Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models
This work addresses the challenge of managing public health crises by optimizing NPI policies to balance disease control and socio-economic impacts, but it appears incremental as it builds on existing epidemiological models with a new optimization strategy.
The paper tackles the problem of balancing prevention and mitigation strategies in public health crises by proposing a new approach for optimal policy recommendations using epidemiological models and a look-ahead reward optimization strategy to choose non-pharmaceutical interventions (NPIs) at different epidemic stages, resulting in non-trivial policies that adhere well to specified constraints as evaluated on SEIR and EpiCast models.
A crucial aspect of managing a public health crisis is to effectively balance prevention and mitigation strategies, while taking their socio-economic impact into account. In particular, determining the influence of different non-pharmaceutical interventions (NPIs) on the effective use of public resources is an important problem, given the uncertainties on when a vaccine will be made available. In this paper, we propose a new approach for obtaining optimal policy recommendations based on epidemiological models, which can characterize the disease progression under different interventions, and a look-ahead reward optimization strategy to choose the suitable NPI at different stages of an epidemic. Given the time delay inherent in any epidemiological model and the exponential nature especially of an unmanaged epidemic, we find that such a look-ahead strategy infers non-trivial policies that adhere well to the constraints specified. Using two different epidemiological models, namely SEIR and EpiCast, we evaluate the proposed algorithm to determine the optimal NPI policy, under a constraint on the number of daily new cases and the primary reward being the absence of restrictions.