Quantifying the Immediate Effects of the COVID-19 Pandemic on Scientists
Kyle R. Myers, Wei Yang Tham, Yian Yin, Nina Cohodes, Jerry G. Thursby, Marie C. Thursby, Peter E. Schiffer, Joseph T. Walsh, Karim R. Lakhani, Dashun Wang
The COVID-19 pandemic has undoubtedly disrupted the scientific enterprise, but we lack empirical evidence on the nature and magnitude of these disruptions. Here we report the results of a survey of approximately 4,500 Principal Investigators (PIs) at U.S.- and Europe-based research institutions. Distributed in mid-April 2020, the survey solicited information about how scientists' work changed from the onset of the pandemic, how their research output might be affected in the near future, and a wide range of individuals' characteristics. Scientists report a sharp decline in time spent on research on average, but there is substantial heterogeneity with a significant share reporting no change or even increases. Some of this heterogeneity is due to field-specific differences, with laboratory-based fields being the most negatively affected, and some is due to gender, with female scientists reporting larger declines. However, among the individuals' characteristics examined, the largest disruptions are connected to a usually unobserved dimension: childcare. Reporting a young dependent is associated with declines similar in magnitude to those reported by the laboratory-based fields and can account for a significant fraction of gender differences. Amidst scarce evidence about the role of parenting in scientists' work, these results highlight the fundamental and heterogeneous ways this pandemic is affecting the scientific workforce, and may have broad relevance for shaping responses to the pandemic's effect on science and beyond.
A fractional-order compartmental model for predicting the spread of the Covid-19 pandemic
我们提出了一个时间-部分房室模型（SEI $_A$ I $_S$ HRD），包括Covid-19大流行的易感人群、暴露人群、感染人群（无症状和有症状）、住院人群、康复人群和死亡人群。我们研究所提出的模型的属性和动力学。讨论了无病平衡点和地方病平衡点渐进稳定的条件。此外，我们研究了参数的敏感性，并使用田纳西州的数据（作为案例研究），讨论了模型参数的可辨识性。模型中的非负参数是通过求解反问题获得的经验数据来自加利福尼亚州，佛罗里达州，佐治亚州，马里兰州，田纳西州，得克萨斯州，华盛顿州和威斯康星州。基本繁殖数略高于1的临界值，这表明需要采取更严格的措施，如使用口罩，社交距离，接触者追踪，甚至更长的居家命令，以减轻病毒的传播。随着其中一些州的居家令被取消，我们看到病例数量几乎立即开始增加，除非采取更严格的措施，否则可能会持续上升到2020年底。
City-scale dark fiber DAS measurements of infrastructure use during the COVID-19 pandemic
在最近的COVID-19大流行期间，世界各地的政府官员命令公民在家中隔离，实时测量道路、医院、杂货店和其他公共基础设施的使用情况对于准确预测病毒感染率和为未来的政府决策提供信息至关重要。虽然移动电话的位置提供了一些关于社区活动的信息，但对整个城市的地面运动进行密集的分布式地球物理感测更完整，也更具有匿名性。在本文中，我们演示了连接到加利福尼亚州帕洛阿尔托下方电信电缆的光纤分布式声学传感（DAS）如何在COVID-19大流行爆发和随后的隔离期间捕获车辆产生的地震和大地测量信号。我们利用DAS应变测量的路基变形所造成的当地汽车和卡车在自动模板匹配检测算法来计算每天行驶的车辆数量超过两个月的时间周围的旧金山湾区避难所的地方秩序。使用Sand Hill Road上一家大型杂货店附近的一段光纤，我们发现订单后车辆数量立即减少了50%，但斯坦福医院附近的数据显示，由于正在进行的医院活动，变化要微妙得多。我们将DAS测量的信息与其他检疫响应指标进行比较，发现与Google和Apple使用手机数据报告的相对变化存在很强的相关性。
Universal Masking is Urgent in the COVID-19 Pandemic: SEIR and Agent Based Models, Empirical Validation, Policy Recommendations
HKUST (University of Science & Technology)（香港科技大学）；International Computer Science Institute（国际计算机科学研究所）；Ecole de Guerre Economique（经济战争学院）；University of Cambridge（剑桥大学）；Manifold Research；University College London（伦敦大学学院）；ELU AI Ltd（ELU AI有限公司）；Royal Free Hospital（皇家自由医院）；Population Research Institute, The Family Federation of Finland（芬兰家庭联合会人口研究所）
