Estimating the effective reproduction number from wastewater (R <sub>t</sub> ): A methods comparison
Bibliographic record
Abstract
Abstract Background The effective reproduction number (R t ) is a dynamic indicator of current disease spread risk. Wastewater measurements of viral concentrations are known to correlate with clinical measures of diseases and have been incorporated into methods for estimating the R t . Methods We review wastewater-based methods to estimate the R t for SARS-CoV-2 based on similarity to the reference case-based R t , ease of use, and computational requirements. Using wastewater data collected between August 1, 2022 and February 20, 2024 from 200 wastewater treatment plants across New York State, we fit eight wastewater R t models identified from the literature. Each model is compared to the R t estimated from case data for New York at the sewershed (wastewater treatment plant catchment area), county, and state levels. Results We find a high degree of similarity across all eight methods despite differences in model parameters and approach. Further, two methods based on the common measures of percent change and linear fit reproduced the R t from case data very well and a GLM accurately predicted case data. Model output varied between spatial scales with some models more closely estimating sewershed R t values than county R t values. Similarity to clinical models was also highly correlated with the proportion of the population served by sewer in the surveilled communities (r = 0.77). Conclusions While not all methods that estimate R t from wastewater produce the same results, they all provide a way to incorporate wastewater concentration data into epidemic modeling. Our results show that straightforward measures like the percent change can produce similar results of more complex models. Based on the results, researchers and public health officials can select the method that is best for their situation. Key messages Wastewater data has been used to estimate the R t in different ways but the relative strengths and weaknesses of each method were unknown. R t estimation results from wastewater data are influenced by sewershed population size and geographic aggregation making selection of the best method dependent on the study location and available data. Estimating the R t from wastewater is desirable because wastewater data are anonymous, comprehensive, and efficient for measuring disease burden.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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".