“Not going back.”

This is a list of things that, once the Covid-19 thing is handled, are probably never going back. In no particular order.

  1. Buying toilet paper or other paper goods in a physical store.
  2. In-person house closings.
  3. Offices full of people who could work for home / viewing working from home as a perk.
  4. Non-essential work travel involving airline flights.
  5. Open-floor office plans.
  6. Schools/universities that are focused on a physical location.
  7. Department stores.
  8. Something you suggest in the comments below or bring to my attention on Twitter.

Organic Design for Command & Control

Col. John Boyd’s “Organic Design for Command & Control” came up in conversation today so I figured I would put my copy up and make it available. As usual I’ll probably add to this over time. Feel free to use the comments below to ask questions, I’m going back to old school comments-enabled writing.

Audio starts a couple slides in. I found the complete audio but haven’t yet matched it to the picture. Enjoy.

Syndromic Surveillance & Covid-19 Caveats

This article is part of the Syndromic Surveillance and Covid-19 collection on Thoughtfaucet.

There are caveats to all data projects. I do not believe any of these undermine the work and thinking. But they are important to note (and I hope you mention other caveats as well–it improves the project) and discuss if necessary. But all the same, remember that John Snow removed the pump handle for a reason.

Syndromic Surveillance: Tracking Covid-19 via Respiratory ER Visits

Growth of reported cases of Respiratory issues in the ERs of NYC, most likely related to Covid-19

The above graph uses the NYC Health EpiQuery data (Respiratory case counts) as of 11:50pm EST March 27, 2020 and the NYC Health & Mental Hygiene’s 2019 Novel Coronavirus (Covid-19) Daily Data Summary (tested positive Covid-19 and Covid-19 deaths) as of 4:00pm March 27, 2020. This page is updated regularly with new data as it becomes available.

Syndromic Surveillance & Covid-19 Resources

This article is part of the Syndromic Surveillance and Covid-19 collection on Thoughtfaucet.

In the process of developing the Syndromic Surveillance Covid-19 NYC graph I gathered and read a variety of resources. Some of these are for specialist audiences and others are for more general audiences. This page is an annotated bibliography of the medical journal articles, Twitter threads, and news reports related to the project.

Loadband, an information design pattern for showing intensity of real world factors

This article is part of the Syndromic Surveillance and Covid-19 collection on Thoughtfaucet.

While working on my “Estimating Future ER Load” information design I wanted a way to show how many cases of influenza-like illness an emergency department was capable of handling over a given time.

Coronavirus: Estimating future ER load in NYC

A chart comparing additional cases per day for Influenza-like Illness, 18 and older, visiting NYC emergency departments with tested cases of Covid-19 March 24, 2020
Additional cases per day for Influenza-like Illness, 18 and older, visiting NYC emergency departments. March 24, 2020

The above graph uses the NYC Health EpiQuery data (ILI case counts) as of 1:47pm EST March 24, 2020 and the NYC Health & Mental Hygiene’s 2019 Novel Coronavirus (Covid-19) Daily Data Summary (tested positive Covid-19 and Covid-19 deaths) as of 9:45am March 24, 2020.

Seeing Covid-19 in Influenza-like Illness, NYC 2020

Percent Influenza-like Illness Emergency Department visits, 18 and older, for NYC 2020 vs 2019

This article is part of the Syndromic Surveillance and Covid-19 collection on Thoughtfaucet.

This document outlines the thinking behind my initial chart, trying to see if there was anything to the idea that Influenza-like Illness could be used to spot Covid-19 in NYC.

A thread on Twitter by Farzad Mostashari mentioned EpiQuery, a data collection maintained by NYC Health. The data consists of emergency department visits broken down by date, coarse age groups, borough of New York City, and what sort of symptoms the person was reporting.