Planning for a pandemic: a global disease modelling platform

The COVID-19 pandemic poses significant difficulties for the UK. Headlines are full of stories of the shortage of critical supplies that whip up a political whirlwind: personal protective equipment, critical drugs for patients in intensive care, ventilators, and sufficient hospital staff. The problem extends beyond the COVID-19 pandemic, with the same issues arising annually with diseases such as seasonal flu. More importantly, should we be faced with a similar pandemic in the future how can we manage it more efficiently.

The government and companies have to plan how to meet peak demand for critical supplies and care providers must ensure that sufficient staff are in place and are properly equipped. However, they fly blind with little guidance, and so cannot produce supplies in advance or plan how to deploy resources.

Over recent years AI and deep learning have become mainstream, delivering new insights and improvements across all parts of society. However, in most cases good quality data is needed to build these models, when there is little data, or it is not accurate, the models created will also be inaccurate and of little use. Intellegens, a spin out from Cambridge University, has developed a new type of machine learning to deal with these type of problems where data is limited and noisy. In these rapidly changing pandemic environments data quality and sparsity is making machine learning model development hard and unstable.

In this project, Intellegens will develop an accurate machine learning model to predict the progression of pandemics and other diseases. Using all available data as it becomes available, which could include:

* Real time patient case metrics including number of infections, recoveries, and deaths
* Population metrics such as age distribution, housing density, and connectedness
* Fraction of the population believed to have been infected but not tested
* Number of unreported deaths outside the hospital system
* Statistics from limited testing facilities
* Profiling of most at risk groups based on age, ethnicity, or other conditions
* Environmental, geographical, and economic indicators

This tool will ultimately allow policy makers and companies to plan ahead for difficult times, ensuring that appropriate supplies and staffing levels are in place for the given rapidly changing environment