A detailed mathematical modelling framework for the risk of airborne infectious disease transmission
in indoor spaces was developed. This modelling framework enables the mathematical analysis of
experiments conducted at the Airborne Infections Research (AIR) facility of eMalahleni, South Africa.
A model was built using this framework to explore possible causes of why an experiment at the AIR
facility, from 31 August 2015 to 4 December 2015, did not produce expected results. In this experiment
the efficacy of upper room germicidal ultraviolet (GUV) irradiation as an environmental control was
tested. However, the experiment did not produce the expected outcome of having fewer infections in
the test animal room than in the control room. The simulation results indicate that dynamic effects,
caused by switching the GUV lights, power outages, or introduction of new patients, did not result in
the unexpected outcomes. However, a sensitivity analysis highlights that significant uncertainty exists
with risk of transmission predictions based on current measurement practices, due to the reliance on
large viable literature ranges for parameters.
This work builds on the commonly used Wells-Riley equation for the circumstance of the research
facility by including additional mechanisms and dynamics. The model framework is given modularly, to assist in the manipulation of the model for different research questions that are wished to be explored
using such a facility. The developed mathematical model is found useful in improving understanding of
the risk of infection of airborne infectious diseases in indoor spaces, and in the theoretical exploration
of the experiment. Especially the dynamics of the model helped to investigate whether the switching
rate of the upper room GUV lights was adequately slow so that one room did indeed get more infectious
particles than another.
Dissertation (MEng)--University of Pretoria, 2020.