Quantitative Trend Analysis of SARS-CoV-2 RNA in Municipal Wastewater Exemplified with Sewershed-Specific COVID-19 Clinical Case Counts

We demonstrate a new methodology for quantitative trend analysis (QTA) to analyze and interpret SARS-CoV-2 RNA wastewater surveillance results concurrently with clinical case data. This demonstration is based on the work completed under the Ontario (Canada) Wastewater Surveillance Initiative (WSI) by two laboratories in four wastewater treatment plants (WWTPs) at each of four large sewersheds, which were sampled over a 9-month period, along with sewershed-specific clinical case counts. The data from the last 5-months, representing a range of high and low case counts, was used for this demonstration. The QTA integrated clinical and wastewater virus signals, while combining recommendations from the United States Centers for Disease Control and Prevention (US CDC) and the Public Health Agency of Canada (PHAC). The key steps in the QTA consisted of signal normalization with pepper mild mottle virus (PMMoV), as a fecal biomarker, statistical linear break-point trend analysis and integration of both wastewater virus signal and clinical cases trend results. Using this approach, the wastewater virus and clinical cases trends, direction, and magnitude were clearly identified and provided a unified complementary tool to support public health decisions on a targeted, sewershed-specific basis.

Network Public Opinion Detection During the Coronavirus Pandemic: A Short-Text Relational Topic Model

Online social media provides rich and varied information reflecting the significant concerns of the public during the coronavirus pandemic. Analyzing what the public is concernedwith from social media information can support policy-makers to maintain the stability of the social economy and life of the society. In this article, we focus on the detection of the network public opinions during the coronavirus pandemic. We propose a novel Relational Topic Model for Short texts (RTMS) to draw opinion topics from social media data. RTMS exploits the feature of texts in online social media and the opinion propagation patterns among individuals. Moreover, a dynamic version of RTMS (DRTMS) is proposed to capture the evolution of public opinions. Our experiment is conducted on a real-world dataset which includes 67,592 comments from 14,992 users. The results demonstrate that, compared with the benchmark methods, the proposed RTMS and DRTMS models can detect meaningful public opinions by leveraging the feature of social media data. It can also effectively capture the evolution of public concerns during different phases of the coronavirus pandemic.