A Review of Wastewater-Based Epidemiology for the SARS-CoV-2 Virus in Rural, Remote, and Resource-Constrained Settings Internationally: Insights for Implementation, Research, and Policy for First Nations in Canada
Bibliographic record
Abstract
Wastewater-based epidemiology (WBE) is regarded as a support tool for detecting and assessing the prevalence of infectious diseases at a population level. For rural, remote, and resource-constrained communities with little access to other public health monitoring tools, WBE can be a low-cost approach to filling gaps in population health knowledge to inform public health risk assessment and decision-making. This rapid review explores and discusses unique considerations of WBE in key settings, with a focus on the detection of the SARS-CoV-2 virus, which has rapidly expanded WBE infrastructure globally. To frame our understanding of possibilities for WBE with First Nations in Alberta, we address the following questions: What are the unique considerations and challenges for WBE under similar contexts in rural, remote, or resource-constrained settings? What are the resources and expertise required to support WBE? This review identifies several unique considerations for WBE in rural, remote, and resource-constrained communities, including costs, accessibility, operator capacity, wastewater infrastructure, and data mobilization-highlighting the need for equity in WBE. In summary, most resource-constrained communities require additional support from external research and/or governmental bodies to undertake WBE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".