Dynamical Modeling of Measles Epidemics Using a Networked Metapopulation Approach
Date of Award
Master of Science in Chemistry, Straight Program
Information Systems & Computer Science
Ma. Regina Justina E. Estuar, PhD
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 trans- mission within and between communities given levels of vaccination can lead to better disease surveillance and public health response. This study imple- mented hybrid networked metapopulation models for measles spreading and introduced methods for segmenting historical incidence, estimating disease pa- rameters, and approximating inter-subpopulation interactions between individ- uals. 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 re- lationship 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.
(2019). Dynamical Modeling of Measles Epidemics Using a Networked Metapopulation Approach. Ateneo de Manila University.