wwinference

2024

An implementation of a hierarchical semi-mechanistic renewal approach jointly calibrating to multiple wastewater concentration datasets from subsets of a specified population and epidemiological indicators, such as cases or hospital admissions, from the whole population.

ActivePackage2024version0.1.1.99stars31

1. Johnson, K.
2. Morris, D.
3. Abbott, S.
4. Bernal Zelaya, C.
5. Vega Yon, G. G.University of UtahiD
6. Bayer, D.
7. Magee, A.
8. Olesen, S.

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Jointly infers infection dynamics from wastewater data and epidemiological indicators

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An implementation of a hierarchical semi-mechanistic renewal approach jointly calibrating to multiple wastewater concentration datasets from subsets of a specified population and epidemiological indicators, such as cases or hospital admissions, from the whole population. The framework extends the widely used semi-mechanistic renewal framework ‘EpiNow2’, using a Bayesian latent variable approach implemented in the probabilistic programming language ‘Stan’. The package fits these two data sources and produces estimated and forecasted hospital admissions, estimated and forecasted wastewater concentrations, and global and local R(t) estimates for the subpopulations represented by each wastewater catchment area.

Cite

@Manual{wwinference,
  title = {wwinference: Jointly infers infection dynamics from wastewater data and epidemiological indicators},
  author = {{Johnson}, {Kaitlyn} and {Morris}, {Dylan} and {Abbott}, {Sam} and {Bernal Zelaya}, {Christian} and {Vega Yon}, {George G.} and {Bayer}, {Damon} and {Magee}, {Andrew} and {Olesen}, {Scott}},
  year = {2024},
  url = {https://github.com/cdcgov/ww-inference-model/},
  note = {R package version 0.1.1.99, https://cdcgov.github.io/ww-inference-model/},
}

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