Detecting And Controlling Insect Vectors In Urban Environments: Novel Bayesian Methods For Complex Spatial Data

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dc.contributor.advisorJason A. Roy
dc.contributor.advisorMichael Z. Levy
dc.contributor.authorBillig, Erica
dc.date2023-05-17T19:23:12.000
dc.date.accessioned2023-05-22T16:59:49Z
dc.date.available2001-01-01T00:00:00Z
dc.date.copyright2018-02-23T20:17:00-08:00
dc.date.issued2017-01-01
dc.date.submitted2018-02-23T12:42:06-08:00
dc.description.abstractEfforts to control the spread of vector-borne diseases often focus on the vector itself. Here, we develop novel methods to strategically guide the search for vectors over urban landscapes. The methodology is motivated by Triatoma infestans, the vector of Chagas disease, a re-emerging vector in Arequipa, Peru. We first propose a novel stochastic epidemic model that incorporates both the counts of disease vectors at each observed house and the complex spatial dispersal dynamics. The goal of our analysis is to predict and identify houses that are infested with T. infestans for entomological inspection and insecticide treatment. A Bayesian method is used to augment the observed data, estimate the insect population growth and dispersal parameters, and determine posterior infestation probabilities of households. We investigate the properties of the model through simulation studies and implement the strategy in a region of Arequipa by inspecting houses with the highest posterior probabilities of infestation and report the results from the field study. After piloting this model in the field and assessing the strengths and weaknesses, we propose a much faster method that extends a Gaussian Field (GF) model to incorporate the urban landscape. GF models can be used to create risk maps of vector presence across large urban environments. However, these models do not typically account for the possibility that city streets function as permeable barriers for insect vectors. We extend GF models to account for this urban landscape. We demonstrate our method on simulated datasets and then apply it to data on T. infestans. We estimate that streets increase the effect of distance on the probability of vector presence at least 1.5 fold compared to the undivided environment. Lastly, we propose a Bayesian generalized multivariate conditional autoregressive approach to jointly model the distribution of vectors, T. infestans, with the proportion of vectors that carry the parasite of Chagas disease, Trypanosoma cruzi. We demonstrate the properties of the model using simulation studies, and apply the method to data from Arequipa.
dc.description.degreeDoctor of Philosophy (PhD)
dc.format.extent84 p.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://repository.upenn.edu/handle/20.500.14332/29085
dc.languageen
dc.legacy.articleid3979
dc.legacy.fulltexturlhttps://repository.upenn.edu/cgi/viewcontent.cgi?article=3979&context=edissertations&unstamped=1
dc.provenanceReceived from ProQuest
dc.rightsErica Billig
dc.source.issue2193
dc.source.journalPublicly Accessible Penn Dissertations
dc.source.statuspublished
dc.subject.otherBayesian
dc.subject.otherChagas
dc.subject.otherGaussian field
dc.subject.otherSIR
dc.subject.otherspatial
dc.subject.otherEpidemiology
dc.subject.otherStatistics and Probability
dc.titleDetecting And Controlling Insect Vectors In Urban Environments: Novel Bayesian Methods For Complex Spatial Data
dc.typeDissertation/Thesis
digcom.date.embargo2001-01-01T00:00:00-08:00
digcom.identifieredissertations/2193
digcom.identifier.contextkey11636671
digcom.identifier.submissionpathedissertations/2193
digcom.typedissertation
dspace.entity.typePublication
relation.isAuthorOfPublication44f2318b-9ddf-44be-8419-62fc2cf88e14
relation.isAuthorOfPublication.latestForDiscovery44f2318b-9ddf-44be-8419-62fc2cf88e14
upenn.graduate.groupEpidemiology & Biostatistics
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