2404.04417
Evaluating COVID-19 Surveillance Testing Strategies at Colorado School of Mines: A Stochastic Modeling Approach
Laura Albrecht, Karin Leiderman, Suzanne S. Sindi, Douglas Nychka
correctmedium confidence
- Category
- Not specified
- Journal tier
- Specialist/Solid
- Processed
- Sep 28, 2025, 12:56 AM
- arXiv Links
- Abstract ↗PDF ↗
Audit review
The paper’s next-generation matrix (NGM) setup (infected states X = (E, IA, IS, IT), F with only first row nonzero, and V as given) and its reported specialization R0 = (14.8 − 10.8 α) β at the Table 2 parameter values are consistent with the model description and tables (modulo minor notation/OCR glitches in the supplement) . The candidate solution, while exploiting the rank‑one structure correctly, misidentifies the IA outflow term by using rI (isolation removal) in place of γ (recovery), and then compensates by plugging an incorrect “baseline” σ ≈ 0.85 to numerically recover 14.8; Table 2’s baseline is σ = 0.4, not 0.85 . This parameter swap materially changes the derived closed form and its σ‑dependence, so the candidate proof is not consistent with the paper’s model despite landing on the same final numeric line.
Referee report (LaTeX)
\textbf{Recommendation:} minor revisions \textbf{Journal Tier:} specialist/solid \textbf{Justification:} A solid, application-focused study with a standard but correctly executed NGM derivation and a clear connection between testing structure and R0. The main quantitative message is supported by the model. Minor issues are confined to notation in the supplement and the need to make an implicit symptomatic-outflow assumption explicit.