Warren, Youngstown part of state system to monitor wastewater for potential COVID-19 surge

Coronavirus

During his Thursday briefing, DeWine said the state will be posting about a new coronavirus wastewater monitoring network

COLUMBUS, Ohio (WCMH) — Ohio Governor Mike DeWine has announced the state will begin monitoring wastewater to help contain the spread of COVID-19.  

During his Thursday briefing, DeWine said the state will be posting about a new coronavirus wastewater monitoring network.  

“The system will give us an earlier warning sign of possible COVID-19 case increases in any given community and allow decision-makers to more quickly plan prevention and response efforts,” said DeWine.  

According to the Ohio Department of Health, research in the U.S. and elsewhere has shown that non-infectious RNA (ribonucleic acid) from the virus that causes COVID-19 (called SARS-CoV-2) can be excreted in the feces of both symptomatic and asymptomatic infected people. It can be detected in wastewater as many as three to seven days before those infections lead to increases in case counts or hospitalizations. 

According to DeWine, each wastewater treatment plant covers specific service areas, which will help provide info on whether a local surge of coronavirus cases may be imminent.   

Wastewater treatment facilities in Warren and Youngstown are part of the program.

Results can be viewed on the Ohio Department of Health website. As of right now, there is no information available for Warren.

In Youngstown, collections were taken beginning August 2 through August 23. The trend shows a dip in gene copies in the wastewater from August 5-10 and then an increase on August 15 and a slight decrease on August 23.

How will the Ohio Wastewater Monitoring Network improve public health?

According to the Ohio Department of Health, the network will:

  • Serve as an early warning of infection in communities or congregate settings.
  • Provide information that can help local communities more quickly intervene with protective measures to slow disease spread.
  • Help communities measure the effectiveness of such interventions (quarantine/face coverings/business limitations).
  • Develop methodologies/predictive models to translate viral loads detected for comparison with other data, such as rates or percentage of infection in communities.
  • Where possible, compare results to previously collected data on prevalence in specific communities to better understand factors affecting disease spread.
  • Determine impacts on disproportionately affected communities or communities where risk of infection is greater.

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