论文标题
用于评估大流行风险的机器推理：西奥多·罗斯福号号案件
Machine Reasoning to Assess Pandemics Risks: Case of USS Theodore Roosevelt
论文作者
论文摘要
评估大流行对社区和工作场所的风险需要智能决策支持系统（DSS）。此类DSS的核心必须基于机器推理技术，例如推理，并且应能够估算决策中的风险和偏见。在本文中，我们使用因果网络来对Covid-19数据进行贝叶斯推断，特别是评估诸如感染率和其他预防指标之类的风险。与其他统计模型不同，贝叶斯因果网络通过联合分布结合了各种数据来源，并且更好地反映了可用数据的不确定性。我们提供了一个示例，使用2020年初西奥多·罗斯福（Theodore Roosevelt）船上发生的Covid-19爆发案例。
Assessment of risks of pandemics to communities and workplaces requires an intelligent decision support system (DSS). The core of such DSS must be based on machine reasoning techniques such as inference and shall be capable of estimating risks and biases in decision making. In this paper, we use a causal network to make Bayesian inference on COVID-19 data, in particular, assess risks such as infection rate and other precaution indicators. Unlike other statistical models, a Bayesian causal network combines various sources of data through joint distribution, and better reflects the uncertainty of the available data. We provide an example using the case of the COVID-19 outbreak that happened on board of USS Theodore Roosevelt in early 2020.
