Placing sensors in sewer networks: A system to pinpoint new cases of coronavirus
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".