P-2360. Wastewater-Based Surveillance of Hepatitis A Virus Across Communities in Alberta, Canada
Bibliographic record
Abstract
Abstract Background Hepatitis A virus (HAV) incident infection in Canada is rarely diagnosed (i.e. incidence of 3.6-10 cases/100,000 persons) (PMID: 18159360). Infections generally relate to imported contaminated food-products, or travelers returning from endemic countries. Due to its fecal-oral spread, possible underdiagnosis, and often-cryptic presentation, HAV is an ideal candidate for wastewater (WW)-based surveillance, a tool increasingly utilized to monitor infectious diseases globally.Figure 1.Longitudinal monitoring of HAV RNA wastewater abundance across eight Alberta municipalities over a 4-month period. Methods 24-hour composite WW was collected weekly from eight geographically disparate, and socioeconomically diverse municipal WW treatment plants in Alberta from August to December 2023. After short-term cold storage, WW was centrifuged, and RNA from the raw pellet was extracted using Qiagen’s RNeasy PowerFecal pro. HAV levels were quantified by RT-qPCR of the vp1 gene. 2021 Canadian census data was used to define population demographics for each participating site. Results HAV was detected in 18/117 (15.4%) WW samples and 5/8 (62.5%) municipalities over the 4-month period (Figure 1). RNA abundance in HAV positive WW samples was a median of 3.4 copies/mL (IQR 0.44 - 6.97). Larger population size (p=0.007), and greater density (p=0.001) were associated with increased likelihood of HAV WW detection, whereas social and economic demographics of populations within sewershed catchments did not associate with likelihood of HAV detection (Table 1). Conclusion HAV RNA is rarely detected in the wastewater of Alberta. Detection was more frequently observed in larger municipalities, which is consistent with non-endemic, imported disease. WW surveillance can potentially be adapted to monitor HAV in the context of outbreaks to reduce secondary transmission, and safeguard public health. Disclosures Mark Swain, MD MSc, Abbott: Advisor/Consultant|Advanz: Advisor/Consultant|Gilead, BMS, CymaBay, Intercept, Genfit, Pfizer, Novartis, Astra Zeneca, GSK, Celgene, Novo Nordisk, Axcella Health Inc., Merck, Galectin Therapeutics: Grant/Research Support|GSK: Advisor/Consultant|Ipsen: Advisor/Consultant|Novo Nordisk: Advisor/Consultant Carla Coffin, MD MSc, Altimmune: Grant/Research Support|Gilead: Grant/Research Support|GSK: Grant/Research Support|Janssen: Grant/Research Support Steven J. Drews, PhD FCCM D(ABMM), Abbott: Grant/Research Support|Danaher: Honoraria|Roche: Advisor/Consultant|Roche: Grant/Research Support
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".