论文标题
通过可解释的AI技术对COVID-19的扩散因子的调查
An Investigation of COVID-19 Spreading Factors with Explainable AI Techniques
论文作者
论文摘要
自从2019年12月首次确定Covid-19以来，全球已实施了各种公共卫生干预措施。由于不同时间在不同国家实施了不同的措施，因此我们使用从2020年1月22日至02/04/2020的数据进行评估了18个国家和地区实施的措施的相对有效性。我们计算了对此期间研究的国家和地区最有效的第一和两个措施。我们的研究使用了两种可解释的AI技术，即Shap和ECPI。因此，我们构建了（机器学习）模型，以预测瞬时繁殖数（$ r_T $），并将这些模型用作对现实世界的代孕，并输入了对我们模型的最大影响力被视为最有效的措施。全面，城市锁定和接触跟踪是两种最有效的措施。为了确保$ r_t <1 $，公共戴口罩也很重要。仅质量测试并不是最有效的措施，尽管与其他措施配对时，它可能是有效的。温暖的温度有助于减少传输。
Since COVID-19 was first identified in December 2019, various public health interventions have been implemented across the world. As different measures are implemented at different countries at different times, we conduct an assessment of the relative effectiveness of the measures implemented in 18 countries and regions using data from 22/01/2020 to 02/04/2020. We compute the top one and two measures that are most effective for the countries and regions studied during the period. Two Explainable AI techniques, SHAP and ECPI, are used in our study; such that we construct (machine learning) models for predicting the instantaneous reproduction number ($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.
