanwhile, I think the Internet has a big problem with bad information. Rumors and propaganda have always been out there, and people have always believed them. But, production values are better now. It’s easier to present compelling arguments that are wrong. Even dangerous. Bad info makes people do stupid stuff like burning cell towers.

The antidote is science. There are over 30,000 scientific journals out there: everything from Abnormal Psychology to Zygote. Most are extremely specialized, but the best of their research makes it into more general magazines like Nature, Science and Scientific American.  I devoured the latter as a kid, but moved on to the harder stuff.

Science research papers usually take a year or more to be published. The delay is mostly because of peer review. Other experts in the field check them over with a fine-tooth comb. The authors then revise and re-revise. Sometimes more experiments need to be run. Sometimes they need a complete do-over. It’s a grueling process, but the result is accurate info.

The system isn’t perfect, but it’s way more trustworthy than Facebook, Fox News or talk radio.  I’d highly recommend giving it a try. Things have sped up a bit for Covid-19, and serious papers are starting to come in.

Dennis Kolva
Programming Director

Science & Politics (Apr 17)

There’s something called the Dunning-Kruger Effect, which says that incompetent people don’t see their own lack of skill. The name comes from a 1999 study, when a Cornell research team tested undergrads for humor, grammar and logic ability. The subjects judged their own talent to be above average at 55% to 75%. Actual competence ranged from 0% to 100%.

The idea has been around for a long time, as in “a little knowledge is a dangerous thing” or “hold my beer”. The paper won an Ig Nobel prize in 2000 because it was so obvious.

I suspect that the results would be different if based on small business owners in their 50s or 60s, rather than late-teen/early-20 Ivy League students. From hard experience, many people learn how to accurately rate their own competence. All it takes is a couple of rashly-made construction estimates, and you quickly gain some (expensive) humility. Likewise, it’s hard to run a successful business long-term if you can’t judge skill levels in yourself and others, and respect them.

Of course, not everyone matures with age. Wisdom seems to come from hard knocks, and some folks are rich enough, pretty enough or charismatic enough to avoid them. Unfortunately, because folks like that are attractive, they often gain power as politicians.

I’d say the recent history of Covid-19 is full of Dunning-Kruger. Too much prideful incompetence in leaders who believe they are much smarter than a virus.

Epidemiologists have been predicting for years that something like Covid-19 would happen. It doesn’t take rocket science to look at H1N1, SARS, Ebola and MERS, and guess that more might be on the way. Those scientists are the real experts, but their voices have too often been drowned out by people in power who have a “gut feeling” that they know better.

Science is hard. It requires patience and discipline, and a willingness to endure tedium without much reward. With rare exceptions, the folks that do it are not charismatic. But, the whole purpose of science is to understand some small bit of reality better. Successful scientists need to be extremely competent in their field.

The path into this mess has many people to blame, or maybe it’s just normal human biases that are at fault. Getting out will need a ton of competence, or it’s going to get much, much worse. The problem is not just the disease, but all the social and economic fallout.

There is a silver lining to all the mistakes: they will provide great data for scientists, over the next few years.

Norway and Finland shut down, but Sweden didn’t. Their counts will be interesting. Angela Merkel is a former research chemist, and Germany has unusually low death rates. Is there a connection? Leaders in Russia, Brazil and the US played down the risks much later than most other countries. That will create interesting data. China and Iran covered up. If their actual numbers ever come out, it’s bound to be worth something.

Dennis Kolva
Programming Director




Covid-19 in New York (Apr 13)

The IHME website predicts that Covid-19 is now peaking or past peak in New York and a couple neighboring states. Also Washington, California and Hawaii. New hospitalizations in those states are expected to decline close to zero by early May. The rest of the US is 2 or 3 weeks behind that schedule. Most states are predicted to peak around May 1, then decline gradually into early June. That assumes social distancing continues through May.

Websites keep appearing with new ways to look at the pandemic. The Unacast site has a Social Distance Scorecard for every state and county, calculated from cell phone location data. The letter grade is based on total travel, non-essential travel, and “encounter density” (how often people are very close to each other). Since we are ahead of the curve here, I’ll cover how it rates a few NY counties, and how it matches with their actual Covid-19 data.

First up is Tompkins County NY, home to Ithaca, Cornell and TurtleSoft. Population about 100K. Encounter density was near the US average in February, but it dropped steeply Mar 13, when all the students went home. Since then we have been very good about testing and isolating. Many people wear masks now, and the sidewalks are mostly empty. 113 confirmed cases as of yesterday, 84 recovered, 4 in the hospital, 0 local deaths.  It’s calm enough that 100 local health workers went by bus to NYC last week, to help out. I think we did it right.

Next is Tioga County NY, where TurtleSoft first began. Mostly rural, with one medium-size village and a few small ones.  Right now it has 19 confirmed infections and 0 deaths, rising slowly. Its encounter density was low to begin with (25% of US average) and dropped from there.

In contrast is Steuben County NY, two to the west of here. Population similar to Tompkins, but 147 cases and 11 deaths so far. Unacast says they are distancing less well, especially for non-essential visits. Of all the rural upstate counties, it’s doing the worst.

