Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.17816 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908669552099328 |
|---|---|
| author | Palacio, Jose R. Ensor, Katherine B. Keller, Sallie A. Schneider, Rebecca Domakonda, Kaavya Hopkins, Loren Stadler, Lauren B. |
| author_facet | Palacio, Jose R. Ensor, Katherine B. Keller, Sallie A. Schneider, Rebecca Domakonda, Kaavya Hopkins, Loren Stadler, Lauren B. |
| contents | Wastewater-based epidemiology (WBE) is an effective tool for tracking community circulation of respiratory viruses. We address estimating the effective reproduction number ($R_t$) and the relative number of infections from wastewater viral load. Using weekly Houston data on respiratory syncytial virus (RSV), we implement a parsimonious Bayesian renewal model that links latent infections to measured viral load through biologically motivated generation and shedding kernels. The framework yields estimates of $R_t$ and relative infections, enabling a coherent interpretation of transmission timing and phase. We compare two input strategies-(i) raw viral-load measurements with a log-scale standard deviation, and (ii) state-space-filtered load estimates with time-varying variances-and find no practically meaningful differences in inferred trajectories or peak timing. Given this equivalence, we report the filtered input as a pragmatic default because it embeds week-specific variances while leaving epidemiological conclusions unchanged. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17816 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inferring Transmission Dynamics of Respiratory Syncytial Virus from Houston Wastewater Palacio, Jose R. Ensor, Katherine B. Keller, Sallie A. Schneider, Rebecca Domakonda, Kaavya Hopkins, Loren Stadler, Lauren B. Applications Wastewater-based epidemiology (WBE) is an effective tool for tracking community circulation of respiratory viruses. We address estimating the effective reproduction number ($R_t$) and the relative number of infections from wastewater viral load. Using weekly Houston data on respiratory syncytial virus (RSV), we implement a parsimonious Bayesian renewal model that links latent infections to measured viral load through biologically motivated generation and shedding kernels. The framework yields estimates of $R_t$ and relative infections, enabling a coherent interpretation of transmission timing and phase. We compare two input strategies-(i) raw viral-load measurements with a log-scale standard deviation, and (ii) state-space-filtered load estimates with time-varying variances-and find no practically meaningful differences in inferred trajectories or peak timing. Given this equivalence, we report the filtered input as a pragmatic default because it embeds week-specific variances while leaving epidemiological conclusions unchanged. |
| title | Inferring Transmission Dynamics of Respiratory Syncytial Virus from Houston Wastewater |
| topic | Applications |
| url | https://arxiv.org/abs/2511.17816 |