First page number:
Last page number:
Most epidemiologists elect to use statistical models that use population-level data to make inference on the spread of some virus or disease. This has become commonplace in the fields of epidemiology and biostatistics since most data used to construct and verify epidemic models are recorded at the population-level. Obtaining inference from a population-level model may be beneficial in studying the spread of disease in a homogeneous population, but the use of such models to describe a heterogeneous population results in inadequate inference. The inaccuracy of these models is further amplified when one tries to make individual-level inference from these population-level models. This thesis argues for the adoption of individual-level (agent-based) inference when attempting to obtain inference for an individual or a heterogeneous population. To support this argument, an example of the ecological fallacy is provided and an epidemic agent-based model is designed to analyze the SARS-CoV-2 outbreak on the Diamond Princess cruise ship. To aid in simulation, a surrogate model is constructed to interpolate analyses for the computationally expensive agent-based model. Finally, the extension of such a method to larger data sets, such as Clark County, Nevada, is considered.
epidemiology, agent-based, surrogate, SARS-CoV-2, statistics
Applied Statistics | Biostatistics | Disease Modeling | Statistical Methodology
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 License
Addressing the Ecological Fallacy with Lagrangian Inference.
Available at: https://digitalscholarship.unlv.edu/award/49