The eradication of infectious diseases has been of key interest for many years.
While the World Health Organization (WHO) and other health organizations
typically track the progress of disease eradication based on whether regions
are meeting their eradication targets, being able to quantify and/or visually
track the eradication of a disease could prove beneficial. This thesis creates the
“canonical path” to the elimination of measles in the United States (US), using
similar methods as defined by Graham et al. [1]. We build on preliminary work
conducted to fulfill the requirements of an Honours Bachelor of Science in Integrated
Science at McMaster University, and the analysis conducted by Graham
et al. [1], through the investigation of the sensitivity of the path to changes in
its definition, as well how the path changes when we change the characteristics
of the disease. This thesis demonstrates the ability to use a canonical path on
a smaller, country-level scale, by using United States (US) state level data to
create the US canonical path. We also determine the model structures necessary
to simulate the canonical path, which suggests that the canonical path
method is most useful for eradicable diseases for which we have ample knowledge
of the disease, including the natural history of infection and vaccination.
We also predict how the path is affected by the pattern of seasonality and by
the natural history of infection. Overall, the analysis suggests that the more
this method is implemented for other countries that have eliminated measles or
for other diseases for which we have achieved elimination, we may gain insight
of the successes and failures of elimination strategies. This knowledge could
help the WHO and other organizations improve their disease elimination and
eradication strategies in the future. / Thesis / Master of Science (MSc)
Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27490 |
Date | January 2022 |
Creators | O'Meara, Elizabeth |
Contributors | Earn, David, Mathematics |
Source Sets | McMaster University |
Language | English |
Detected Language | English |
Type | Thesis |
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