Rifat Anwar

Date of Graduation

2017

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Civil and Environmental Engineering

Committee Chair

Antar Jutla

Committee Co-Chair

Radhey S Sharma

Committee Member

Lian-Shin Lin

Abstract

Dengue is the most significant arthropod-borne virus in terms of human morbidity and mortality. Geographic expansion of dengue and intensity of outbreak has amplified significantly during the last few decades. Thus, the understanding of the dynamic of the large outbreaks has become indispensable for planning of control interventions in future epidemics. In this regard, local entomological, meteorological and epidemiological parameters based dengue models can be an essential tool for better interpretation of dengue-climate relationship at a regional scale. Process based modelling is resourceful in combining the vector and host dynamic along with the response to the meteorological factors for dengue transmission. In previous studies, process based models have not dealt with the integrated impact of vector-host dynamic and dengue transmission epidemiology by incorporating weather dependent transmission mechanism. In this study, a process-based model has been developed and validated for Iquitos of Peru, based on both vector and host population dynamic as well as the whole infection transmission mechanism. The sole objective was to develop a simple model to represent the actual scenario triggering dengue epidemic considering the most important features of vector population dynamics, transmission mechanism and environmental linkages. The model has used remote sensing or satellite based environmental data and also introduced dew point temperature as a new and effective weather parameter to depict the transmission process of dengue. The model has been capable of simulating the peak and moderate scenario in temporal scale, with considerable quantification of the actual number of cases for the 2004 and 2008 epidemics. Eventually, this type of model can be modified to use for different regions to predict the peak scenario based on local weather parameters effecting the infection transmission and vector development process along with population density.

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