Epidemiologists should consider GLM & IMR for better Covid death predictions and overcome testing bias

[This article was first published on R-posts.com, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Social science research network (SSRN) reviewer has posted our “A Novel Solution to Biased Data in Covid-19 Incidence Studies” written by myself (H D Vinod) and K Theiss.  It uses a new two-equation generalized linear model (glm) with Poisson link for forecasting Covid-19 deaths in the US and individual states. The first equation explicitly models the probability of being tested. It helps build an estimate of the bias using inverse mills ratio (IMR) Detailed paper is available for free download at:


It uses open-source R software.  A supplement to the paper entitled “State-by-state Forecasts of Covid-19 Deaths” is available for free download at http://www.fordham.edu/economics/vinod/WeeklyCovid.pdf

Our bias-corrected out-of-sample death forecasts for future weeks should help state governors and mayors decide on limits to opening local economies.  One can compare the performance of democratic versus republican governors using the information tabulated.

To leave a comment for the author, please follow the link and comment on their blog: R-posts.com.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)