Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.
Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.
Author ORCID Identifier
https://orcid.org/0000-0002-0370-9846
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Public Health
Year Degree Awarded
2021
Month Degree Awarded
September
First Advisor
Nicholas Reich
Second Advisor
Laura Balzer
Third Advisor
David Osthus
Subject Categories
Epidemiology
Abstract
Infectious disease modeling has emerged as a powerful data driven tool for monitoring outbreaks, assessing intervention strategies, and allocating public health resources. This thesis addresses a variety of challenges faced in real-world infectious disease forecasting. We first present methods for aggregating forecasts made at different spatial scales, where explicitly modeling the spatial dependency would be computationally prohibitive. We then extend the mechanistic model framework to create an operational forecasting model capable of handling real-world COVID-19 surveillance system issues. Finally, we propose a new framework for merging mechanistic and statistical approaches to infectious disease forecasting. This framework allows modelers to construct “semi-mechanistic” models that draw from the strengths of both mechanistic and statistical paradigms. In an application setting of forecasting COVID-19 cases and deaths, we demonstrate that a semi-mechanistic model outperforms both a fully mechanistic model and a fully statistical model.
DOI
https://doi.org/10.7275/23793198
Recommended Citation
Gibson, Graham, "APPLIED INFECTIOUS DISEASE FORECASTING FOR PUBLIC HEALTH" (2021). Doctoral Dissertations. 2328.
https://doi.org/10.7275/23793198
https://scholarworks.umass.edu/dissertations_2/2328
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License