This calculator lets you tweak things like R0, incubation time, and hospitalization rate to see how affect the results. From the author:

At the time of writing, the coronavirus disease of 2019 remains a global health crisis of grave and uncertain magnitude. To the non-expert (such as myself), contextualizing the numbers, forecasts and epidemiological parameters described in the media and literature can be challenging. I created this calculator as an attempt to address this gap in understanding.

Graphics reporter Harry Stevens from The Washington Post helps us see the impact of “social distancing” with this coronavirus simulator. He shows the effects of four simulations — a free-for-all, an attempted quarantine, moderate social distancing, and extensive social distancing.

Harry goes on to say, “moderate social distancing will usually outperform the attempted quarantine, and extensive social distancing usually works best of all.”

To simulate more social distancing, instead of allowing a quarter of the population to move, we will see what happens when we let just one of every eight people move.

This interactive dashboard was created by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University to visualize and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The data collected and displayed are freely available on GitHub.

Below are a few notable pull-quotes from this correspondence on The Lancet’s Infectious Diseases journal.

The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds.

The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable…

Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks.

For more updates and resources follow Lauren Gardner on Twitter or read the readme.

Diagrams lets you draw the cloud system architecture in Python code. It was born for prototyping a new system architecture design without any design tools. You can also describe or visualize the existing system architecture as well. Diagrams currently supports four major providers: AWS, Azure, GCP and Kubernetes.

I’ve never found a diagramming tool I’ve enjoyed using. The idea of just writing some code and letting a tool do the drawing might be just what the doctor ordered. Start with the quick start.

The State of JS 2019 survey left many in awe of the beautifully rendered line graph created by Amelia Wattenberger. So we’ve brought her on JS Party to discuss how she built it!

We’ll chat about all things D3, a JavaScript library for creating data visualizations, and even learn a bit about the CSS cascade.

This isn’t the most practical item in our feed this week, but it’s definitely neat and the how is it made section of the README (and related code) shows how you might accomplish something similar.

In this episode we’re shining our maintainer spotlight on Ovilia. Hailing from Shanghai, China, Ovilia is an up-and-coming developer who contributes to Apache ECharts, maintains Polyvia, which does very cool low-poly image and video processing, and has a sweet personal website, too.

This episode with Ovilia continues our maintainer spotlight series where we dig deep into the life of an open source software maintainer. We’re producing this series in partnership with Tidelift. Huge thanks to Tidelift for making this series possible.

Move over, Hansel. Hand-drawn is so hot right now.

Collin Dellow:

If you love Graphviz but hate its 1990s aesthetics, you might find this tool handy. It’s great for documenting workflows and designs in a way that prompts people to consider it a work-in-progress that’s open for collaboration and discussion.

Built with RoughJS and (or at least inspired by) viz.js.

Excellent work coming from Microsoft’s VIDA research group

This new version of SandDance has been rebuilt from scratch with the goal of being modular, extensible, and embeddable into your custom applications.

I do love the hand-drawn style for charts like these. It almost feels like you worked harder on them for some reason.

There are some fascinating results in this study put out by HackerRank where they surveyed 10,351 student developers. One example that shows a growing trend in developer ed:

University students today seem to be showing less interest in Stack Overflow compared to professional developers. Instead, YouTube is starting to become more favorable as a learning tool for the next generation of developers. We found that 73% of students use YouTube, compared to only 64% of professional developers (where the majority of professional developers were aged 25-34, and the majority of student developers were aged 18-24).

A little less surprising, but still good to know for those breaking in to the scene:

There’s a big opportunity for student developers to learn JavaScript and JavaScript-focused frameworks. Employers need it more than any other skill. As the direction for web applications have moved from static to dynamic, JavaScript has become increasingly dominant in the industry. In fact, 95% of web applications are built on JavaScript—so it’s hard to ignore the disconnect.

This is a really well done report. 👌

Love it! Go here for more examples.

Within this interactive map, vocal bursts are plotted along the 24 dimensions of emotion they can reliably convey. Each letter corresponds to a vocal burst. Fascinating little web demo!

Renders a time histogram with a preview of the whole data below it, that can be used both to pan and zoom in on specific intervals as well as to see an overview of the distribution of the data (demo).

Feature-rich and easy on the eyes. Read the intro post on Medium for the backstory and motivation behind this.

A diagramming model which uses a set of typing characters to approximate the intended shape.

The JS community has some great open-source projects that make data visualization easier, however, there has not been a go-to solution for building real-time backends that can back these charts and make them real-time. With GraphQL, we can get a real-time backend running within seconds and use it to power real-time charts.

As of March 14, 2019, Google Image Charts API is no longer in service. That’s a shame, because it was a convenient way to easily generate a chart image from a URL. The good news is an open source replacement is ready for service!

jumping from SQL Server to PostgreSQL is much more than changing a tool. PostgreSQL was built on Unix, with Unix in mind as the platform of choice, and typically runs best when it’s sitting on some type of Unix box. The Unix world has a pretty specific idiom for how to go about things and it certainly isn’t visual!

Rob Conery with a deep dive into psql and what makes it awesome.

The repositories of distributions such as Debian and Nixpkgs are among the largest collections of open source (and some unfree) software. They are complex systems that connect and organize many interdependent packages.

Is it possible to capture the large scale features of such a repository in an image? Are there common design choices of the contributors? Did they lead to any emergent structure?

This work resulted in some beautiful (and interesting) visualizations. Here’s a sneak peak 👇

React Quantum parses through your React application to create a color-coded tree model of its component hierarchy. On hover, each tree node will display two key component performance metrics—render time and re-render frequency—as well as memoized state and props to indicate what, specifically, initiated the render.

Generates a slick looking bar chart of command usage from your .bash_history to learn and share where you’ve been spending your key-presses on the command line.

Venn diagrams are great for explaining set operations such as UNION, INTERSECT, and EXCEPT, but they aren’t so great at explaining JOINs:

A JOIN is really a cartesian product (also cross product) with a filter.

Turns out there’s a better way: JOIN diagrams, which I’ve never even heard of. Click through to read all about ’em. 🤓

This “Data Color Picker” looks like a spectacular tool for any developer out there (like myself) who appreciates the value of a good color palette, but lacks the ability to put one together. You’re not alone!

(This tool is for generating equidistant palettes for data visualizations, but it can most certainly be used generically.)

Creating visually equidistant palettes is basically impossible to do by hand, yet hugely important for data visualizations. Why? When colors are not visually equidistant, it’s harder to (a) tell them apart in the chart, and (b) compare the chart to the key. I’m sure we’ve all looked at charts where you can hardly use the key since the data colors are so similar.

You pick the “endpoint” colors and it generates all of the colors in-between. Very cool.

Cool idea: take Google’s autocomplete suggestions, repeat the same query on them, take those suggestions, repeat the same query on them, and then draw a line between each suggestion. This could be useful for market research, to find alternative solutions, or simply for curiosity’s sake.