SYDNEY, Aug. 7, 2023 /PRNewswire/ -- New research shows it is possible to accurately predict hospital admission numbers due to COVID-19 up to four weeks in advance with an Artificial Intelligence (AI) based system using COVID wastewater sampling.
The research was led by Professor Qilin Wang and Dr Xuan Li from the University of Technology Sydney's Faculty of Engineering and IT (pictured) together with researchers from UNSW Sydney, Delft University of Technology and Morgan State University. Image: Andy Roberts.
The study, published in the journal Nature Communications, used wastewater data from 159 counties in the US, covering nearly 100 million Americans, along with US hospital admission records, to develop the prediction model.
The research was led by Professor Qilin Wang and Dr Xuan Li from the University of Technology Sydney together with researchers from UNSW Sydney, Delft University of Technology and Morgan State University.
Dr Li was recently awarded a two-year grant from the Australian Academy of Science WH Gladstones Population and Environment Fund to develop an Australian-based wastewater prediction model.
"COVID-19 still poses a heavy burden on healthcare systems around the world. The number of Australians in hospital with COVID-19 peaked at around 5500. Rapid increases in patient numbers can stress frontline healthcare capacity and increase fatality rates," Dr Li said.
"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," she said.
Professor Wang said the research shows wastewater surveillance combined with 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.
"Wastewater monitoring is already conducted in many countries, but it is limited to showing whether COVID-19 is present in a region, as well as a rough estimation of whether the burden is increasing or decreasing.
"We used artificial intelligence to pick up patterns and changes in the data and learn from this to increase the accuracy of predictions.
"Variables that can influence hospital admissions include changing behaviour due to public policies, vaccination rates, holidays and weather. The established model can help accurately predict the hospitalisation needs due to COVID-19 in the region," he said.
Dr Li hopes to extend her research to include other infectious diseases that can be detected through wastewater-based epidemiology, including food-borne pathogens, such as salmonella and E-coli, and viruses such as flu, norovirus and hepatitis A.
"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.
"I'm grateful for the opportunity to receive the WH Gladstones award, particularly as an early career researcher, to explore the potential to create early-warning systems for COVID-19 and other diseases. I hope this work can benefit the community and inspire other women in science."
The paper: Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties was authored with Xuan Li, Huan Liu, Li Gao, Samendra Sherchan, Ting Zhou, Stuart Khan, Mark van Loosdrecht and Qilin Wang.
The study was supported by the Australian Research Council and the Australian Academy of Science.
The University of Technology Sydney (UTS) is among the top 100 universities in the world, known for driving positive impact through a range of global research partnerships. UTS ranks #1 in research impact in Australia.
