Dynamical modeling of measles epidemics using a networked metapopulation approach

Date of Award

2019

Document Type

Thesis

Degree Name

Master of Science in Computer Science, Straight

Department

Information Systems & Computer Science

First Advisor

Estuar, Ma. Regina Justina E., Ph.D.

Abstract

The burden of measles continues to afflict many developing countries, where medical resources are limited and communicable diseases can develop rapidly within a population. The ability to model and forecast epidemic transmission within and between communities given levels of vaccination can lead to better disease surveillance and public health response. This study implemented hybrid networked metapopulation models for measles spreading and introduced methods for segmenting historical incidence, estimating disease parameters, and approximating inter-subpopulation interactions between individuals. The flux movement of individuals can be approximated using the ideal flow of a transportation network. Results show that hybrid models that incorporate estimations of human movement can be used as alternative implementations for classical compartmental models. Geographical interpolation also suggest a relationship between measles incidence and the presence of roads and highways. Analysis also reveal that the rate of transmission and recovery from measles influence the spreading of the virus the most.

Comments

The C7.M65 2019

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