AI wastewater sampling predicts Covid hospitalisations

Despite the epidemiological nature of the work, the study was led by two engineers from the University of Technology Sydney (UTS) - Professor Qilin Wang and Dr Xuan Li. Working alongside colleagues from UNSW Sydney, Delft University of Technology and Morgan State University, Wang and Li examined wastewater data from 159 counties in the US, covering nearly 100 million Americans. They then cross referenced this data with US hospital admission records to develop their prediction model. The work is published in Nature Communications.

“My PhD focused on sewer design to reduce concrete corrosion, however I graduated right around the time of COVID-19 and saw an opportunity to monitor and study the pandemic,” said Dr Li.

“Current prediction methods are based on COVID-19 laboratory testing, or self-testing and reporting, however this does not pick up asymptomatic cases, and many countries are moving away from rigorous testing requirements.”

According to Professor Wang, wastewater is a rich source of data that is not yet fully exploited. He believes AI-based modelling can be a cost-effective early warning system, allowing public health officials to better prepare for and manage pandemic waves, and efficiently allocate limited healthcare resources.