The President’s Special Awards for Pandemic Service from the Royal Academy of Engineering awarded Sewers4COVID as one of the exceptional examples of engineering in the service of society in the context of pandemic challenges.
The Sewers4COVID team from the University of Exeter and led by Professor Dragan Savic FREng applied machine learning to sewer epidemiology to estimate the number of infected people in a certain geographical area to track the spread of infection.
The Centre for Water Systems team contributing to Sewers4COVID, led by Professor Dragan Savic FREng, includes Dr. Lydia Vamvakeridou-Lyroudia, Professor Albert Chen, Dr. Mehdi Khoury and Gareth Lewis. They use wastewater-based epidemiology, which involves analysing sewage samples on a daily basis.
By applying machine learning techniques to the sewer surveillance data, Sewers4COVID can detect hotspots of potential outbreaks in real-time. The method also takes into consideration socioeconomic conditions, to identify vulnerable groups that are at high risk. The result is an estimate of the number of infected people in the area covered by the sewer system.
This original approach was conceived in 48 hours when the Exeter engineers competed with more than 2,000 other teams in the #EUvsVirus hackathon organised by the European Commission in April. Collaborating online, the team developed the methodology and a powerful prototype of a digital pandemic observatory in the Netherlands.
Durk Krol, Executive Director of Water Europe, says: “Sewers4COVID offers an alternative approach, which is independent of costly individual viral tests, for large scale-surveillance that provides scientifically-proven evidence to support public health management. It will quickly reflect the phase of virus concentration in communities, enabling governments to manage restrictions in a timely way with confidence.”
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