In recent years, epidemic policy-making models are increasingly being used to
provide reference for governors on prevention and control policies against
catastrophic epidemics such as SARS, H1N1 and COVID-19. Existing studies are
currently constrained by two issues: First, previous methods develop policies
based on effect evaluation, since few of factors in real-world decision-making
can be modeled, the output policies will then easily become extreme. Second,
the subjectivity and cognitive limitation of human make the historical policies
not always optimal for the training of decision models. To these ends, we
present a novel Policy Combination Synthesis (PCS) model for epidemic
policy-making. Specially, to prevent extreme decisions, we introduce
adversarial learning between the model-made policies and the real policies to
force the output policies to be more human-liked. On the other hand, to
minimize the impact of sub-optimal historical policies, we employ contrastive
learning to let the model draw on experience from the best historical policies
under similar scenarios. Both adversarial and contrastive learning are adaptive
based on the comprehensive effects of real policies to ensure the model always
learns useful information. Extensive experiments on real-world data prove the
effectiveness of the proposed model.

我们提出了一种新颖的疫情政策制定模型，采用对抗学习和对比学习的方法，以确保模型始终学习到有用的信息，并在真实世界数据上进行了广泛的实验证明了该模型的有效性。