Then there is downstate, which is having a very rough time. As of this writing, New York City has 106,763 cases and 7,349 deaths, with another 100K cases and 4K deaths in surrounding counties. It’s worse than most countries. In normal times, the encounter density is about 3000 times the US average in Manhattan, and 500x in Brooklyn/Queens/Bronx. Those numbers are way down now, but even in shutdown it’s still more crowded than the US average from pre-Covid times.

Low population density does not guarantee low infection rates, but it sure seems to help. That may be good news for the 3/4 of the US that expanded during the Automotive Era. Walkable cities with good public transport are fun to visit, but they’re also better at spreading respiratory disease. So far the more car-centric regions have lower infection and death rates. There’s something to be said for isolating inside a big yard, and traveling in a personal sealed metal box.

San Francisco is an exception to the density correlation: it has high density but low rates of Covid-19. Probably because its mayor shut everything down in early March. Seattle also did fairly well, despite their early start. They tested and tracked cases aggressively.

(Edit Apr 15: this link gives a different take on Covid-19 in rural areas).

Stay safe, folks.

Dennis Kolva
Programming Director


Covid-19 and Construction #3 (Apr 8)

New York State shut down non-essential businesses on Mar 23. A bit more than two weeks ago, but it seems like ages.

Construction was included in the list of essential businesses. Some projects continued for a while, but they are all shut down completely now. Getting UI plus $600 a week was probably the carrot to stop working, and the increase in local infections was the stick.

An employee in a local supermarket tested positive for Covid-19 on Saturday.  So far 500 people have gone for testing because of it, but results aren’t back yet. I checked in at the (different) supermarket where I left N95 masks last week: apparently the manager gave them to her family members. So much for making it safer for everyone else in the county.

People are dying in New York City, but it’s less serious upstate. This site has projections for hospitalizations vs hospital beds for each country and state, assuming current social distancing. It predicts that deaths in NYS will peak today and then drop steadily to almost zero at month end. Last week the site was pessimistic about ICU beds for most states, but it’s looking better today. Hopefully their math and assumptions are accurate.

I went back to Cornell 2008 to 2013 and finished a degree in Molecular Biology. It’s fascinating to read the scientific journal articles that cover SARS and Covid-19.  Coronavirus in general has many sneaky ways to bypass our robust anti-viral systems. It won’t be easy to create a vaccine against it.

In a sense, the world was lucky with Covid-19. It definitely has been scary enough that nobody will ignore its family in the future. Good thing, because SARS had a 10% death rate and MERS 34%. This pandemic could have been a whole lot worse.

If you read European history, from the Stone Age until the 20th Century it was a constant stream of war, epidemics and famine. However, the last big war ended in 1945, the last big epidemic was 1909 and the last famine in USA or Europe was 1814. Since then, most of us have had a comfortable, peaceful, prosperous time. We have smartphones, Internet, laser levels, cordless power tools. It’s unsettling to have death sniffing up close again.

But, here we are. It’s time to think about where to go from here.

Dennis Kolva
Programming Director




Exponential Growth Part 2 (Apr 3)

Construction estimating uses a lot of math, but it’s simple stuff.  Multiply unit costs times quantities, add them up, and you’ve got a bid.  There might be a little subtraction or division here and there, but nothing more complicated. Construction accounting is even simpler. Thank goodness for sales tax, which at least has percentages to make it more interesting.

As a math nerd, it’s exciting to work with more than just basic arithmetic. So please forgive me as I indulge in a bit more Covid-19 amateur epidemiology.

Recently I mentioned exponential growth, with a series that starts out 1-2-4-8-16-32.  That’s powers of two, which is important for software developers. However, it’s not the only type of exponential curve.

Globally, Covid-19 probably has an R0 basic reproduction number of about 2.7. With that multiplier, the series goes 1-3-7-20-53-143-387-1046. Seven jumps to 1000, not ten. At 4 or 5 days per jump, it’s roughly 1000x per month. Two months makes a million, where we are at now globally. Three months tops a billion.

That R0 value is just an average. The actual growth rate depends on the virus, which can mutate. Usually diseases evolve to become more infectious and less lethal, but that’s not guaranteed. It also depends on the human hosts. If they are densely packed and mobile, the actual R is higher. If socially isolated, it’s lower. The whole point of ‘flattening the curve’ is to reduce R down to something close to 1, so the expansion rate is not so scary.

The series for a R0 value of 1 goes like this: 1-1-1-1-1-1. Still new infections, but they are offset by older cases that either recover or die. Rabies is a disease that acts like that. Nasty, but uncommon. Good thing, because a rabies that spread like Covid-9 would truly be the Zombie Apocalypse, except everyone would end up dead instead of undead.

Unchecked epidemics start out exponential, but eventually they start to run out of hosts. The R value declines to 1, and then all the way to zero.  Overall, the cumulative case count makes an S shape, sometimes called the ‘logistics curve’. It grows steeper and steeper for the first half, then flatter and flatter for the remainder. The number of new cases each day makes a bell curve.

The good news is, the rate of increase globally has started to slow down. Last week I predicted a million cases by Monday, but it took until Thursday instead. Most likely, social distancing is starting to work. Not everywhere, but the data looks promising for much of Europe, and a few parts of the US.

Sadly, much of the US is still in the early growth stage, where it looks easy peasy. In a week or two or three it won’t be.

Dennis Kolva
Programming Director