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The research project, in its experimental phase, started in the spring of 2020 and has just obtained funds to expand the research on a larger scale.

A Harrisburg University of Science and Technology Wastewater-based epidemiology (WBE) research project could provide an efficient and rapid way to predict the possible spread of the SARS-CoV-2 virus and the distribution of people infected with the disease by COVID-19.

A research team led by Professor Kevin Purcell, Ph.D., Program Lead for Data Analytics and Associate Professor of Data Science, carried out the initial project. The success of the project’s initial stage secured additional funding needed to cover a greater geographic area to consolidate this research, and will be of great help so that public health officials can predict areas of increasing prevalence of COVID-19 and take the necessary precautionary measures.

According to Professor Purcell, the study area has been extended to the entire city of Harrisburg, the capital of Pennsylvania.

“The sewer basin we are working with handles 8 million gallons of water a day and serves a population of 66,000 residents,” Purcell said. “So far, we have been doing a simple follow-up using epidemiological methods of wastewater and we have had great success.”

Purcell also refers to the fact that the counts of genetically detected cases are closely following the COVID-19 cases reported by the Department of Health, maintaining a three-week moving average.

“In fact, we are able to detect spikes in case counts, usually two to three weeks before cases spike in the Health Department data,” he said. “This is really promising for such a simple pilot project.”

Other valuable details pertain to the presence of multiple variants of COVID-19 within the population. This helps relate the dynamics of case counts tocurrent events within the sewer system’s footprint, which provides promising indications for public health management.

Study methodology

The Harrisburg University research team uses genetic testing to detect the presence and concentration of SARS-CoV-2 RNA in wastewater effluent samples. As explained by Purcell, pathogens enter the sewage system when infected individuals eliminate them through bodily excretions including saliva, urine, and feces.

In this way, “by collecting samples from various points upstream of a wastewater treatment facility and then using standard genetic tests to determine the presence or absence and amount of viral pathogen in the sample, a modeling algorithm can be developed customized for a specific region or spatial extension, allowing the analysis of the obtained values,” Purcell said.

By combining spatial and temporal attributes in the model, a clear picture of the dynamics of COVID-19 can be developed, determining a valuable map that identifies the status of a viral outbreak.

Understanding these dynamics can help in efforts to predict where COVID-19 outbreaks will occur and, in this way, take the necessary precautions to minimize their spread and therefore their possible effects, providing medical assistance and the adoption of quarantine measures, helping to minimize the spread of pathogens and their consequent threat to public health.

A great advantage of the study is that it helps identify asymptomatic, pre-symptomatic or undiagnosed individuals in the community.

“In addition, once these research systems are established, the results obtained are not limited to the identification of SARS-CoV-2, they can also be used for other pathogens and chemical signals such as opioids,” Purcell said. “The research creates a powerful framework for non-invasively monitoring and protecting public health.”


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