随机环境条件下慢性消耗性疾病的传播及其基于深度强化学习的控制
Spread of Chronic Wasting Disease under Stochastic Environmental Conditions and its Control using Deep Reinforcement Learning
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中文总结 AI 辅助
本研究构建随机易感-感染-环境模型，利用深度强化学习评估CWD控制策略，发现组合策略最优，且环境去污染是关键手段。
中文摘要 AI 辅助
慢性消耗性疾病（CWD）是一种致命的朊病毒病，影响鹿、麋鹿、驼鹿、驯鹿、麂及其他鹿科动物。由于自由放养的鹿科动物种群面临环境变异性和随机性，确定性模型可能遗漏重要的动态特征，如随机性消退。我们开发了一个随机易感-感染-环境模型，使用带反射的微分方程以确保易感类保持非负。我们考察了随着CWD压力和防控措施增加，环境变异性对鹿科动物种群的影响。对于确定性模型，我们推导了基本再生数，其为直接贡献与环境贡献之和，表明地方性流行阶段在R0=1时出现。对于随机系统，我们建立了局部适定性、正性及无病律。入侵的顶部Lyapunov指数不受反射影响。我们使用在混合动作空间中通过近端策略优化训练的深度强化学习智能体评估CWD缓解策略，比较了狩猎、去污染及组合策略。在确定性情形下，仅狩猎可控制疾病，但使种群减少约58%，而去污染需要持续努力。组合策略使鹿科动物种群数量增加一倍以上，并几乎消除感染和污染。在随机情形下，该策略在约80%的运行中控制住疾病，10%出现大规模暴发；有效性随噪声增加而降低。在所有场景中，智能体始终强调环境去污染这一关键控制方法。
英文摘要
Chronic wasting disease (CWD) is a fatal prion disease affecting deer, elk, moose, reindeer, muntjac, and other cervids. Because free-ranging cervid populations face environmental variability and randomness, deterministic models may miss important dynamics like stochastic fade-out. We develop a stochastic Susceptible-Infectious-Environmental model using differential equations with reflection to ensure the susceptible class remains non-negative. We examine how environmental variability influences cervid populations as CWD pressure and control measures increase. For the deterministic model, we derive the basic reproduction number as the sum of direct and environmental contributions, showing the endemic phase arises at R0=1. For the stochastic system, we establish local well-posedness, positivity, and the disease-free law. The top Lyapunov exponent for invasion remains unaffected by reflection. We evaluate CWD mitigation using a deep reinforcement learning agent trained with Proximal Policy Optimization in a hybrid action space, comparing hunting, decontamination, and combined strategies. In the deterministic case, hunting alone can control the disease but reduces the population by about 58%, while decontamination requires sustained effort. The combined policy more than doubles the cervid population and nearly eliminates infection and contamination. In the stochastic case, the policy contains the disease in about 80% of runs, with 10% experiencing large outbreaks; effectiveness decreases as noise increases. Across all scenarios, the agent consistently emphasizes environmental decontamination, the key control method.
