Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus
We consider a proposed system that would place sensors in a number of wastewater manholes in a community in order to detect genetic remnants of SARS-Cov-2 found in the excreted stool of infected persons. These sensors would continually monitor the manhole’s wastewater, and whenever virus remnants ar...
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| Format: | Article |
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Public Library of Science (PLoS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/130487 |
| _version_ | 1826198342122602496 |
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| author | Nourinejad, Mehdi Berman, Oded Larson, Richard Charles |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Nourinejad, Mehdi Berman, Oded Larson, Richard Charles |
| author_sort | Nourinejad, Mehdi |
| collection | MIT |
| description | We consider a proposed system that would place sensors in a number of wastewater manholes in a community in order to detect genetic remnants of SARS-Cov-2 found in the excreted stool of infected persons. These sensors would continually monitor the manhole’s wastewater, and whenever virus remnants are detected, transmit an alert signal. In a recent paper, we described two new algorithms, each sequentially opening and testing successive manholes for genetic remnants, each algorithm homing in on a neighborhood where the infected person or persons are located. This paper extends that work in six important ways: (1) we introduce the concept of in-manhole sensors, as these sensors will reduce the number of manholes requiring on-site testing; (2) we present a realistic tree network depicting the topology of the sewer pipeline network; (3) for simulations, we present a method to create random tree networks exhibiting key attributes of a given community; (4) using the simulations, we empirically demonstrate that the mean and median number of manholes to be opened in a search follows a well-known logarithmic function; (5) we develop procedures for determining the number of sensors to deploy; (6) we formulate the sensor location problem as an integer nonlinear optimization and develop heuristics to solve it. Our sensor-manhole system, to be implemented, would require at least three additional steps in R&D: (a) an accurate, inexpensive and fast SARS-Cov-2 genetic-remnants test that can be done at the manhole; (b) design, test and manufacture of the sensors; (c) in-the-field testing and fine tuning of an implemented system. |
| first_indexed | 2024-09-23T11:03:21Z |
| format | Article |
| id | mit-1721.1/130487 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:03:21Z |
| publishDate | 2021 |
| publisher | Public Library of Science (PLoS) |
| record_format | dspace |
| spelling | mit-1721.1/1304872022-09-27T16:49:38Z Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus Nourinejad, Mehdi Berman, Oded Larson, Richard Charles Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Massachusetts Institute of Technology. Institute for Data, Systems, and Society We consider a proposed system that would place sensors in a number of wastewater manholes in a community in order to detect genetic remnants of SARS-Cov-2 found in the excreted stool of infected persons. These sensors would continually monitor the manhole’s wastewater, and whenever virus remnants are detected, transmit an alert signal. In a recent paper, we described two new algorithms, each sequentially opening and testing successive manholes for genetic remnants, each algorithm homing in on a neighborhood where the infected person or persons are located. This paper extends that work in six important ways: (1) we introduce the concept of in-manhole sensors, as these sensors will reduce the number of manholes requiring on-site testing; (2) we present a realistic tree network depicting the topology of the sewer pipeline network; (3) for simulations, we present a method to create random tree networks exhibiting key attributes of a given community; (4) using the simulations, we empirically demonstrate that the mean and median number of manholes to be opened in a search follows a well-known logarithmic function; (5) we develop procedures for determining the number of sensors to deploy; (6) we formulate the sensor location problem as an integer nonlinear optimization and develop heuristics to solve it. Our sensor-manhole system, to be implemented, would require at least three additional steps in R&D: (a) an accurate, inexpensive and fast SARS-Cov-2 genetic-remnants test that can be done at the manhole; (b) design, test and manufacture of the sensors; (c) in-the-field testing and fine tuning of an implemented system. 2021-04-21T16:29:03Z 2021-04-21T16:29:03Z 2021-04 2020-12 Article http://purl.org/eprint/type/JournalArticle 1932-6203 https://hdl.handle.net/1721.1/130487 Nourinejad, Mehdi et al. "Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus." PLoS ONE 16, 4 (April 2021): e0248893. © 2021 Nourinejad et al https://doi.org/10.1371/journal.pone.0248893 PLoS ONE Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science (PLoS) PLoS |
| spellingShingle | Nourinejad, Mehdi Berman, Oded Larson, Richard Charles Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus |
| title | Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus |
| title_full | Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus |
| title_fullStr | Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus |
| title_full_unstemmed | Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus |
| title_short | Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus |
| title_sort | placing sensors in sewer networks a system to pinpoint new cases of coronavirus |
| url | https://hdl.handle.net/1721.1/130487 |
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