Department of Geosciences, Auburn University, 2050 Beard Eaves Coliseum, Auburn, AL 36849, USA.
Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, 301 E. Dean Keeton St., Stop C1786, Austin, TX 78712, USA.
Spat Spatiotemporal Epidemiol. 2022 Aug;42:100521. doi: 10.1016/j.sste.2022.100521. Epub 2022 May 28.
Severe acute respiratory syndrome - coronavirus 2 (SARS-CoV-2) continues to effect communities across the world. One way to combat these effects is to enhance our collective ability to remotely monitor community spread. Monitoring SARS-CoV-2 in wastewater is one approach that enables researchers to estimate the total number of infected people in a region; however, estimates are often made at the sewershed level which may mask the geographic nuance required for targeted interdiction efforts. In this work, we utilize an apportioning method to compare the spatial and temporal trends of daily case count with the temporal pattern of viral load in the wastewater at smaller units of analysis within Austin, TX. We find different lag-times between wastewater loading and case reports. Daily case reports for some locations follow the temporal trend of viral load more closely than others. These findings are then compared to socio-demographic characteristics across the study area.
严重急性呼吸系统综合症-冠状病毒 2(SARS-CoV-2)继续对全球各地的社区产生影响。一种对抗这些影响的方法是增强我们远程监测社区传播的集体能力。监测废水中的 SARS-CoV-2 是一种使研究人员能够估计一个地区受感染人数的方法;然而,估计通常是在污水流域层面进行的,这可能掩盖了针对目标干预措施所需的地理细微差别。在这项工作中,我们利用一种分配方法来比较德克萨斯州奥斯汀市较小分析单位内每日病例数与废水中病毒载量的时空趋势。我们发现废水负荷和病例报告之间的滞后时间不同。一些地点的每日病例报告比其他地点更接近病毒载量的时间趋势。然后将这些发现与研究区域的社会人口统计学特征进行比较。