UNDERSTANDING GEOSPATIAL DYNAMICS OF PARASITE MIGRATION AND HUMAN MOBILITY AS FACTORS CONTRIBUTING TO MALARIA TRANSMISSION IN THE GREATER MEKONG SUBREGION

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dc.contributor.advisorStewart, Kathleenen_US
dc.contributor.authorLi, Yaoen_US
dc.contributor.departmentGeographyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2021-09-17T05:32:07Z
dc.date.available2021-09-17T05:32:07Z
dc.date.issued2021en_US
dc.description.abstractMuch effort has been made to control malaria over the past decades in South-East Asia Confirmed cases of Plasmodium falciparum (P.f.) and Plasmodium vivax (P.v.) malaria were reduced by 46%, and mortality by 60%. However, malaria remains a major problem in the Greater Mekong Subregion (GMS) with the emerging resistance to the artemisinins and their partner drugs. This raises concerns that the usefulness of first-line malaria treatments may be diminishing in the GMS, and that drug resistance could spread worldwide. Estimating malaria parasite migration patterns is crucial for malaria elimination as well as understanding the role that human mobility plays in malaria transmission. This dissertation will focus on the GMS, especially Cambodia and Myanmar which have been widely regarded as the epicenter of emerging resistance to artemisinin-based combination therapies. This dissertation is structured as three separate studies that look first at the movement of malaria parasites across a region, and then two studies that focus on human movement and how these movements can lead to increased exposure as well as transmission of malaria. In the first study, a semi-automatic workflow was developed to select the optimal number of demes that will maximize model accuracy and minimize computing time when computing estimated effective migration surfaces. A validation analysis showed that the optimized grids displayed both high model accuracy and reduced processing time compared to grid densities selected in an unguided manner. In the second study, an agent-based simulation model was built to estimate and simulate the daily movements of local populations in Singu and Ann Townships in Myanmar in order to identify how two townships in different parts of Myanmar compared with respect to mobility and P.v. and P.f. positivity. The third study examined mobility patterns of local village populations in Singu Township, Myanmar when they traveled longer distances outside of Singu, and discuss these patterns of regional travel in the context of daily mobility within the township.en_US
dc.identifierhttps://doi.org/10.13016/ltz2-xxvk
dc.identifier.urihttp://hdl.handle.net/1903/27791
dc.language.isoenen_US
dc.subject.pqcontrolledGeographyen_US
dc.subject.pqcontrolledGeographic information science and geodesyen_US
dc.subject.pquncontrolledAgent based modelingen_US
dc.subject.pquncontrolledGenomicen_US
dc.subject.pquncontrolledGISen_US
dc.subject.pquncontrolledGreater Mekong Subregionen_US
dc.subject.pquncontrolledHuman mobilityen_US
dc.subject.pquncontrolledMalariaen_US
dc.titleUNDERSTANDING GEOSPATIAL DYNAMICS OF PARASITE MIGRATION AND HUMAN MOBILITY AS FACTORS CONTRIBUTING TO MALARIA TRANSMISSION IN THE GREATER MEKONG SUBREGIONen_US
dc.typeDissertationen_US

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