Should I go to brunch? An interactive tool for COVID-19 curve flattening

A tool to help you understand how your actions affect the spread of coronavirus in your community

with Salman Mohammed, Claudel Rheault, and Patrick Steeves

Update 19/03/20: thanks to lots of fantastic feedback, we’ve now released V2 of the tool, which brings historical case data per day for your country, better graphs, and a breakdown of forecast mortality by age! It’s still at https://corona-calculator.herokuapp.com ✌️

Many people have been persuaded by recent developments that we should take coronavirus very seriously indeed. Posts such as this one underline how important isolation has been as a tactic to control the spread of Coronavirus. You’ve probably seen scary curves of number of infected individuals:

Supplemented by the idea of “flattening the curve” to limit the load upon healthcare systems:

What impact can I have on these curves?

People (including us) find it hard to reason about the likely progression of the disease because there are lots of unfamiliar concepts (like exponential growth) and potentially important variables (like how quickly people get diagnosed and how long the disease remains transmissible).

All of this make it difficult for non-expert individuals to reason about how they should change their behaviour, and what kind of difference that is likely to make to the things we care about: deaths and disruption.

So we built a tool to help us understand how the disease might progress depending on the number of social contacts an infected person will have. It’s called the Corona Calculator, and it aims to help you understand how limiting your social interactions can affect the spread of Coronavirus and the load upon healthcare systems where you live.

Find it at https://corona-calculator.herokuapp.com

Contribute to the code at https://github.com/archydeberker/corona-calculator

Read about our methodology and our data sources at our public Notion Page.

Found an error or like to request a feature? Tell us via Google Form.

Why build this, now?

On Friday March 13th, our office was shut and we commenced remote work. At that point, there were 24 infected people in Canada, with 850 under investigation. As we were starting to discuss our new remote life rituals and organization, it became clear that not everyone had the same understanding of “isolation”. Was it ok to regroup in small teams in coffee shops? Should we still go to that party on Saturday? What about our team bowling game?

We started to look at the numbers and work out what we could build. There were already some amazing apps available to track the progression of the disease, from Johns Hopkin, and enough background on the dynamics of spread to get us fairly worried. Discussing the UK’s “herd immunity” strategy with our family and friends underlined that people were using very different mental models when they were considering a reasonable public health response.

Our expertise is in artificial intelligence and creating complex systems which are intuitive to use. We’re not public health professionals, although some of us have previously studied the mathematical modelling of infectious disease. We set out to create a tool to help individuals understand the likely impact of their choices.

How to use the tool

How many cases are there in your region?

Start by setting your location. Every hour we check the numbers from Johns Hopkins and use these to provide you with the current number of confirmed cases in your country:

One of the tricky things about containing the Coronavirus is that we know that we don’t really know how many cases there are at any one time. We use numbers from a Japanese paper to estimate that the true number of cases at any one time is about 10 x the number of confirmed cases. However, this is highly dependent upon testing strategy; there’s massive variation in the pervasiveness of testing worldwide.

How does my behaviour affect the spread?

The major thing you can control as an individual is how much you choose to self-isolate. This is reflected in the app by the number of people that an infected person interacts with each day.

This is a very important number, because it determines how quickly the disease spreads: what’s known as the basic reproduction rate in epidemiology. Remember, Coronavirus doesn’t show symptoms for several days, so it’s hard to know whether you might already be infected.

In the words of Prof Graham Medley, our best option is to “imagine you do have the virus.. and change your behaviour so that you’re not transmitting”.

TK: https://twitter.com/BeardedGenius/status/1238475687830355970

You’ll see that changing this has a dramatic effect on the peak number of infected people and the deaths Coronavirus cause:

All of our graphs are interactive (thanks, Plotly), so you can zoom in to see what’s going on:

You can find details of how we’re modelling the spread of disease and how we calculate death rate in our Notion page.

Why does the rate of infection matter?

Some percentage (we calculate it to be about 15%, based on this paper) of people require hospitalization after catching coronavirus, with symptoms ranging from respiratory distress to organ failure.

This means that a lot of people are going to need hospital care, and that’s going to be an issue. How much of an issue depends upon how many people are ill at any one time:

Again, you can play with the slider on the left and zoom in to understand how the number of people an infected person interacts with is a crucial determinant of load upon the healthcare system.

This, for us, is the scariest part of the Coronavirus pandemic: the potentially huge stress it will put upon our healthcare systems, with the corresponding increase in mortality that could result — along with the harrowing choices it entails for healthcare professionals.

Note that we don’t have good data on how the mortality rate will change as healthcare systems become overwhelmed, and so we can’t incorporate that into our models today. It seems safe to say the mortality rates estimated from (eventually) well-controlled outbreaks in Wuhan underestimate the mortality rate in a healthcare system on the brink of collapse.

Can you be sure this is right?

No!

There are several ways in which we could be wrong:

The model we’re using could be too simplistic.

However, it’s been around for a long time and is quite well respected so we think this is unlikely to be a big source of error.

2. The numbers we’re using might be inaccurate.

There is a lot of uncertainty right now about crucial numbers about the coronavirus and its dynamics. We’ve relied heavily upon numbers from Johns Hopkins and the MIDAS Network but there is just a lot we don’t know right now, and numbers from different countries can be quite disparate! This is potentially a big source of error.

3. We might have screwed something up

We’re technologists, not epidemiologists, and we made this in a rush (everything about coronavirus happens fast…). We would love some feedback on the code we’ve written over the weekend!