Projecting and tracking COVID-19 infection rates in England with R, leveraging Wikidata to tag scientific abstracts, and a new deep-learning workflow with the luz package Episode Links This week's curator: Robert Hickman (@robwhickman (https://twitter.com/robwhickman)) Tracking SARS-CoV-2 In England with {epidemia} (https://imperialcollegelondon.github.io/epidemia/articles/multiple-obs.html) Tagging the Scientific Abstracts with Wikidata Items (https://dwayzer.netlify.app/posts/2021-06-15-tagging-the-abstracts-with-wikidata-items) Que haja luz: More light for torch! (https://blogs.rstudio.com/tensorflow/posts/2021-06-17-luz) Entire issue available at rweekly.org/2021-W25 (https://rweekly.org/2021-W25.html) Supplemental Resources {epidemia} package documentation (https://imperialcollegelondon.github.io/epidemia/index.html) A COVID-19 Model for Local Authorities of the United Kingdom (https://rss.org.uk/RSS/media/File-library/News/2021/MishraScott.pdf) How epidemiology has shaped the COVID pandemic (https://www.nature.com/articles/d41586-021-00183-z)