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Early warning signals of disease elimination
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Southall, Emma Rose (2021) Early warning signals of disease elimination. PhD thesis, University of Warwick.
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WRAP_Theses_Southall_2021.pdf - Submitted Version - Requires a PDF viewer. Download (12Mb) | Preview |
Official URL: http://webcat.warwick.ac.uk/record=b3795686
Abstract
The battle against infectious diseases includes some notable success stories, including the global eradication of smallpox [82] and wild poliovirus type 2 [2]. The 2012 London Declaration on neglected tropical diseases (NTDs) built on these successes by establishing goals for elimination and eradication of 10 NTDs by 2020. The intention of this declaration was to achieve elimination through various active interventions, from vector control to mass drug administration. The potential for eradicating diseases such as polio, guinea worm, measles, mumps or rubella is immense (International Task Force for Disease Elimination, [32]). However, each elimination program shares one fundamental challenge: how do we know when we can relax controls?
Control campaigns have substantial economic consequences; as such there are high demands to reduce costs and reallocate resources. However, if campaigns are stopped prematurely it can result in disease resurgence and subsequently put control efforts back by decades. It can be very difficult to know when the prevalence is low enough that the disease will die out without further intervention, particularly for diseases that lack accurate tests. To overcome the challenges of identifying disease elimination, numerous studies have suggested the use of early warning signals (EWSs). EWSs are statistical methods which are used to anticipate a critical transition before it is reached. EWSs offer the ability to anticipate disease elimination indirectly in real-world noisy time-series data. In this thesis, I investigate EWSs of disease elimination. I focus on analytically understanding the behaviour of EWSs when calculated on different types of infectious disease data. I present new methods for combining multiple data streams into a single source and introduce preprocessing methods which can improve the predictive power of EWSs. Lastly, I use EWSs with a specific infectious disease, gambiense human African trypanosomiasis (gHAT) and explore whether EWSs can be used to detect regional elimination of this disease.
| Item Type: | Thesis (PhD) | ||||
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| Subjects: | Q Science > QA Mathematics Q Science > QR Microbiology > QR180 Immunology R Medicine > RA Public aspects of medicine R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine |
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| Library of Congress Subject Headings (LCSH): | Communicable diseases, Communicable diseases -- Prevention -- Statistics, Communicable diseases -- Epidemiology -- Data processing, Epidemics -- Mathematical models, Epidemics -- Prevention -- Data processing | ||||
| Official Date: | 2021 | ||||
| Dates: |
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| Institution: | University of Warwick | ||||
| Theses Department: | Mathematics for Real-World Systems Centre for Doctoral Training | ||||
| Thesis Type: | PhD | ||||
| Publication Status: | Unpublished | ||||
| Supervisor(s)/Advisor: | Dyson, Louise ; Tildesley, Michael J. | ||||
| Format of File: | |||||
| Extent: | viii, 200, xv leaves : illustrations, charts | ||||
| Language: | eng |
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