Testing wastewater samples for COVID to predict surges in clinical cases gained attention. Since the beginning of the pandemic, scientists have been researching this approach.
A new mathematical model that uses wastewater samples to accurately predict the number of clinical COVID-19 cases in a community five days ahead of time Masaaki Kitajima, an environmental engineer at Hokkaido University, and colleagues in Japan developed and validated the approach.
It could assist healthcare providers in better tailoring infection control policies, particularly when clinical surveillance is lacking. The findings were published in the journal Environment International.
Testing wastewater samples for SARS-CoV-2 to predict surges in clinical cases has gained attention. Since the beginning of the pandemic, scientists have been researching this approach.
However, current methods aren’t particularly sensitive, and they can only detect increasing cases without forecasting their numbers within a community.
Kitajima and his colleagues developed a method to detect SARS-CoV-2 RNA in wastewater samples, but it requires solid material and does not work well with diluted wastewater on rainy days. To address this, they modified their approach by using special filters that can capture the viral RNA from diluted wastewater.
This is followed by extracting RNA from the filter, amplifying it, and then running polymerase chain reaction (PCR) tests to detect it. They call the new method EPISENS-M.
They used EPISENS-M to test weekly samples collected from two wastewater treatment plants in Sapporo, Japan, from May 2020 to June 2022.
They compared the SARS-CoV-2 data from these samples to the clinical surveillance data of COVID-19 cases from the two catchment areas. “When new cases were still less than one in every 100,000 residents, EPISENS-M was able to detect SARS-CoV-2 RNA in wastewater with more than 50% accuracy,” says Kitajima.
Further data analysis revealed that infected people begin shedding the virus into the sewage system around five days before clinical testing.
The researchers used the wastewater virus data in conjunction with clinical surveillance data to create a mathematical model that takes into account fluctuations in viral shedding throughout the course of disease to successfully predict the number of new cases expected over the next five days.
“We still don’t know how vaccinations, for example, affect viral shedding from infected people into the sewage system,” Kitajima says. “Our method also necessitates the purchase of costly equipment. So further research is still needed to improve this method and to make it more cost-effective. Nonetheless, this study shows that wastewater-based epidemiology has the potential to predict the number of clinical cases in a community even when fully notifiable clinical surveillance is not used.”