Tracing Infectious Diseases using Genetic and Spatial Data
نویسندگان
چکیده
The analysis of viral genetic sequence data collected during disease outbreaks has emerged as a promising new tool for understanding infectious disease dynamics and designing control measures against infectious diseases. Hence, there is a need for statistical methodologies that effectively integrate genetic data with other epidemiological data to perform inference on the underlying disease dynamics. In this project, we develop a Markov chain Monte Carlo framework which incorporates genetic, temporal and spatial data into a single framework that infers who infected whom among a group of patients, as well as various diseaserelated parameters, while allowing for missing data. Using simulations, we show that our algorithm determines who infected whom more accurately than existing methods, and has the additional benefit of jointly inferring unknown parameters such as the disease mutation rate, transmission rate, and contact network related parameters. We apply our approach to analyze 433 H1N1 viral genetic sequences drawn from the early stages of the 2009 H1N1 influenza pandemic, and to estimate the basic reproductive number of the pandemic. We then extend our framework: firstly, we investigate how best to incorporate spatial data by comparing several ways of measuring the distance between two locations: namely, geographical distance, travel time and route length. Secondly, we extend the disease transmission model to allow for mixtures of different transmission routes, for example, allowing diseases to travel along an air traffic network as well as over land, and extend our inference algorithm to this setting.
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تاریخ انتشار 2014