Host, parasite, and failure at the colony level: COVID-19 and the US information ecosystem

Trump campaign attempts to remove satirical cartoon from online retailer | Comics and graphic novels | The Guardian

This cartoon is offensive. And yet.


A few months ago I did some reading about viruses and other parasites, inspired by the obvious, but also those ants that get commandeered by cordyceps fungi, as seen in this awesome Richard Attenborough video:

Besides the incredible feat of programming ants to disseminate fungus spores, the video reveals two other astounding facts about this system. First, worker ants from afflicted colonies selflessly identify and remove infected ants and dump their bodies far away, reflecting intergenerational genetic training as well as the ability to gather and process the information necessary to make the diagnosis and act on it. And second, there are many, many cordyceps species, each evolved to prey upon only one species, reflecting a pattern of co-evolution between host and parasite.

This led me to reading about colony defenses in general, including not just ants but things like wasps and termites that leave chemical protection for future generations, and bees getting together to make hive fevers to ward off parasitic infections. I don’t find a video of exactly a hive fever, but this one is similar: It’s bees using their collective body temperature to cook a predatory hornet to death:

Incredible. That got me thinking about how information management and dissemination is vital to colony-level defenses against parasites. They need to process and transmit information to work together in the arms race against parasites (especially viruses) that usually evolve much more rapidly than they do.

And you may know where this is going: How the US failed against SARS-CoV-2. In an information arms-race, life and death struggle against a parasitic virus that mutates exponentially faster than we can react — who knows how many experimental trials it took to design SARS-CoV-2? — this kind of efficient information system is what we need. And it worked in some ways, as humanity identified the virus and shared the data and code necessary to take action against it. But clearly we failed in other ways — communicating with our fellow citizens, dislodging the disinformation and misinformation that clouded their understanding and led so many to sacrifice themselves at the behest of a corrupt political organization and its demented leader.

Is this social evolution, I asked (despairingly), in which the Chinese system of government proves its superiority for survival at the colony level, while the US democratic system chokes on its own infected lungs. Worse, is the virus programming us to exacerbate our own weaknesses — yanking our social media chains and our slavery-era political institutions, like the rabies virus, which infects the brain and then explodes out through the salivary glands of a zombified attack animal. Colonies of ants rise or fall based on how they respond to parasites, which themselves are evolving to control ant behavior, as they evolve together. How exceptional are humans? Maybe we just do it faster, in social evolutionary time, rather than across many generations of breeding. Fascinating, but kind of dark. lol.

Anyway, naturally my concern is with information systems and scholarly communication. How human success against the virus has come from the rapid generation and dissemination of science and public health information (including preprints and data sharing). And failure came from disinformation and information corruption. Dr. Birx in the role the rabid raccoon, watching herself lose her grip on scientific reality as the authoritarian leader douses the public health information system with bleach and sets it on fire with an ultraviolet ray gun “inside the body.”

So I wrote a short paper titled, “Host, parasite, and failure at the colony level: COVID-19 and the US information ecosystem,” and posted it on SocArXiv: socarxiv.org/4hgam.* It includes this table:

hpit2


* I barely took high school biology. In college I took “Climate and Man,” and “Biology of Human Affairs.” That’s pretty much it for my life sciences training, so don’t take my word for it. Comments welcome.

The COVID-19 epidemic in rural U.S. counties

I’ve been working on the COVID-19 epidemic in rural U.S. counties, and have now posted a paper on SocArXiv, here: https://osf.io/preprints/socarxiv/pnqrd/. Here’s the abstract, then some figures below:

Having first reached epidemic proportions in coastal metropolitan areas, COVID-19 has spread around the country. Reported case rates vary across counties from zero to 125 per thousand population (around a state prison in the rural county of Trousdale, Tennessee). Overall, rural counties are underrepresented relative to their share of the population, but a growing proportion of all daily cases and deaths have been reported in rural counties. This analysis uses daily reports for all counties to present the trends and distribution of COVID-19 cases and deaths in rural counties, from late March to May 16, 2020. I describe the relationship between population density and case rates in rural and non-rural counties. Then I focus on noteworthy outbreaks linked to prisons, meat and poultry plants, and nursing homes, many of which are linked to high concentrations of Hispanic, American Indian, and Black populations. The growing epidemic in rural counties is apparently driven by outbreaks concentrated in these institutional settings, which are conducive to transmission. The impact of the epidemic in rural areas may be heightening due to their weaker health infrastructure and more vulnerable populations, especially due to age, socioeconomic status, and health conditions. As a result, the epidemic may contribute to the ongoing decline of health, economic, and social conditions in rural areas.

Here are COVID-19 cases in rural counties across the country. Note that the South, Mid-Atlantic, Michigan, and New England have the most (fewer in West and upper Midwest). When you look at cases per capita, you see the concentration in the South and isolated others.

F1 rural county cases maps

COVID is still underrepresented in rural counties, but their share of the national burden is increasing, as they keep adding more than 2,000 cases and just under 100 deaths per day.

F2 new cases and deaths

Transmission dynamics are different in rural counties. They show a weaker relationship between pop density and cases. This suggests to me that there are more idiosyncratic factors at work (prisons, meat plants, nursing homes), which are high concentrations of vulnerable people.

F3 population density and cases

These are the rural outbreak cases I identified, for which I could find obvious epidemic centers in institutions: Prisons, meatpacking and poultry plants, and nursing homes. These 28 select counties account for 15% of the rural burden.

F4 rural county selected cases

In addition to the institutional concentration, these outbreak cases also show distinct overrepresentation of Hispanic, American Indian, and Black populations. Here are some of the outbreak cases plotted against minority concentrations.

F5 rural county minority scatters

And here’s a table of those selected cases:

crt2

Lots more to be done, obviously. It’s a strong limitation to be restricted to case and death counts at the county level. Someone could go get lists of prisons and meatpacking plants and nursing homes and run them through this, etc. But I wanted to raise this issue substantively. By posting the paper on SocArXiv, without peer review, I’m offering it up for comment and criticism. Also, I’m sharing the code (which links to the data, all public): osf.io/wd2n6/. Messy but usable.

A related thought on writing a paper about COVID19 right now: The lit review is daunting. There are thousands of papers, most on preprint servers. Is this bad? No. I use various tools to decide what’s reliable to learn from. If it’s outside my area, I’m more likely to rely on peer-reviewed journals, or those that are widely citied or reported. But the vast quantity available still helps me see what people are working on, what terms, and types of data they use. I learned a tremendous amount. Much respect to the thousands of researchers who are doing what they can to respond to this global crisis.

COVID-19 graphs, with data and code

Updated March 25.

Although I’m not an expert on pandemic analysis, I am naturally following the COVID-19 data as best I can. And because I always understand data better when I make the figures myself, I’ve been making and looking at COVID-19 trend data, and sharing it as I go.

The figures below are the latest I made as of March 18 25 29, but you can click on the images to link to the current version. The figures, as well as data files and code, are in an Open Science Framework project, here: osf.io/wd2n6/, under CC0 license (free to use for any purpose). The project updates automatically as I go, but these figures won’t (because this is an old fashioned blog).

First, across countries:

country cases and deaths

For this one, to put the diverse US in perspective, in included US states in addition to selected countries. These are deaths.

countries and states since 10 deaths

State cases and deaths, per capita:

state cases and death rates bar

Finally, one with commentary: The first month, in numbers and Trump’s winning words:

Microsoft PowerPoint - first month of winning coronavirus.pptx