Categories
COVID-19

Thanks for the shout-out Dr Russell!

It was fun for UNB to get a shout out from New Brunswick’s Chief Medical Officer of Health Dr Jennifer Russell during the February 8 COVID-19 briefing:

This was at around the 6:45 mark. It has truly been a pleasure working with the folks at NB public health on COVID modelling, and I’m very much looking forward to our future collaborations.

Categories
COVID-19

Recapping a busy COVID modelling week

Last week was very busy in terms of the public release of COVID modelling I have been working on for various governments in Canada. First off was a press conference from the Government of Prince Edward Island on Wednesday, January 26:

About 35 minutes in, you can find Dr Heather Morrison (Chief Health Officer) discussing recent forecasts for new cases and hospitalizations under current conditions, and various hypothetical scenarios considering the effects of the January 19 circuit breaker in PEI.

I have also been doing some modelling for the Northwest Territories (NWT). On the government’s website, you can find a webpage called The Use of Mathematical Modelling in the Northwest Territories (NWT). This page contains a concise summary of NWT modelling results as of Wednesday, January 26. We plan to update this webpage regularly as more data becomes available.

Forecast of hospitalizations in the NWT from Wednesday, January 26

Finally, Dr Jennifer Russell (Chief Medical Officer of Health of New Brunswick) presented a modelling slide at a Thursday, January 27 press conference (around 5 minutes in):

This chart compared model projections for hospitalizations during New Brunswick’s level 3 restrictions and actual data.

I am very pleased to acknowledge that all the above modelling was done in cooperation with government scientists from multiple jurisdictions. The underlying modelling framework is the result of a yearslong effort in collaboration with the New Brunswick Department of Health. The work has also been supported by the National Sciences and Engineering Research Council of Canada (NSERC), the Canadian Institutes of Health Research (CIHR), the New Brunswick Health Research Foundation (NBHRF), Mathematics for Public Health (MfPH), the Atlantic Association for Research in the Mathematical Sciences (AARMS), and the Government of New Brunswick.

Categories
COVID-19 Visualizations

How memes go viral

For over a year now, my main research focus has temporarily shifted away from gravitational physics towards the mathematical modelling of COVID-19 (for obvious reasons). Although this might seem like a big change, the two areas actually share a lot in common. One of the main techniques for understanding cosmological dynamics as well as the spread of infectious disease is the theory of dynamical systems, which has played a key role in my research for many years. Similarly, Bayesian statistics are extremely useful for analyzing both astrophysical observations and COVID case counts.

I have also been interested in how various phenomena spread on graphs. Several years ago, I made this video on how memes go viral on the internet:

The idea is that there is a social network online describing the connections between individuals. If one person becomes interested in something (back then, the “ice bucket” challenge was in vogue), they might share it with their contacts, who in turn might share it with other people, and so on.

You might be thinking that this process sounds an awful lot like how an infectious disease spreads. The purpose of the above video is to push this analogy as far as we can by creating a disease model of how a “meme” spreads on the internet.

How about actual infectious diseases? Adapting the techniques in the video to model COVID-19 in New Brunswick was the topic of a problem at the 2021 AARMS Industrial Problem Solving Workshop. I presented this problem in collaboration with The Black Arcs, a local Fredericton company with expertise in detailed computer simulations of daily life in cities and towns. This project is ongoing, and is starting to yield some exciting results.