Identification and characterization of super-spreaders from voluminous epidemiology data

Abstract

Planning for large-scale epidemiological outbreaks often involves executing compute-intensive disease spread simulations. To capture the probabilities of various outcomes, these simulations are executed several times over a collection of representative input scenarios, producing voluminous data. The resulting datasets contain valuable insights, including sequences of events such as super-spreading events that lead to extreme outbreaks. However, discovering and leveraging such information is also computationally expensive. In this thesis, we propose a distributed approach for analyzing voluminous epidemiology data to locate and classify the super-spreaders in a disease network. Our methodology constructs analytical models using features extracted from the epidemiology data. The analytical models are amenable to interpretation and disease planners can use them to inform identification of super-spreaders that have a disproportionate effect on epidemiological outcomes, enabling effective allocation of limited resources such as vaccinations and field personnel.