Implementation of SARS-CoV-2 genomic surveillance during the COVID-19 pandemic through an academic–public health collaboration in southeast Michigan

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.