用可解释的AI技术研究COVID-19扩展因子
An Investigation of COVID-19 Spreading Factors with Explainable AI Techniques
摘要：
自2019年12月首次发现COVID-19以来，世界各地已实施各种公共卫生干预措施。由于不同国家在不同时间实施的措施不同，我们使用2020年1月22日至2020年4月2日的数据，对18个国家和地区实施的措施的相对有效性进行了评估。我们计算出在此期间对所研究的国家和地区最有效的前一项和前两项措施。在我们的研究中使用了两种可解释的人工智能技术SHAP和ECPI;因此，我们构建了（机器学习）模型来预测瞬时繁殖数（$R_t$），并将这些模型用作现实世界的替代品，并将对我们模型影响最大的输入视为最有效的措施。全面的城市封锁和接触者追踪是两个最有效的措施。为了确保$R_t<1$，公众佩戴口罩也很重要。大规模测试本身并不是最有效的措施，尽管与其他措施结合使用时，它可能是有效的。温暖的温度有助于减少传播。
英文摘要：
Since COVID-19 was first identified in December 2019, various public health interventions have been implemented across the world. As different measures are implemented at different countries at different times, we conduct an assessment of the relative effectiveness of the measures implemented in 18 countries and regions using data from 22/01/2020 to 02/04/2020. We compute the top one and two measures that are most effective for the countries and regions studied during the period. Two Explainable AI techniques, SHAP and ECPI, are used in our study; such that we construct (machine learning) models for predicting the instantaneous reproduction number ($R_t$) and use the models as surrogates to the real world and inputs that the greatest influence to our models are seen as measures that are most effective. Across-the-board, city lockdown and contact tracing are the two most effective measures. For ensuring $R_t<1$, public wearing face masks is also important. Mass testing alone is not the most effective measure although when paired with other measures, it can be effective. Warm temperature helps for reducing the transmission.
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。
