A Proposed Model for Partial Identification of SARS-CoV2 Infection Rates Given Observed Tests and Cases

Helping us understand what the observed numbers of cases and tests mean for COVID-19 spread.

A model that shows how to estimate the effect of suppression measures on the SARS-CoV2 without knowing the true infection rate.
R
Measurement
Bayesian
Data Science
Author

Robert Kubinec

Published

March 28, 2020

For an up to date version of this model, please see our paper at https://osf.io/preprints/socarxiv/jp4wk/.