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The goal of this vignette is to provide a place to document how epinowcast has been applied in real-world settings. To start, we will document a few known use cases, many of which the authors are directly involved in. We hope this can both inspire future users to apply the methods to similar use cases of their own, and those currently or previously using epinowcast to document it here. This should also motivate and help prioritize future development. If you are using epinowcast for a real-world application, please consider opening a PR to add a description of the case study to this table.

The table below contains columns to provide information on the following variables related to the use case: Pathogen: Pathogen of interest in the use case, e.g. COVID-19. Data type(s): A brief description of the data source(s) used e.g. individual level line-list clinical case data Purpose: Brief description of the purpose of using epinowcast e.g. research or real-time response Location: Specific geographic location and associated granularity of the data to e.g. counties in the United States Organization type: Type of organization doing the analysis e.g. academic, federal government, local health department Links: Include here any links to manuscripts/pre-prints and github repositories describing and applying the analysis, e.g. Manuscript

Description Pathogen Data type(s) Purpose Location Organization type Links
Nowcasts of COVID-19 hospital admissions in Germany COVID-19 Counts of hospital admissions by date of positive test Real-time response National and state-level in Germany Academic Pre-print, Github repo, Report
Generative Bayesian modelling to nowcast R(t) from line-list data with missing symptom onset date COVID-19 Individual line-list hospitalizations Research National level data in Switzerland Academic Manuscript, Github repo
Nowcasting cases of norovirus in England in winter 2023-2024 Norovirus Counts of norovirus positive laboratory reports Evaluation for real-time response National level data in England Federal government Pre-print