Author Archives: rdmyers75@hotmail.com

coronavirus trackers and simulations revisited

Update: December 13, 2020 (and from time to time since then, I update links if I notice they are broken)

This post is getting a surprising amount of attention. I don’t normally update posts, but I am updating this one since it is getting attention and the commentary in the original post is significantly outdated. Rest assured, if you are a historian in the far future studying what I was thinking back in June 2020, I have kept the original post at the bottom. I am keeping all the links, just grouping them somewhat and removing (from this section) the outdated commentary. (Thank you, Word Press, for making a simple copy-and-paste operation like this beyond excruciating.)

Data Trackers

  • Johns Hopkins – map, stats, access to data sets
  • New York Times – a national (U.S.) map by county and plots by state (now, with a paywall! as of 7/30/21. Which I will never pay because WEAPONS OF MASS DESTRUCTION!)
  • Financial Times – similar to others, but they look at excess deaths a little differently and have some interesting graphics
  • BBC – similar to NYT, but international
  • CDC – changed this link to their “COVID-19 by County” page on 2/26/22; the updated recommendation is to mask indoors if new cases in your county are 200,000 per 100,000 population per week, AND if the number of people entering the hospital and/or in the hospital is above certain thresholds. It’s a little hard to find the data and figure out yourself, so if you trust the CDC (and who wouldn’t?) you can just type in your county and they will tell you if it is high/medium/low.
  • https://coronavirus.thebaselab.com/ – a variety of maps and plots
  • City Observatory – intermittent data-based articles and maps
  • Our World in Data – excellent interactive country-level data, maps, and plots. A tip – you can also type in “world” or the name of a continent in the country box.
  • https://aatishb.com/covidtrends/ – a very clever animated time series of growth in cases over time, by country
  • Reuters – just more numbers and maps, similar to NYT
  • Covid Act Now – state-level data and communication in a simple, easy to understand index format
  • Harvard Global Health Institute COVID Risk Levels Dashboard – similar to Covid Act Now, but less simple and less easy to understand. Seems to have more ability to drill down into county-level data, although when you do that much of it is blank.
  • Wastewater surveillance from “Biobot Analytics” – added 4/30/22.

Simulations

  • University of Washington IHME – the best place I have found for understandable future projections. At the state level.
  • FiveThirtyEight – compares different models (no longer updating as of 7/30/21)
  • https://covid19risk.biosci.gatech.edu/ – This site calculates the probability that someone in a group of a given size is infected, based on the estimated rate of active cases in a U.S. state.
  • MicroCOVID – a risk calculator based on local data and allowing you to adjust your risk tolerance and try out various scenarios (added 8/8/21), such as “one night stand with a random person” (on the latter, please remember there are other diseases besides just Covid-19, for example antibiotic-resistant syphilis…)
  • Covid-19 Forecast hub – another visualization of various models and ensembles of models

Vaccine Trackers

Local Pennsylvania/Philadelphia Interest

  • The state of Pennsylvania has a useful dashboard which they have now made public (or it was public before and I didn’t notice.) It compares cases, positive tests, and hospital data for the current and last 7-day period, at the county level.
  • Speaking of Philadelphia, a shout out to the Philadelphia Health Department which provides some open downloadable data.

Miscellaneous Stuff

Original Post (June 27, 2020)

I decided to list out and summarize the variety of trackers and simulations I’ve mentioned in previous posts. Like many people (in the U.S. Northeast at least), I was glued to coronavirus info on various screens from roughly mid-March to mid-May, then my attention started to gradually drift to other things as the situation got better. Now, it seems that it has either stabilized at a not-quite-out-of-the-woods level, or is slowly reversing itself as we see other parts of the country start to be affected more seriously (sorry if you are reading this and are being affected, we in the Northeast take no pleasure in your suffering, I promise, although we suggest you turn out any bigoted anti-science politicians in your area who are letting this happen.) Anyway, I find that I am interested in starting to look at trackers and simulations again on a daily basis. These are in the order I discovered them.

