This website uses cookies primarily for visitor analytics. Certain pages will ask you to fill in contact details to receive additional information. On these pages you have the option of having the site log your details for future visits. Indicating you want the site to remember your details will place a cookie on your device. To view our full cookie policy, please click here. You can also view it at any time by going to our Contact Us page.

Wastewater data ensures Covid hospital resources don’t go to waste

30 November 2023

Wastewater surveillance means that new Covid-19 admissions can be predicted much more accurately than clinical data, a new study demonstrates.

Image: Syracuse University
Image: Syracuse University

The COVID-19 pandemic has been a burden on the global healthcare system since its arrival in early 2020. COVID remains a threat to our communities, particularly during the winter months when new cases and hospitalisations are likely to surge.
The ability to predict where and when new patients will be admitted to hospitals is essential for planning and resource allocation.

“Our findings indicate that wastewater surveillance improves prediction models for hospitalisations by 11 percent over models that use case data at the county level and by 15 percent for regional hospitalisation estimates,” says Dustin Hill, an environmental Data Scientist and Epidemiologist who works in the public health department in the Falk College of Sport and Human Dynamics at Syracuse University.

“When looking at how many beds a hospital has available, those percentages can make a big difference in whether that hospital is going to have space for new patients or not, and this data can help them get ready for changes.”

Hill led a project that used wastewater surveillance data in predictive models to improve estimates for new COVID hospital admissions in New York state.

Throughout the pandemic, hospitalisation forecasting models have relied heavily on clinical data collected from polymerase chain reaction (PCR) and antigen tests. 

However, this data can be biased because of a lack of widespread testing and may not be quick enough to indicate a surge.

For the new study, published in the journal Infectious Disease Modeling, the researchers combined wastewater surveillance data (how much SARS-CoV-2 is found in wastewater) with clinical case and comorbidity data to predict the seven-day average of new hospital admissions 10 days after the wastewater sample collection.

Wastewater data are being collected across New York State through the New York State Wastewater Surveillance Network, and that data can be used to continuously update forecasting predictions each week.

According to the research, the average difference between predicted hospitalisations and observed hospitalisations was 0.013 per 100,000 population, or 1.3 in 10,000,000 population, providing high accuracy.

The New York State Wastewater Surveillance Network is testing for COVID in at least one wastewater treatment plant in each of the state’s 62 counties, covering a population of more than 15.3 million. The New York State Wastewater Surveillance Network dashboard provides the most recent statistics regarding the network.

The research team is exploring how their methods to predict COVID hospitalisations can be further refined and applied to other infectious diseases such as RSV and influenza as wastewater surveillance expands to cover these public health threats.

“Predicting future hospitalisations using wastewater data helps get our public health partners in front of surges before they happen so they are prepared when new patients need to be admitted and can distribute resources accordingly,” Hill says.

“The methods we developed here are going to be instrumental for tracking the diseases we already know about, and perhaps even more important for the diseases that could arise in the future.”

Print this page | E-mail this page

MinitecRegarl Rexnord