🚨🎉New preprint (and R package #rstats) out in @F1000Research, with @AdamJKucharski and @sbfnk : We developed the package o2geosocial to reconstruct who-infected-whom from routinely collected surveillance data. f1000research.com/articles/10-31 (NOT YET PEER REVIEWED) 1/5
The objective of o2geosocial is to make the most of epidemiological data when genetic sequences are not available, nor informative. It uses the date of symptoms onset, location, age and genotype of the cases to reconstruct who-infected-whom #CRAN CRAN.R-project.org/package=o2geos… 2/5
This preprint describes the implementation of o2geosocial. It also shows how to group cases into transmission clusters and infer who-infected-whom using o2geosocial and simulated data. We implemented different models to highlight the flexibility of the method. 3/5
In order to make this analysis as reproducible and clear as possible, the code used to generate the analysis is shown along with the results. This example can therefore be used as a tutorial 4/5
We hope that o2geosocial can help maximise the information brought by routinely collected data for outbreak reconstruction, and look forward to seeing / running more applications of this method. The source code of the package is on Github github.com/alxsrobert/o2g… 5/5
• • •
Missing some Tweet in this thread? You can try to
force a refresh
