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Bayesian Optimization With Gaussian ...

Holden, Tobias McKenzie,

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Bayesian Optimization With Gaussian Process Emulation Applied to Calibration of Environmental and Within-Host Parameters in an Agent-Based Model of Plasmodium falciparum Malaria Transmission /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian Optimization With Gaussian Process Emulation Applied to Calibration of Environmental and Within-Host Parameters in an Agent-Based Model of Plasmodium falciparum Malaria Transmission // Tobias McKenzie Holden.
Author:	Holden, Tobias McKenzie,
Description:	1 electronic resource (103 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Epidemiology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32038837
ISBN:	9798315797388

Bayesian Optimization With Gaussian Process Emulation Applied to Calibration of Environmental and Within-Host Parameters in an Agent-Based Model of Plasmodium falciparum Malaria Transmission /
Holden, Tobias McKenzie,

Bayesian Optimization With Gaussian Process Emulation Applied to Calibration of Environmental and Within-Host Parameters in an Agent-Based Model of Plasmodium falciparum Malaria Transmission /Tobias McKenzie Holden. - 1 electronic resource (103 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

Malaria continues to be a major global health concern and is responsible for more than 500,000 deaths annually, with more than 90% in resource-limited areas of sub-Saharan Africa. Individual-based models have become key parts of the effort to eradicate malaria, informing decisions about when, where, and how to effectively intervene against disease. These models simulate processes within and interaction between humans and mosquitos such that population-level phenomena emerge mechanistically but require calibration to site-specific epidemiological data following changes to model structure. For more detailed, complex models, the large number of uncertain model parameters makes calibration to epidemiological data challenging. We developed a user-friendly software pipeline for applying Bayesian optimization with Gaussian process emulation and trust-region based Thompson sampling to calibration of a complex malaria transmission model. We demonstrated the functionality of the pipeline in two key calibration applications. The pipeline successfully captured local malaria transmission intensity and seasonality from primary data in a mechanistic model of climate-driven mosquito development, generating a baseline model of transmission at a well-studied malaria trial site. Following updates to introduce heterogeneity and decay to modeled immunity, the pipeline rescued model goodness-of-fit to data describing uncomplicated and severe disease incidence, prevalence, parasite densities, and infectiousness across eight diverse malaria-endemic sites. The resulting recalibrated within-host model also had improved fit to out-of-sample datasets describing infection duration and multiplicity.

English

ISBN: 9798315797388Subjects--Topical Terms:

568544
Epidemiology.
Subjects--Index Terms:

Calibration

Bayesian Optimization With Gaussian Process Emulation Applied to Calibration of Environmental and Within-Host Parameters in an Agent-Based Model of Plasmodium falciparum Malaria Transmission /
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520 $a Malaria continues to be a major global health concern and is responsible for more than 500,000 deaths annually, with more than 90% in resource-limited areas of sub-Saharan Africa. Individual-based models have become key parts of the effort to eradicate malaria, informing decisions about when, where, and how to effectively intervene against disease. These models simulate processes within and interaction between humans and mosquitos such that population-level phenomena emerge mechanistically but require calibration to site-specific epidemiological data following changes to model structure. For more detailed, complex models, the large number of uncertain model parameters makes calibration to epidemiological data challenging. We developed a user-friendly software pipeline for applying Bayesian optimization with Gaussian process emulation and trust-region based Thompson sampling to calibration of a complex malaria transmission model. We demonstrated the functionality of the pipeline in two key calibration applications. The pipeline successfully captured local malaria transmission intensity and seasonality from primary data in a mechanistic model of climate-driven mosquito development, generating a baseline model of transmission at a well-studied malaria trial site. Following updates to introduce heterogeneity and decay to modeled immunity, the pipeline rescued model goodness-of-fit to data describing uncomplicated and severe disease incidence, prevalence, parasite densities, and infectiousness across eight diverse malaria-endemic sites. The resulting recalibrated within-host model also had improved fit to out-of-sample datasets describing infection duration and multiplicity.
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650 4 $a Epidemiology. $3 568544
650 4 $a Applied mathematics. $3 2122814
650 4 $a Parasitology. $3 635062
653 $a Calibration
653 $a Malaria
653 $a Modeling
653 $a Optimization
653 $a Seasonality
653 $a Within-host model
690 $a 0766
690 $a 0364
690 $a 0718
710 2 $a Northwestern University. $b Health Sciences Integrated PhD Program. $e degree granting institution. $3 3823154
720 1 $a Gerardin, Jaline $e degree supervisor.
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