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We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes and stochastic optimal control of stochastic differential equations (SDEs) with jumps. In contrast to previous work, this novel perspective is particularly well-suited to make use of fine-grained data about disease outbreaks and lets us overcome the shortcomings of current control strategies. Our control strategy resorts to treatment intensities to determine who to treat and when to do so to minimize the amount of infected individuals over time. Preliminary experiments with synthetic data show that our control strategy consistently outperforms several alternatives. Looking into the future, we believe our methodology provides a promising step towards the development of practical data-driven control strategies of epidemic processes.
Stochastic optimal control of epidemic processes in networks L. Lorch, A. De, S. Bhatt, W. Trouleau, U. Upadhyay, M. Gomez-RodriguezIn NeurIPS 2018 Workshop on Machine Learning for Health (ML4H), 2018
Bibtex Entry:
	author = {Lorch, Lars and De, Abir and Bhatt, Samir and Trouleau, William and Upadhyay, Utkarsh and Gomez-Rodriguez, Manuel},
	journal = {NeurIPS 2018 Workshop on Machine Learning for Health (ML4H)},
	title = {Stochastic optimal control of epidemic processes in networks},
	year = {2018}}