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Game and Reference: Policy Combination Synthesis for Epidemic Prevention and Control

Zhiyi Tan, Bingkun Bao

TL;DR

To prevent extreme decisions, adversarial learning is introduced between the model-made policies and the real policies to force the output policies to be more human-liked and contrastive learning is employed to let the model draw on experience from the best historical policies under similar scenarios.

Abstract

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.

Game and Reference: Policy Combination Synthesis for Epidemic Prevention and Control

TL;DR

To prevent extreme decisions, adversarial learning is introduced between the model-made policies and the real policies to force the output policies to be more human-liked and contrastive learning is employed to let the model draw on experience from the best historical policies under similar scenarios.

Abstract

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.
Paper Structure (23 sections, 13 equations, 6 figures, 4 tables)

This paper contains 23 sections, 13 equations, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Policy-Making based on Epidemic Prediction
  4. Policy-Optimizing based on Effect Evaluation
  5. Method
  6. Problem Statement
  7. Overall Framework
  8. Policy Generator
  9. Adaptive Multi-Task Learning Network
  10. Multi-Objective Policy Evaluator
  11. The II-SEIR epidemic prediction module
  12. Adversarial Module
  13. Contrast Module
  14. Multi-Task Training
  15. Experiments
  16. ...and 8 more sections

Figures (6)

  • Figure 1: The framework of PCS model
  • Figure 2: Comparison of SEIR and the proposed II-SEIR
  • Figure 3: Epidemic containment effect comparison of single policy
  • Figure 5: Comparison of PCS-made policies and real policies
  • Figure 6: Comparison of output policies with and without adversarial module
  • ...and 1 more figures