COVID19 cases per hospital bed

May 1st graph updates wrt. the coronavirus pandemonium. Here I’m plotting bubble sizes proportional to the number of SARS-CoV-2 cases per total hospital beds in a county, and coloring by log cases per county.

COVID19 cases per hospital bed

Here is basically the same graph, after switching the color and size axes: bubble sizes are now proportional to the sqrt(cases) in a county, and color represents the number of cases per bed.

Let’s check in with NYC and see how it’s fairing compared to the rest of the US…

Lastly I’ve generated a log-log plot of cumulative vs. daily cases and deaths. Ultimately we want the daily cases and deaths to drop significantly, so when things start going very well, there should be a dramatic drop along the y-dim (particularly for the deaths graph, since daily cases might indicate a ramping-up of testing, which I’d consider a good thing). As of May 1st, things look promising, but this battle is certainly not over. At least, however, it looks like the physical distancing tourniquet has greatly attenuated the exponential growth.

2019-nCOV Data Visualizations

FeaturedEuropean Coronavirus

Here I will be adding data visualizations (both found and original) depicting facets of the 2019/2020 coronavirus pandemic.

Let’s start with one found on https://coronavirus.1point3acres.com showing how the cumulative number of confirmed COVID19 cases in each state has evolved over time.

This shows pretty clearly the situation in New York is dire, but I wouldn’t be surprised if California starts to close the gap.

Here is a similar graph, for each country

Here is a data viz I made using ERIS ArcGIS…

The best covid19 dashboard I’ve found belongs to the group at Johns Hopkins University…

One way we might be able to predict which U.S. counties will be disproportionately affected by the coronavirus pandemic is whether there are enough hospitals (specifically hospital beds) in those regions to accomodate the number of people expected to become infected with COVID-19.

Using data compiled from the CDC, Johns Hopkins University, and the NYT databases…

… I’ve generated a map of the location of U.S. hospitals. Each dot is a hospital. The size of the dot is proportional to the number of beds each hospital contains. Each dot is colored according to how many beds there are per 1000 people in the county. Such a map might help reveal areas with a low number of hospital beds per service population.

(full MATLAB code tutorial here)

Here is another plot similar to the one above. In this one…

  • Dot size == total number of hospital beds in a given county
  • Dot color == log(Cases) in that county

This next graph shows U.S. COVID-19 related hospitalizations over time, broken down by age group. The y-axis represents the number of infections per 100,000 people.

Plotting the location of every U.S. hospital using MATLAB Mapping Toolbox

FeaturedU.S. Hospital Locations

Given the current COVID-19 pandemic, one major question is will we have enough medical resources to handle those who need treatment. In order to make predictions about the nCOV sequelae, one key piece of information is the number of U.S. hospitals, and where they are located.

I’ve put a copy of this dataset here: https://bit.ly/UShospitals

After importing the data the MATLAB, we simply need to pass the latitude and longitude data into the mapping toolbox. We will use a method that draw circles of a given size, centered at each lat/lon provided.

Hospitals in the U.S.

This looks pretty good from this level of zoom, but you will find out upon zooming in that map overlays that draw polygons that (for good reason) don’t resize when you zoom in and out…

You can however plot locations using pins, which will resize based on the current zoom level. Here is an example of that code:

Pins indicate the location of U.S. hospitals