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Mathematical models are a helpful tool for testing assumptions and elucidating the quantitative implications of disease features.
The eradication of diseases has long been a focus of the global health community and researchers in epidemiology and other related fields.
Decision makers need efficient algorithms to draw meaningful conclusions from detailed stochastic simulations with respect to a goal-oriented objective.
Understanding the environmental conditions of disease transmission is important in the study of vector-borne diseases.
This work develops compressive sampling strategies for computing the dynamic mode decomposition (DMD) from heavily subs