Intellegens has received funding from Innovate UK to model COVID-19 data and improve the management of future coronavirus outbreaks and other infectious diseases. The tool, which will be based on Intellegens' unique deep-learning algorithm Alchemite™, will help understand how policy changes might impact outcomes, helping inform decision-making.
Intellegens, the artificial intelligence (AI) company with a unique deep-learning algorithm, has received funding from Innovate UK to model COVID-19 data from around the world and improve the management of future coronavirus outbreaks and other global pandemics.
Using data collected by different countries throughout the current crisis, Intellegens will build an interactive, predictive tool that will make it easier for governments, healthcare providers, and companies to predict the progression of pandemics and forward plan accordingly.
The tool, based on Intellegens’ Alchemite™ Engine and Analytics products, will feature a dashboard that will accurately summarize predictions for the geographic distribution, timing, and patient characteristics of future cases. Crucially, users will also be able to change parameters to understand how policy choices might impact on outcomes, helping to inform decision-making.
Once complete, Intellegens will work with The Richard Nixon Foundation to roll the model out as a public policy tool. In the first instance, the tool will be targeted at health and social care providers, who need to ensure adequate resources and supplies are in place to meet the needs of the sick and vulnerable.
Dr Gareth Conduit, CTO at Intellegens, said:
The emergence of COVID-19 has exposed a weakness in the global strategy for managing pandemics, which can significantly impact on human health, daily life, and world economics. Throughout the pandemic, different countries have adopted different approaches, with varying degrees of success. Globally, the challenges faced have been distinct with many countries facing a shortage of critical supplies including personal protective equipment, drugs, and ventilators. The key to managing future pandemics lies in modeling COVID-19 data from multiple sources, to compare and contrast the experiences of different countries at a granular level, and learn from best practice. This means bringing together information, in different formats, which may be inconsistent and, in some cases, incomplete.
Standard data modeling tools struggle with data that is sparse or noisy, but the Intellegens’ Alchemite™ technology is different. Working with the information available, Alchemite™ accurately predicts missing values and extrapolates correlations between all parameters - significantly reducing the amount of time and money spent on scrutinizing statistics. It is a proven approach that enables discoveries to be made ten times quicker. We are confident that Alchemite™ will prove useful in the ongoing battle against COVID-19. With Innovate UK funding in place, we will create a tool that will enable governments, policy makers, care providers, and companies to better meet peak demand, whether planning the availability of critical supplies or deploying staff.
To train the Alchemite™ engine, Intellegens will use information from online repositories and organizations such as the NHS, The European Centre for Disease Prevention, and the World Health Organization. Data will include:
- Real time patient case metrics including numbers of infections, recoveries, and deaths
- Population metrics such as age distribution, housing density, and connectedness
- Fraction of 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
Alchemite™ has already proved helpful in the COVID-19 crisis - helping scientists, researchers and engineers conduct virtual, data-driven experiments, which would otherwise have been delayed with the closure of universities, laboratories, and manufacturing plants.