Fighting against the pandemic diseases with unique characters requires new sophisticated approaches like the artificial intelligence. This paper develops an artificial intelligence algorithm to produce multi-dimensional policies for controlling and minimizing the pandemic casualties under the limited pharmacological resources. In this respect, a comprehensive parametric model with a priority and age-specific vaccination policy and a variety of non-pharmacological policies are introduced. This parametric model is utilized for constructing an artificial intelligence algorithm by following the exact analogy of the model-based solution. Also, this parametric model is manipulated by the artificial intelligence algorithm to seek for the best multi-dimensional non-pharmacological policies that minimize the future pandemic casualties as desired. The role of the pharmacological and non-pharmacological policies on the uncertain future casualties are extensively addressed on the real data. It is shown that the developed artificial intelligence algorithm is able to produce efficient policies which satisfy the particular optimization targets such as focusing on minimization of the death casualties more than the infected casualties or considering the curfews on the people age over 65 rather than the other non-pharmacological policies. The paper finally analyses a variety of the mutant virus cases and the corresponding non-pharmacological policies aiming to reduce the morbidity and mortality rates.

中文翻译：

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药物、非药物政策与突变：基于人工智能的多维决策算法，用于控制大流行性疾病的伤亡


对抗具有独特特征的流行病需要人工智能等新的复杂方法。本文开发了一种人工智能算法，可以在有限的药理学资源下产生多维策略来控制和最大程度地减少大流行伤亡。在这方面，引入了具有优先级和特定年龄的疫苗接种政策以及各种非药物政策的综合参数模型。该参数模型用于通过遵循基于模型的解决方案的精确类比来构建人工智能算法。此外，该参数模型由人工智能算法操纵，以寻求最佳的多维非药物政策，从而根据需要最大限度地减少未来大流行的伤亡。药物和非药物政策对不确定的未来伤亡的作用在真实数据中得到了广泛的讨论。结果表明，所开发的人工智能算法能够产生满足特定优化目标的有效政策，例如更注重减少死亡人数而不是感染人数，或者考虑对 65 岁以上的人而不是其他非其他人实行宵禁。 -药理学政策。本文最后分析了多种变异病毒病例以及相应的旨在降低发病率和死亡率的非药物政策。