Thanks for your fantastic talk yesterday @kejohnson9. I was particularly interested in one of your leading questions, i.e., “How do we streamline deploying scientific ideas/models into production and tool development”?
I think the forecasting/nowcasting community is fast evolving and it’s important to act on this now rather than later. On the call, I asked if we could employ the tidymodels approach where the community has developed guidelines for developing models. Some interface/engine/output elements are shared in most of the packages in this ecosystem, so I think it might be more straightforward to streamline these. For example, most of the packages use stan directly or indirectly (epinowcast, EpiNow2, EpiSewer, epidist, etc). The workflow is often similar, you nowcast or forecast a timeseries of cases and evaluate with a package like scoringutils which expects the inputs to be structured in a certain way. Most of the packages expect you to define priors or distributions. If these standards or guidelines are not established now, we may end up with a fragmented ecosystem like the wider epidemiology ecosystem. What are folks’ thoughts on achieving this in this child ecosystem? Would a set of guidelines like that of tidymodels be welcomed and what would it take to get to this?