Speaker
Description
Real-time growth and prevalence of infectious diseases are vital information for epidemic response. Yet it is rare to directly observe infections, and so we must reconstruct infection trends from delayed and imperfect alternatives, including time-series of case counts and cross-sectional infection prevalence surveys. Joint inference from multiple data can improve estimates of infection trends, and reveal details about observation processes including proportions of infections observed. epiwave is an R package to access a statistical model for inferring the trend of underlying infections that can be fitted to multiple types of latent observable data. In this presentation, we highlight how we designed the interface for the software to explicitly mirror the form of the underlying statistical models. Specifically, we demonstrate the how different data inputs for each dataset are ingested in a parallel structure - designed to match the logic of model structure itself. The design principles employed present a learning opportunity for anyone interested in design of new statistical software. The flexibility in the parallel structure allows a user to incorporate any number of different data inputs using the same principles, providing support in real-time epidemic analyses where data availability can be volatile and unpredictable, especially during rapidly evolving health crises.
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | package development, design principles, public health, statistical modelling |
|---|---|
| Virtual Option | This submission is for onsite presentation primarily, but I would also like it to be considered for pre-recorded virtual presentation if I don't get an onsite slot |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |
| Interested in serving as reviewer? | saras.windecker@gmail.com |