Estimating the underreporting of COVID-19 cases using fatality data
LE3 .A278 2021
2021
Teismann, Holger Mendivil, Franklin
Acadia University
Bachelor of Science
Honours
Mathematics & Statistics
This thesis presents research completed in collaboration with the Acadia COVID-19 Modelling Group during the spring and summer of 2020, as the COVID-19 pandemic began. In particular, it focuses on the lack of reliable COVID-19 case incidence data due to variation in testing and reporting policies as well as resources available across different regions. We introduce a phenomenological GLM model that uses death data and estimates of the infection fatality ratio (IFR) to produce estimates for the true number of COVID-19 cases in various regions. We then compare estimates from this model with model estimates from another study [11] as well as serological data from various regions. We also discuss a compartmental model, based on the SIR model and modified to fit the specifications of COVID-19. We use the final size relation for this model to improve regional estimates of the IFR and, which could be used to improve the estimates from our GLM model.
The author retains copyright in this thesis. Any substantial copying or any other actions that exceed fair dealing or other exceptions in the Copyright Act require the permission of the author.
https://scholar.acadiau.ca/islandora/object/theses:3602
