An integrated framework for early detection and transmissibility assessment of emerging variants in wastewater

Tracking the emergence of new SARS-CoV-2 variants is important for a comprehensive understanding of the pandemic’s progression. However, it remains challenging due to the low variant prevalence in the early stage of an outbreak. Here, we present an integrated framework that combines three key components: early variant detection in wastewater, validation through clinical genome sequencing, and transmissibility assessment using mathematical modeling. Using the SARS-CoV-2 Omicron variant as a proof of concept, we developed a novel nested allele-specific RT-qPCR assay (NAS-PCR) for wastewater surveillance. Our framework detected Omicron in Greater Boston wastewater samples starting from September 2021, over two months before the first U.S. clinical case. We validated these findings by analyzing GISAID clinical sequence data, which revealed 172 previously unreported Omicron genomes predating its official identification in South Africa. To assess transmissibility, we developed a Susceptible-Infected-Viral load model using quantified wastewater concentrations, which estimated Omicron’s basic reproduction number (R0) between 2.36 and 3.09, showing robust consistency across varying population sizes, data points, and viral shedding rates. This integrated approach unifies molecular diagnostics, wastewater epidemiology, and mathematical modeling for comprehensive variant surveillance. Our framework provides a systematic solution for early warning and risk assessment of emerging variants, which can strengthen public health preparedness for future viral threats.Competing Interest StatementEric Alm is scientific advisor and shareholder of BioBot Analytics. The other authors declare no competing interest.Funding StatementThis work is supported by the National Science Foundation (DMS-2421257) and UT system Rising STARs award. T.P. is supported by Director's postdoctoral fellowship at Los Alamos National Laboratory. This work was also supported by the MIT Center for Microbiome Informatics and Therapeutics to EJA, the National Research Foundation, Prime Minister's Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) program funding to the Singapore-MIT Alliance for Research and Technology (SMART) Antimicrobial Resistance Interdisciplinary Research Group (AMR IRG), the Intra-CREATE Thematic Grant (Cities) grant NRF2019-THE001-0003a to JT and EJA and funding from the Singapore Ministry of Education and National Research Foundation through an RCE award to Singapore Centre for Environmental Life Sciences Engineering (SCELSE).Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present work are contained in the manuscript