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Preprint ARTICLE | doi:10.20944/preprints202409.2376.v1

Graph Neural Network for Daily Supply Chain Problems

Fabian Raul Gonzalez

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: supply chain; graph neural network; machine learning

Online: 30 September 2024 (06:07:57 CEST)

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Preprint REVIEW | doi:10.20944/preprints202104.0481.v1

Minimizing Errors in RT-PCR Detection and Quantification of SARS-CoV-2 RNA for Wastewater Surveillance

Warish Ahmed, Stuart Simpson, Paul Bertsch, Kyle Bibby, Aaron Bivins, Linda Blackall, Silvia Bofill-Mas, Albert Bosch, Joao Brandao, Phil Choi, Mark Ciesielski, Erica Donner, Nishita D'Souza, Andreas Farnleitner, Daniel Gerrity, Raul Gonzalez, John Griffith, Pradip Gyawali, Charles Haas, Kerry Hamilton, Chanditha Hapuarachchi, Valerie Harwood, Rehnuma Haque, Greg Jackson, Stuart Khan, Wesaal Khan, Masaaki Kitajima, Asja Korajkic, Giuseppina La Rosa, Blythe Layton, Erin Lipp, Sandra McLellan, Brian McMinn, Gertjan Medema, Suzanne Metcalfe, Wim Meijer, Jochen Mueller, Heather Murphy, Colleen Naughton, Rachel Noble, Sudhi Payyappat, Susan Petterson, Tarja Pitkanen, Veronica Rajal, Brandon Reyneke, Fernando Roman, Joan Rose, Marta Rusinol, Michael Sadowsky, Laura Sala-Comorera, Yin Xiang Setoh, Samendra Sherchan, Kwanrawee Sirikanchana, Wendy Smith, Joshua Steele, Rosalie Sabburg, Erin Symonds, Phong Thai, Kevin Thomas, Josh Tynan, Simon Toze, Janelle Thompson, Andy Whiteley, Judith Wong, Daisuke Sano, Stefan Wuertz, Irene Xagoraraki, Qian Zhang, Amity Zimmer-Faust, Orin Shanks

Subject: Environmental And Earth Sciences, Waste Management And Disposal Keywords: COVID-19; SARS-CoV-2; Wastewater; Surveillance; False-positive; False-negative; RT-PCR

Online: 19 April 2021 (13:08:13 CEST)

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Wastewater surveillance for pathogens using the reverse transcription-polymerase chain reaction (RT-PCR) is an effective, resource-efficient tool for gathering additional community-level public health information, including the incidence and/or prevalence and trends of coronavirus disease-19 (COVID-19). Surveillance of SARS-CoV-2 in wastewater may provide an early-warning signal of COVID-19 infections in a community. The capacity of the world’s environmental microbiology and virology laboratories for SARS-CoV-2 RNA characterization in wastewater is rapidly increasing. However, there are no standardized protocols nor harmonized quality assurance and quality control (QA/QC) procedures for SARS-CoV-2 wastewater surveillance. This paper is a technical review of factors that can lead to false-positive and -negative errors in the surveillance of SARS-CoV-2, culminating in recommendations and strategies that can be implemented to identify and mitigate these errors. Recommendations include, stringent QA/QC measures, representative sampling approaches, effective virus concentration and efficient RNA extraction, amplification inhibition assessment, inclusion of sample processing controls, and considerations for RT-PCR assay selection and data interpretation. Clear data interpretation guidelines (e.g., determination of positive and negative samples) are critical, particularly during a low incidence of SARS-CoV-2 in wastewater. Corrective and confirmatory actions must be in place for inconclusive and/or potentially significant results (e.g., initial onset or reemergence of COVID-19 in a community). It will also be prudent to perform inter-laboratory comparisons to ensure results are reliable and interpretable for ongoing and retrospective analyses. The strategies that are recommended in this review aim to improve SARS-CoV-2 characterization for wastewater surveillance applications. A silver lining of the COVID-19 pandemic is that the efficacy of wastewater surveillance was demonstrated during this global crisis. In the future, wastewater will play an important role in the surveillance of a range of other communicable diseases.

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