Authors
Title
Estimating Dynamic Networks Using A Hidden Markov Model
Abstract
Human life and diseases are inseparable. Diseases can be caused by our own bodies as they age and degenerate or by infectious pathogens. Our study is about infectious diseases, such as flu or sexually transmitted diseases. The prediction of the spread of a disease is paramount to establish intervention methods or procedures to curb an epidemic. There are three key parameters in modeling of epidemic diseases:
SIR model : Susceptible, Infected, and Recovered SocialContactnetwork, representing person-to-person contact: static or dynamic Genome sequenced data of infected hostWe have developed theoretical approaches that can take into account dynamic networks and, independently, that can use genomic data of the pathogen, sampled from infected individuals, to reconstruct the path of an epidemic. By considering the location and time of the sampled pathogen sequence data we can combine the sampled infection network and the mutational history of the pathogen to reconstruct a more accurate contact network. We can reconstruct this dynamic contact networks using genetic data and epidemic parameters via a Hidden Markov Model: HMM
