Abstract
Regional genomic surveillance is essential for tracking viral evolution and informing targeted public health responses. During the COVID-19 pandemic, we established a collaborative genomic surveillance pipeline for SARS-CoV-2 in Southeast Michigan to support national surveillance efforts and guide local pandemic response strategies. This work aims to present the methods and resources we have achieved during this effort and demonstrates the feasibility of such a collaboration. A partnership between Wayne State University (WSU), the Detroit Health Department (DHD), Henry Ford Health (HFH), the Wayne Health Mobile Unit (WHMU), and the Michigan Department of Health and Human Services (MDHHS) was established to collect, sequence, and analyze SARS-CoV-2 samples. Samples underwent automated nucleic acid extraction, RT-qPCR testing, and whole genomic sequencing at WSU’s integrative biosciences center (IBio). We analyzed consensus genome sequences using high-performance computing infrastructure for lineage assignment and variant identification. Between January 2022 and July 2024, we collected and archived 7508 samples, with 6235 (83.0%) successfully sequenced. A sub-analysis of 4637 HFH samples explored geographic distributions across 295 Michigan ZIP codes. Compared to the overall proportion of deaths among all people with SARS-CoV-2 positive tests in the sample (3.6% [95% CI (3.1, 4.2)]), the case-fatality rate was significantly increased with the 19 A + B [7.69%, 95% CI (5.03, 11.58)] and 20A (European 2 lineage: EU2) [9.65%, 95% CI (7.72, 11.99)] variants. The frequency distributions of variants showed a strong correlation (r = 0.98) with Michigan’s statewide data reported in GISAID. Omicron was the most prevalent variant detected (64% of cases). Our program demonstrated capacity for academic-public health partnerships to detect SARS-CoV-2 variant circulation in Southeast Michigan. This framework provides a replicable model for future pathogen surveillance programs to build on in response to infectious disease outbreaks.
Data availability
The datasets generated and/or analyzed during the current study are available in the National Library of Medicine repository, BioSample/NCBI (PRJNA1332824).
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Acknowledgements
This work was supported in part by the MI-SAPPHIRE project. Figure 1 was created using BioRender.com.
Funding
Funding was provided as part of the Michigan Sequencing and Academic Partnerships for Public Health Innovation and Response (MI-SAPPHIRE) initiative at the Michigan Department of Health and Human Services (MDHHS) which is supported with funds from the Centers for Disease Control and Prevention through the Epidemiology and Laboratory Capacity for Prevention and Control of Emerging Infectious Diseases Enhancing Detection Expansion (6NU50CK000510-02-07).
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RR: Supervision, Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing—original draft, review and editing, prepared Figs. 1, 5, S1, S8. XZ: Data curation, Formal analysis, Methodology, Software, Validation, Visualization, Writing-contributed to original draft, prepared Figs. S11–S13. SB: Data curation, Formal analysis, Software, Validation, Visualization, Writing-contributed to original draft, prepared Tables 1, 2, S1–S4 and Figs. 2–4, S2–S7, S9, S10. BW: Data curation, Investigation, Methodology, Resources, Writing-contributed to original draft. KG: Data curation, Investigation, Methodology, Resources, Writing—contributed to original draft. NV: Data curation, Investigation, Methodology. PS: Data curation AL: Conceptualization, Data curation, Investigation, Methodology, Resources, Writing—contributed to original draft. MM: Conceptualization, Data curation, Investigation, Methodology, Resources, Writing-contributed to original draft. GS: Conceptualization, Data curation, Investigation, Methodology, Resources, Writing-contributed to original draft, review and editing. JK: Data curation. MT: Data curation. PL: Data curation, Investigation, Resources Writing—contributed to original draft, review and editing. PK: Conceptualization, Writing—contributed to original draft, review and editing. MZ: Conceptualization, Funding acquisition SK: Data curation, Formal analysis, Software, Validation, Visualization. WL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing—review and editing All authors reviewed the manuscript.
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Raychouni, R., Zhang, X., Bauer, S.J. et al. Implementation of SARS-CoV-2 genomic surveillance during the COVID-19 pandemic through an academic–public health collaboration in southeast Michigan. Sci Rep (2026). https://doi.org/10.1038/s41598-026-39974-7
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DOI: https://doi.org/10.1038/s41598-026-39974-7
