Identification and characterization of super-spreaders from voluminous epidemiology data
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 ...
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