# Epinowcast meeting (2022-11-17) **URL:** \ **Category:** Meetings\ **Created:** [21 October 2022 11:24 UTC](https://community.epinowcast.org/t/epinowcast-meeting-2022-11-17/91 "2022-10-21T11:24:23Z")\ **Posts on this page:** 1\ **Showing post:** 4
**Author:** ![samabbott](https://dub1.discourse-cdn.com/flex005/user_avatar/community.epinowcast.org/samabbott/32/5_2.png) [@samabbott](https://community.epinowcast.org/u/samabbott)\ **Post date:** [17 November 2022 07:54 UTC](https://community.epinowcast.org/t/epinowcast-meeting-2022-11-17/91/4 "2022-11-17T07:54:58Z")
Sadly Sam has failed to organise a meeting link or finish the mission statement draft 馃槶. Unfortunately I also have an unavoidable and out of my control clash at 2.30 so will have to dash unless we can push to tomorrow (which I assume would not be good for others?). My bad! My excuse is that I鈥檝e been very distracted working with @sangwoopark on estimating censored delay distributions truncated by an underlying outbreak process. It is pretty fascinating stuff and I have lots of questions about what it means for the kind of analysis we often do. Maybe someone else has a better handle on this! # Some details! This is our working repo (mostly code at the moment): [GitHub - parksw3/epidist-paper](https://github.com/parksw3/dynamicaltruncation) This paper by Shaun Seaman has some fascinating stuff in it (including the explicit connection to nowcasting) but also nicely room for more to be done. I was thinking of asking him to come and present on it at our monthly seminar (that does mean I need to organise it!). > **[Estimating a time-to-event distribution from right-truncated data in an...](https://pmc.ncbi.nlm.nih.gov/articles/PMC9465556/)** > > Time-to-event data are right-truncated if only individuals who have experienced the event by a certain time can be included in the sample. For example, we may be interested in estimating the distribution of time from onset of disease symptoms to ... @sbfnk suggested we use Ebola lineliest data for the case study in this work and it turns out to be the perfect target for nowcasting (including with confirmed/probable case complexity). The repo has code to process the data, visualise etc if wanting to play. [https://www.pnas.org/doi/10.1073/pnas.1518587113](https://www.pnas.org/doi/10.1073/pnas.1518587113) Me pulling out some graphs: [Case study data 路 Issue #9 路 parksw3/epidist-paper 路 GitHub](https://github.com/parksw3/dynamicaltruncation/issues/9#issuecomment-1312112963) Interestingly early versions of this data were used for delay distribution estimation, and Rt based projection. I think it could be a great case study for us. More here [Examples of delay estimation from the literature. 路 Issue #19 路 parksw3/epidist-paper 路 GitHub](https://github.com/parksw3/dynamicaltruncation/issues/19#issuecomment-1313464613). --- _[View the full topic](https://community.epinowcast.org/t/epinowcast-meeting-2022-11-17/91)._