  • Johns Hopkins – a neat map early on, although now the entire world has become a blob. Still a good place to stare at data.
  • New York Times – a national (U.S.) map by county and plots by state. seems to load even though I have used all my free articles for the month.
  • BBC – they update continuously but I’m not sure if this link will be to the latest
  • CDC – this is what I would have predicted would be the go-to source of information and expertise if you asked me before all this started…but it’s mediocre at best. Yes, that just about sums it up.
  • https://coronavirus.thebaselab.com/ – a variety of maps and plots to stare at, not my first stop but a little different if I am tired of others
  • University of Washington IHME – still the most informative state-level simulations I have found, accounting for hospital capacity among other things
  • City Observatory – they did an awesome analysis by U.S. metro area, which I have not seen anyone else do (human beings interact with each other socially and economically in cities and their suburbs, which often cut across states, and states often contain metro areas that are not connected much socially or economically. Economists, social scientists and urban planners know this of course, but nobody else studying the epidemic seems to have figured this out. Seriously, other data visualization and simulation sites, you can do this, it’s just a matter of grouping data by counties.) Unfortunately, they quit updating it and have not automated it. I still check every now and then to see if they have picked it up.
  • Our World in Data – pretty much every conceivable way of looking at data by country. I like to look at confirmed deaths per million across countries. By this measure, the starkest contrast is east vs. west. The eastern countries were hit first, hard, and without warning, and their death rates are very, very low. They have a variety of government types, responses, ethnicities and cultures. I just don’t think anybody has come close to explaining it. The U.S. is in the middle of the pack of western countries, which somewhat contradicts conventional wisdom and suggests news organizations are making the obvious error of not normalizing by population.
  • https://aatishb.com/covidtrends/ – an animated time series of new confirmed cases in the past week vs. total confirmed cases, both on a log scale, by country. As I write this, shows the beginning of a concerning uptick for the United States, and Brazil out of control.
  • Reuters – I actually never wrote about this one, but it has a map and some numbers.
  • FiveThirtyEight – they have an aggregation of various simulation models out there. New York and New Jersey look like a stream sprayed horizontally out of a garden hose, while Texas and Florida (today) look more like a fire hose.
  • https://covid19risk.biosci.gatech.edu/ – This site calculates the probability that someone in a group of a given size is infected, based on the estimated rate of active cases in a U.S. state. I assume it’s estimated active cases, anyway, or it wouldn’t make sense. It would be better by metro area (seriously guys, someone just get this done), but still a nice idea. I’m in Philadelphia, but I figure the New Jersey numbers are probably the most applicable.
  • Covid Act Now – provides a composite risk index at the state level, and county when county level data is available in the right format (which is not that often)
  • Harvard Global Health Institute COVID Risk Levels Dashboard – keeps it simple with just data on new cases, but gives you a variety of nice mapping, charting, and tabular formats to slice and dice the data at country, (U.S.) state or county level.
  • The state of Pennsylvania has a useful dashboard which they have now made public (or it was public before and I didn’t notice.) It compares cases, positive tests, and hospital data for the current and last 7-day period, at the county level.
  • Speaking of Philadelphia, a shout out to the Philadelphia Health Department which provides some open downloadable data.
  • I look at the FAO food price index on occasion. It’s falling lately. Sometimes I look at oil and gold prices, and how many Special Drawing Rights can be bought with one U.S. dollar. Oh and, the Rapture Index is at an all time high!

IMD World Competitiveness Ranking

The United States fell from 3rd to 10th in the IMD World Competitiveness Ranking this year, after being 1st just a couple years ago. Asian tigers (Singapore, Hong Kong) and Scandinavia/Northern Europe (Denmark, Switzerland, Netherlands, Sweden, Norway) make up most of the top 10, when Canada and UAE making the cut, and Taiwan just edged out at #11.

For the second year in a row, the USA failed to fight back having been toppled from its number one spot last year by Singapore, and coming in at 10th (3rd in 2019). Trade wars have damaged both China and the USA’s economies, reversing their positive growth trajectories. China this year dropped to 20th position from 14th last year.

IMD

City-states tend to do well, so my quick reaction is that it might make more sense to compare Singapore and Hong Kong to, say, the New York City or Toronto metro areas rather than the U.S. and Canada as a whole.

pandemic reinsurance

You can buy insurance against a pandemic. Well, if you are a giant corporation or a small country. It seems like insurers wouldn’t be able to offer it, but some of the reinsurers, which insure insurance companies against rare catastrophic risks, actually do. They do it by finding parties that can insure them, and the parties that are willing to insure them are pension funds, because when old people start dying in large numbers pension funds actually have a lot of extra cash lying around. The bigger the pandemic and the more people are dropping like flies, the more cash they have to pay off the reinsurance companies. Yes, the insurance business is kind of sinister, so there it is. From Wired.

house of cards

James K. Galbraith has a very pessimistic view of the U.S. economy going forward.

America’s economic plight is structural. It is not simply the consequence of Trump’s incompetence or House Speaker Nancy Pelosi’s poor political strategy. It reflects systemic changes over 50 years that have created an economy based on global demand for advanced goods, consumer demand for frills, and ever-growing household and business debts. This economy was in many ways prosperous, and it provided jobs and incomes to many millions. Yet it was a house of cards, and COVID-19 has blown it down.

