Malaria is one of the most devastating infectious diseases, with nearly half of the worlds population currently at risk of infection. Although mathematical models have made significant contributions towards the control and elimination of malaria, it continues to evade control. This thesis focuses on two aspects of malaria that complicate dynamics, helping it persist.
The basic reproductive number is one of the most important epidemiological quantities as it provides a foundation for control and elimination. Recently, it has been suggested that R0 should be modified to account for the effects of finite host population on a single disease-generation. In chapter 2, we analytically calculate these finite-population reproductive numbers for both vector-borne and directly transmitted diseases with homogeneous transmission. We find simple, generalizable formula and show that when the population is small, control and elimination may be easier than predicted by R0.
In chapter 3, we extend the results of chapter 2 and find expressions for the finite- population reproductive numbers for directly transmitted diseases with different types of heterogeneity in transmission. We also outline a framework for discussing the different types of heterogeneity in transmission. We show that although the effects of heterogeneity in a small population are complex, the implications for control are simple: when R0 is large relative to the size of the population, control or elimination is made easier by heterogeneity.
Another basic question in malaria modeling is the effects of immunity on the population- level dynamics of malaria. In chapter 4, we explore the possibility that clinical immunity can cause bistable malaria dynamics. This has important implications for control: in areas with bistable malaria, if malaria could be eliminated until clinical immunity wanes, it would not be able to invade. We built a simple, analytically tractable model of malaria transmission and solved it to find a criterion for when we expect bistability to occur. Additionally, we review what is known about about the parameters underlying the model and highlighted key clinical immunity parameters for which little is known. Building on these results, in chapter 5, we fit the model developed in chapter 4 to incidence data from Kericho, Kenya and estimate key clinical immunity parameters to better understand the role clinical immunity plays in malaria transmission.
Finally, in chapter 6, we summarize the key results and discuss the broader implications of these findings on future malaria control. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/18313 |
| Date | 11 1900 |
| Creators | Keegan, Lindsay T |
| Contributors | Dushoff, Jonthan, Biology |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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