TY - JOUR
T1 - Optimization of sewage sampling for wastewater-based epidemiology through stochastic modeling
AU - Martin, Max
AU - Goethals, Paul
AU - Newhart, Kathryn
AU - Rhodes, Emily
AU - Vogel, Jason
AU - Stevenson, Bradley
N1 - Funding Information:
The views and opinions expressed in this manuscript are those of the authors and do not reflect the views and opinions of the United States Military Academy, the United States Army, or the United States Department of Defense. The authors also acknowledge the University of Oklahoma Office of the Vice President of Operations for funding the sampling and the students and staff at the Oklahoma Water Survey, Oklahoma University, for their diligent work in assisting sample collection and analysis.
Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - The proliferation of the SARS-CoV-2 global pandemic has brought to attention the need for epidemiological tools that can detect diseases in specific geographical areas through non-contact means. Such methods may protect those potentially infected by facilitating early quarantine policies to prevent the spread of the disease. Sampling of municipal wastewater has been studied as a plausible solution to detect pathogen spread, even from asymptomatic patients. However, many challenges exist in wastewater-based epidemiology such as identifying a representative sample for a population, determining the appropriate sample size, and establishing the right time and place for samples. In this work, a new approach to address these questions is assessed using stochastic modeling to represent wastewater sampling given a particular community of interest. Using estimates for various process parameters, inferences on the population infected are generated with Monte Carlo simulation output. A case study at the University of Oklahoma is examined to calibrate and evaluate the model output. Finally, extensions are provided for more efficient wastewater sampling campaigns in the future. This research provides greater insight into the effects of viral load, the percentage of the population infected, and sampling time on mean SARS-CoV-2 concentration through simulation. In doing so, an earlier warning of infection for a given population may be obtained and aid in reducing the spread of viruses.
AB - The proliferation of the SARS-CoV-2 global pandemic has brought to attention the need for epidemiological tools that can detect diseases in specific geographical areas through non-contact means. Such methods may protect those potentially infected by facilitating early quarantine policies to prevent the spread of the disease. Sampling of municipal wastewater has been studied as a plausible solution to detect pathogen spread, even from asymptomatic patients. However, many challenges exist in wastewater-based epidemiology such as identifying a representative sample for a population, determining the appropriate sample size, and establishing the right time and place for samples. In this work, a new approach to address these questions is assessed using stochastic modeling to represent wastewater sampling given a particular community of interest. Using estimates for various process parameters, inferences on the population infected are generated with Monte Carlo simulation output. A case study at the University of Oklahoma is examined to calibrate and evaluate the model output. Finally, extensions are provided for more efficient wastewater sampling campaigns in the future. This research provides greater insight into the effects of viral load, the percentage of the population infected, and sampling time on mean SARS-CoV-2 concentration through simulation. In doing so, an earlier warning of infection for a given population may be obtained and aid in reducing the spread of viruses.
KW - Epidemiology
KW - Monte Carlo simulation
KW - Wastewater
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U2 - 10.1186/s44147-023-00180-1
DO - 10.1186/s44147-023-00180-1
M3 - Article
AN - SCOPUS:85148445490
SN - 1110-1903
VL - 70
JO - Journal of Engineering and Applied Science
JF - Journal of Engineering and Applied Science
IS - 1
M1 - 11
ER -