Project Syndicate

Slow, underlying trends can undermine the resilience of a system, without obvious impacts on the surface. Then, when a crisis hits, whether or not that crisis is related to the underlying trend, the system is not able to bounce back the way it would have without the trend. Imagine rising temperatures and invasive species very slowly putting pressure on a healthy forest or water body. The ecosystem can resist these pressures, maybe for a long time. But then one day, a major storm, fire, or drought comes along. Absent the underlying pressure, the the ecosystem could have rebounded to its original state, but with the underlying pressure, it rebounds to something short of its underlying state. Even if the shock is less than catastrophic and the system rebounds to something just a little short of the original state, successive crises over time can lead to a long, slow slide that might only be obvious in retrospective. Or, if the shift is very slow, “shifting baseline syndrome” sets in, where the people involved lose their memory of what the system used to be like, and don’t fully realize what has been lost.

missiles, drones, and mines

I was reading an article recently (which I can’t find at the moment) arguing that the future of warfare is a large number of cheap missiles, drones and mines that make it almost impossible for an adversary to get close enough to attack you. This was put forth as a recommended strategy for the United States – we can give or sell these to our allies, flood the world with these things and make money in the process. It just seems cynical to me because today’s allies are not always tomorrow’s allies. Training Aghan freedom fighters in terrorist tactics seemed like a good idea at one time too.

teeth: miracle or weakness of evolution?

I’ve always thought that teeth might be the weakest point of the human body. Why did our teeth evolve to be made of calcium, which dissolves in acid, when pretty much all our food is acidic? Why do we have to strap metal torture devices to children’s teeth for years just for them to be reasonably straight? Why don’t animals seem to have these problems?

This article in Scientific American sings the praises of teeth. It argues that, like many of our other organs and systems, our modern lives just aren’t what they evolved to deal with. It basically comes down to the idea that our food is too sweet and too soft.

The evolutionary history of our teeth explains not only why they are so strong but also why they fall short today. The basic idea is that structures evolve to operate within a specific range of environmental conditions, which in the case of our teeth include the chemicals and bacteria in the mouth, as well as strain and abrasion. It follows that changes to the oral environment can catch our teeth off guard. Such is the case with our modern diets, which are unlike any in the history of life on our planet. The resulting mismatch between our biology and our behavior explains the dental caries (cavities), impacted wisdom teeth and other orthodontic problems that afflict us.

Scientific American

I admit, I don’t like working for my food – I like boneless, seedless, shell-less everything. My teeth may have paid the price.

integrating movement ecology and biodiversity research

This article talks about two sub-disciplines of ecology that have developed independently and would benefit from more integration. One is about the movement of individual animals, whether natural or fragmented/impacted by humans. The other is about the variety of organisms and how they interact with each other in habitats.

Editorial: thematic series “Integrating movement ecology with biodiversity research”

Bridging the gap between biodiversity research and movement ecology is possible. First integrations demonstrated that individual movement capacities and strategies are critical in determining the persistence of species and communities in fragmented landscapes, with changing climatic conditions, or in the presence of invasive species. At the same time, the ever-increasing human impact on nature puts long-established movement patterns in jeopardy, and organismal movement is changing perceivably across scales. Yet, a full-fledged integration of movement ecology and biodiversity research is still in its infancy. Empirically, we need more studies that not only focus on the movement of individuals, but also how they interact, while moving, with their environment and with other individuals, including their own and other species. From a theoretical viewpoint, there is a lack of modelling approaches that integrate individual movement and its consequences with population and community dynamics.

Movement Ecology

This could potentially be helpful at a time when remaining natural habitats are becoming increasingly fragmented, and are interspersed with agricultural, urban and suburban environments. All this could be optimized, given the right theory. Professional and political understanding and willingness to act would have to follow, of course, but doing the science would be a necessary first step.

Technosols

A technosol is an artificially created planting/structural medium from manmade materials, such as construction debris and compost. This article from Ecological Engineering journal says a mix of 20% “excavated deep horizons” (in layman’s terms, I think this is just dirt from construction sites), 70% crushed concrete, and 10% compost might work. If we truly want green cities, and we don’t want to reduce natural habitats to wastelands by harvesting materials from them to green our cities, this could be a good approach.

May 2020 in Review

You can’t say that 2020 has not been interesting so far. The Covid-19 saga continued throughout May. I certainly continued to think about it, including a fun quote from The Stand, but my mind began turning to other topics.

 

Most frightening and/or depressing story:

  • Potential for long-term drought in some important food-producing regions around the globe should be ringing alarm bells. It’s a good thing that our political leaders’ crisis management skills have been tested by shorter-term, more obvious crises and they have passed with flying colors…doh!

Most hopeful story:

Most interesting story, that was not particularly frightening or hopeful, or perhaps was a mixture of both:

  • There are unidentified flying objects out there. They may or may not be aliens, that has not been identified. But they are objects, they are flying, and they are unidentified.