What did we do?
Case study.
We were approached by the School of Mathematical and Physical Sciences at the University of Sussex to help them produce a tool for regional healthcare planners to forecast the local impact of Covid-19.
Covid-19 has changed everyone's lives on a global scale so we jumped at the chance to help in any way we could.
Forecasting the local impact of Covid-19
The fantastic team at the University of Sussex had an innovative way to model Covid-19 on a local scale. Most of the existing Covid tools focussed on a national scale ignoring regional differences in population demographics that can significantly impact the way the pandemic progresses within a region.
The University of Sussex envisaged a tool that took into account these local differences and could help regional planners develop scenario-based policies to help manage the pandemic.
Sussex came to us with a basic prototype completed but wanted to build it into a professional quality tool.
Interactive graphs and simulations
In addition to a traditional marketing site explaining how the tool works and selling its features, we built the main app using Nuxt.JS allowing us to create interactive graphs that will enable planners to see how the pandemic is predicted to progress.
Users can adjust key pandemic parameters such as R rate and hospital capacity to help them predict when they will need to make interventions such as local lockdowns to ease demand on hospitals and reduce transmission.
We also helped the Sussex mathematicians take their mathematical models written in Python and place them behind a secure API that the front end can use to run the simulations and retrieve the graphs' data.
Keeping pace with the pandemic
When we started the project, no one could have predicted how quickly the pandemic would progress, so building an app that kept up with the ever-changing situation was undoubtedly a challenge.
Luckily we worked with an incredible team including a UX researcher (Ben Sauer), UX designer (Emma Smith), and UI Designer (James Gilliard) who all did an excellent job of taking the tricky maths behind the models and making them into a useful and understandable tool.