用机器推理评估流行病风险：以USS西奥多·罗斯福为例
Machine Reasoning to Assess Pandemics Risks: Case of USS Theodore Roosevelt
摘要：
评估流行病对社区和工作场所的风险需要智能决策支持系统（DSS）。这种决策支持系统的核心必须基于机器推理技术，如推理，并应能够估计决策中的风险和偏见。在本文中，我们使用因果网络对COVID-19数据进行贝叶斯推断，特别是评估感染率和其他预防指标等风险。与其他统计模型不同，贝叶斯因果网络通过联合分布将各种数据源结合起来，更好地反映了可用数据的不确定性。我们以2020年初发生在美国西奥多·罗斯福号航空母舰上的COVID-19疫情为例。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
