An Efficient Approach for Dengue Mitigation: A Computational Framework

Dengue mitigation is a major research area among scientist who are working towards an effective management of the dengue epidemic. An effective dengue mitigation requires several other important components. These components include an accurate epidemic modeling, an efficient epidemic prediction, and...

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Main Author: Dinayadura, Nirosha
Other Authors: Bryce, Renee
Format: Others
Language:English
Published: University of North Texas 2019
Subjects:
Online Access:https://digital.library.unt.edu/ark:/67531/metadc1505177/
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spelling ndltd-unt.edu-info-ark-67531-metadc15051772021-09-12T05:30:38Z An Efficient Approach for Dengue Mitigation: A Computational Framework Dinayadura, Nirosha Dengue Mitigation Computational Framework Regression Ensemble Resource Allocation Dengue Prediction Dengue -- Epidemiology. Dengue -- Prevention. Dengue mitigation is a major research area among scientist who are working towards an effective management of the dengue epidemic. An effective dengue mitigation requires several other important components. These components include an accurate epidemic modeling, an efficient epidemic prediction, and an efficient resource allocation for controlling of the spread of the dengue disease. Past studies assumed homogeneous response pattern of the dengue epidemic to climate conditions throughout the regions. The dengue epidemic is climate dependent and also it is geographically dependent. A global model is not sufficient to capture the local variations of the epidemic. We propose a novel method of epidemic modeling considering local variation and that uses micro ensemble of regressors for each region. There are three regressors that are used in the construction of the ensemble. These are support vector regression, ordinary least square regression, and a k-nearest neighbor regression. The best performing regressors get selected into the ensemble. The proposed ensemble determines the risk of dengue epidemic in each region in advance. The risk is then used in risk-based resource allocation. The proposing resource allocation is built based on the genetic algorithm. The algorithm exploits the genetic algorithm with major modifications to its main components, mutation and crossover. The proposed resource allocation converges faster than the standard genetic algorithm and also produces a better allocation compared to the standard algorithm. University of North Texas Bryce, Renee Tiwari, Chetan Fu, Song Mikler, Armin R. 2019-05 Thesis or Dissertation x, 98 pages Text local-cont-no: submission_1463 https://digital.library.unt.edu/ark:/67531/metadc1505177/ ark: ark:/67531/metadc1505177 English Public Dinayadura, Nirosha Copyright Copyright is held by the author, unless otherwise noted. All rights Reserved.
collection NDLTD
language English
format Others
sources NDLTD
topic Dengue Mitigation
Computational Framework
Regression Ensemble
Resource Allocation
Dengue Prediction
Dengue -- Epidemiology.
Dengue -- Prevention.
spellingShingle Dengue Mitigation
Computational Framework
Regression Ensemble
Resource Allocation
Dengue Prediction
Dengue -- Epidemiology.
Dengue -- Prevention.
Dinayadura, Nirosha
An Efficient Approach for Dengue Mitigation: A Computational Framework
description Dengue mitigation is a major research area among scientist who are working towards an effective management of the dengue epidemic. An effective dengue mitigation requires several other important components. These components include an accurate epidemic modeling, an efficient epidemic prediction, and an efficient resource allocation for controlling of the spread of the dengue disease. Past studies assumed homogeneous response pattern of the dengue epidemic to climate conditions throughout the regions. The dengue epidemic is climate dependent and also it is geographically dependent. A global model is not sufficient to capture the local variations of the epidemic. We propose a novel method of epidemic modeling considering local variation and that uses micro ensemble of regressors for each region. There are three regressors that are used in the construction of the ensemble. These are support vector regression, ordinary least square regression, and a k-nearest neighbor regression. The best performing regressors get selected into the ensemble. The proposed ensemble determines the risk of dengue epidemic in each region in advance. The risk is then used in risk-based resource allocation. The proposing resource allocation is built based on the genetic algorithm. The algorithm exploits the genetic algorithm with major modifications to its main components, mutation and crossover. The proposed resource allocation converges faster than the standard genetic algorithm and also produces a better allocation compared to the standard algorithm.
author2 Bryce, Renee
author_facet Bryce, Renee
Dinayadura, Nirosha
author Dinayadura, Nirosha
author_sort Dinayadura, Nirosha
title An Efficient Approach for Dengue Mitigation: A Computational Framework
title_short An Efficient Approach for Dengue Mitigation: A Computational Framework
title_full An Efficient Approach for Dengue Mitigation: A Computational Framework
title_fullStr An Efficient Approach for Dengue Mitigation: A Computational Framework
title_full_unstemmed An Efficient Approach for Dengue Mitigation: A Computational Framework
title_sort efficient approach for dengue mitigation: a computational framework
publisher University of North Texas
publishDate 2019
url https://digital.library.unt.edu/ark:/67531/metadc1505177/
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