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ABSTRACT Municipal wastewater harbors diverse RNA viruses, which are responsible for many emerging and reemerging diseases in humans, animals, and plants. Although genomic sequencing can be a high-throughput approach for profiling the RNA virome in wastewater, wastewater processing methods often influence sequencing outcomes. Here, we systematically evaluated two wastewater processing methods, tangential-flow ultrafiltration (TFF) and Nanotrap Microbiome A Particles, for detecting the target RNA virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via amplicon sequencing and characterizing the RNA virome using whole-transcriptome shotgun sequencing. Our results from paired comparison tests showed that the TFF and Nanotrap methods recovered similar SARS-CoV-2 variants at the lineage level (analysis of similarity [ANOSIM]R= −0.012,P= 0.874). Optimizing automated procedures for the Nanotrap method and concentration factors for the TFF method was critical for achieving high-depth and high-breadth coverage of the target virus genome. Notably, the two methods enriched distinct RNA viromes from the same wastewater samples (ANOSIMR= 0.260,P= 0.002), with TFF samples showing 22-fold and 7-fold higher relative abundances ofReoviridaeandCoronaviridae, respectively. These differences are likely due to the distinct virus concentration mechanisms employed by each method, which are influenced by liquid-solid partitioning of virus particles and interactions of viral surface proteins with ligands. Our findings underscore the importance of optimizing wastewater processing methods for genomic monitoring and have implications for broader environmental applications.IMPORTANCEWastewater genomic sequencing is an emerging technology for tracking viral infections within communities. However, different methods for concentrating viruses and extracting nucleic acids can influence the recoveries of RNA virome from wastewater. An in-depth understanding of virus concentration mechanisms and their impact on sequencing data quality and bioinformatic output would be critical to guide method selection and optimization. Specifically, this study systematically evaluated tangential-flow ultrafiltration and Nanotrap microbiome particles for their application to sequence SARS-CoV-2 and whole RNA virome from wastewater. Both methods yielded high-quality sequencing data for amplicon sequencing of SARS-CoV-2, but their outcomes diverged in the recovered RNA virome. We identified RNA viruses that are preferentially recovered by each of these two methods and proposed considerations of method selection for future studies of wastewater RNA virome. 

In the face of the unprecedented COVID-19 pandemic, various government-led initiatives and individual actions (e.g., lockdowns, social distancing, and masking) have resulted in diverse pandemic experiences. This study aims to explore these varied experiences to inform more proactive responses for future public health crises. Employing a novel “big-thick” data approach, we analyze and compare key pandemic-related topics that have been disseminatedtothe public through newspapers with those collectedfromthe public via interviews. Specifically, we utilized 82,533 U.S. newspaper articles from January 2020 to December 2021 and supplemented this “big” dataset with “thick” data from interviews and focus groups for topic modeling. Identified key topics were contextualized, compared and visualized at different scales to reveal areas of convergence and divergence. We found seven key topics from the “big” newspaper dataset, providing a macro-level view that covers public health, policies and economics. Conversely, three divergent topics were derived from the “thick” interview data, offering a micro-level view that focuses more on individuals’ experiences, emotions and concerns. A notable finding is the public’s concern about the reliability of news information, suggesting the need for further investigation on the impacts of mass media in shaping the public’s perception and behavior. Overall, by exploring the convergence and divergence in identified topics, our study offers new insights into the complex impacts of the pandemic and enhances our understanding of key issues both disseminated to and resonating with the public, paving the way for further health communication and policy-making. 

Within the geo-simulation research domain, micro-simulation and agent-based modeling often require the creation of synthetic populations. Creating such data is a time-consuming task and often lacks social networks, which are crucial for studying human interactions (e.g., disease spread, disaster response) while at the same time impacting decision-making. We address these challenges by introducing a Python based method that uses the open data including that from 2020 U.S. Census data to generate a large-scale realistic geographically explicit synthetic population for America’s 50 states and Washington D.C. along with the stylized social networks (e.g., home, work and schools). The resulting synthetic population can be utilized within various geo-simulation approaches (e.g., agent-based modeling), exploring the emergence of complex phenomena through human interactions and further fostering the study of urban digital twins. 

The COVID-19 pandemic has prompted an unprecedented global effort to understand and mitigate the spread of the SARS-CoV-2 virus. In this study, we present a comprehensive analysis of COVID-19 in Western New York (WNY), integrating individual patient-level genomic sequencing data with a spatially informed agent-based disease Susceptible-Exposed-Infectious-Recovered (SEIR) computational model. The integration of genomic and spatial data enables a multi-faceted exploration of the factors influencing the transmission patterns of COVID-19, including genetic variations in the viral genomes, population density, and movement dynamics in New York State (NYS). Our genomic analyses provide insights into the genetic heterogeneity of SARS-CoV-2 within a single lineage, at region-specific resolutions, while our population analyses provide models for SARS-CoV-2 lineage transmission. Together, our findings shed light on localized dynamics of the pandemic, revealing potential cross-county transmission networks. This interdisciplinary approach, bridging genomics and spatial modeling, contributes to a more comprehensive understanding of COVID-19 dynamics. The results of this study have implications for future public health strategies, including guiding targeted interventions and resource allocations to control the spread of similar viruses. 

Over the last two decades, there has been a growth in the applications of geographically-explicit agent-based models. One thing such models have in common is the creation of synthetic populations to initialize the artificial worlds in which the agents inhabit. One challenge such models face is that it is often difficult to create reusable geographically-explicit synthetic populations with social networks. In this paper, we introduce a Python based method that generates a reusable geographically-explicit synthetic population dataset along with its social networks. In addition, we present a pipeline for using the population datasets for model initialization. With this pipeline, multiple spatial and temporal scales of geographically-explicit agent-based models are presented focusing on Western New York. Such models not only demonstrate the utility of our synthetic population on commuting patterns but also how social networks can impact the simulation of disease spread and vaccination uptake. By doing so, this pipeline could benefit any modeler wishing to reuse synthetic populations with realistic geographic locations and social networks. 

The sequencing of human virus genomes from wastewater samples is an efficient method for tracking viral transmission and evolution at the community level. However, this requires the recovery of viral nucleic acids of high quality. We developed a reusable tangential-flow filtration system to concentrate and purify viruses from wastewater for genome sequencing. A pilot study was conducted with 94 wastewater samples from four local sewersheds, from which viral nucleic acids were extracted, and the whole genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was sequenced using the ARTIC V4.0 primers. Our method yielded a high probability (0.9) of recovering complete or near-complete SARS-CoV-2 genomes (>90% coverage at 10× depth) from wastewater when the COVID-19 incidence rate exceeded 33 cases per 100 000 people. The relative abundances of sequenced SARS-CoV-2 variants followed the trends observed from patient-derived samples. We also identified SARS-CoV-2 lineages in wastewater that were underrepresented or not present in the clinical whole-genome sequencing data. The developed tangential-flow filtration system can be easily adopted for the sequencing of other viruses in wastewater, particularly those at low concentrations.