This site is intended to be community for discussion on topics primarily around infection prevention, hospital epidemiology, public health, emergency management, and safety. There may be an occasional item that might appeal to a much wider audience that is intended to make our lives easier, drive personal growth, or just to make people laugh.

Comments are encouraged and will not be edited except in cases of spam, offensive content, or flaming. This is to be a safe place for discussion and debate and I reserve the right to block anyone who doesn’t follow these simple rules. I’m looking forward to our discussions.


COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University

GISAID data provided on this website are subject to GISAID Terms and Conditions

Healthdata.gov: COVID-19 Reported Patient Impact and Hospital Capacity by Facility

Hasell, J., Mathieu, E., Beltekian, D. et al. A cross-country database of COVID-19 testing. Sci Data 7, 345 (2020).

Hodcroft, Emma B. 2021. “CoVariants: SARS-CoV-2 Mutations and Variants of Interest.”

Mathieu, E., Ritchie, H., Ortiz-Ospina, E. et al. A global database of COVID-19 vaccinations. Nat Hum Behav (2021).

Edouard Mathieu, Saloni Dattani, Hannah Ritchie and Max Roser (2022) – “Monkeypox”. Published online at OurWorldInData.org.

National Institute for Communicable Diseases/DATCOV, South Africa

Our World in Data

The Delphi Group at Carnegie Mellon University U.S. COVID-19 Trends and Impact Survey, in partnership with Facebook


Cases and 21-day Slope

The light blue in these is a basic epidemic curve for the given area. It represents the number of cases on a given day on the left y-axis. The x-axis is simply the date. The dark blue line following the curve is simply the seven day running average, which is how much of the media shows the data.

The red line in this particular graph is the first derivative of the cases. It finds the average slope (the total number of cases/the number of days) over the past three weeks and is measured on the right y-axis. There is also a dotted black line representing zero for this particular part of the graph. When the red line is at zero, cases are remaining flat. The distance the the red line gets above or below zero represents the rate at which cases are increasing or decreasing.

A good analogy to understand this is that if the red line is very high, it’s similar to someone flooring the accelerator of a car. The opposite is true if the red line is a long distance below zero. It’s equivalent to someone stomping on the brake. If the red line were just a little above or below zero, they are either accelerating or braking slowly.

On the right side you will see the red line extending out past the epidemic curve below it. One of the benefits of using a first derivative is that it can be used to project a little bit forward where the pandemic is headed in a particular area. I’ve found that it’s reasonably accurate about 10 days out, but obviously the margin of error gets wider the further that data is projected out from the present.

PHOTO: Colorized transmission electron micrograph of Avian influenza A H5N1 viruses (seen in gold) grown in MDCK cells (seen in green).
Photo Courtesy CDC-PHIL


16 responses to “About

  1. Fred Bucheit

    I have an idea that I would like you to comment on. Perhaps it is not realistic, but here goes. Would there be any benefit for a person with a severe lung infection with the covid19 virus to inhale a very light mist of ethanol alcohol? Alcohol is deadly to the virus from what I read and alcohol in small quantities is not that harmful to humans.

  2. Patty Whitaker

    I’m wondering what your background is? It would be most helpful if you would include that in your “about” description?

    If I missed it on your page can you please tell me where I can find it.

    Thank you.

  3. I do similar for our service area in Pennsylvania. The trend is definitely not looking good. https://public.tableau.com/app/profile/saems#!/

  4. Hi there – sorry for asking this here but I couldn’t leave a comment on the page linked below. I am SO glad to see this work; almost everything I see does not either represent the correct use of data,and uses graphs etc that are difficult to interpret e.g. number of covid cases only that does not consider the size of the vaccination vs nonvaccination cohorts which anti-vaxxers have used to say there is little difference. My question is regarding figure 1 on the linked page; there is a huge 80% democrat spike compared to others at the start of the pandemic. I’m trying to put my head around it but would love to hear your thoughts on it. Cheers and stay safe! Carly https://icemsg.wordpress.com/politics/the-republican-democrat-covid-divide/

    • That’s fine, thanks for the comment. I didn’t realize that pages didn’t allow comments by default, unlike the blog side of this. It’s probably good considering how much traffic it’s driven on Twitter.

      That is exactly the pattern I would expect for a few reasons. First, cities tend to lean much more democrat politically. In addition, international flights would be coming into cities, in this case, NYC, which is what influenced that. Another part is that if it were possible to rewind knowledge back to that time, we knew very little about the virus and didn’t really have much in the way of direction around the use of masks and other nonpharmaceutical interventions. Finally, population density plays a big role in transmission, especially somewhere like NYC where people are often crowded together in buses, the subway, etc.

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