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Author ORCID Identifier

https://orcid.org/0000-0003-1373-3177

AccessType

Campus-Only Access for One (1) Year

Document Type

dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Public Health

Year Degree Awarded

2024

Month Degree Awarded

February

First Advisor

Andrew Lover

Second Advisor

Nicholas Reich

Third Advisor

Stephen Rich

Fourth Advisor

Youssef Oulhote

Subject Categories

Epidemiology | Parasitic Diseases

Abstract

In 2020, an estimated 241 million cases and 627,000 deaths worldwide were due to malaria. Malaria is caused by a Plasmodium parasite, which is transmitted from person to person through the bite of an Anopheles mosquito. The Greater Mekong Subregion (GMS) has made great progress towards malaria elimination over the last decade, however, current tactics may be insufficient to achieve the goal of subregional elimination by the year 2030. Ivermectin (IVM) has been proposed as a useful tool for eliminating malaria because it is a low-cost mosquitocide that can be safely administered to cattle. The extent to which this drug impacts the mortality of mosquitoes in the GMS is unknown. Chapters 1 and 2 investigated the impact of IVM on the mortality of anophelines in Central Viet Nam. Chapter 1 is a laboratory-based randomized control trial to evaluate the impact of feeding on IVM-treated cattle on the mortality of Anopheles dirus and Anopheles epiroticus. Results from this work show that both mosquito species had a reduced lifespan (a key factor impacting transmission) after feeding on IVM. Chapter 2 is a randomized village-based trial to quantify the effect of IVM-treated cattle on anopheline populations in treated vs. untreated villages. Using a difference-in-differences model, results from this study showed that there was not a statistically significant difference in the number of captured mosquitoes in treatment villages compared to control villages. Additionally, IVM treatment did not impact mosquito species diversity uniformly across treated villages. Chapter 3 uses serological data from long-lasting Plasmodium serological markers to quantify the malaria seroprevalence in Northern Laos, and delineate factors associated with Plasmodium seropositivity based on data from a 2016 cross-sectional study (N = 5,084). Using two different modeling approaches (a finite mixture model and a Bayesian model), this chapter provides an alternative method for classifying serological data in an area with declining malaria prevalence.

DOI

https://doi.org/10.7275/36465189

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