论文标题
对大流行控制的经济影响的启发式评估
Heuristic assessment of the economic effects of pandemic control
论文作者
论文摘要
数据驱动的风险网络描述了在流行病学和生态学等领域引起的许多复杂系统动力学。他们缺乏明确的动力，并且具有多种成本来源，这两者都超出了传统控制理论的当前范围。我们通过将来自世界经济论坛的专家共识与风险激活数据相结合以定义其拓扑和互动来构建全球风险网络。这些风险中的许多风险（包括极端天气）在活跃时会产生巨大的经济成本。我们介绍了一种将网络交互数据转换为连续动态的方法，我们应用了最佳控制。我们为基于经验收集的数据构建和控制风险网络动态的第一种方法。我们确定政府通常使用的七种风险控制Covid-19-19，并表明存在许多替代驾驶员风险集，可能会降低控制成本。
Data-driven risk networks describe many complex system dynamics arising in fields such as epidemiology and ecology. They lack explicit dynamics and have multiple sources of cost, both of which are beyond the current scope of traditional control theory. We construct the global risk network by combining the consensus of experts from the World Economic Forum with risk activation data to define its topology and interactions. Many of these risks, including extreme weather, pose significant economic costs when active. We introduce a method for converting network interaction data into continuous dynamics to which we apply optimal control. We contribute the first method for constructing and controlling risk network dynamics based on empirically collected data. We identify seven risks commonly used by governments to control COVID-19 spread and show that many alternative driver risk sets exist with potentially lower cost of control.
