Using genomic epidemiology of SARS-CoV-2 to support contact tracing and public health surveillance in rural Humboldt County, California.

During the COVID-19 pandemic within the United States, much of the responsibility for diagnostic testing and epidemiologic response has relied on the action of county-level departments of public health. Here we describe the integration of genomic surveillance into epidemiologic response within Humboldt County, a rural county in northwest California. Through a collaborative effort, 853 whole SARS-CoV-2 genomes were generated, representing [~]58% of the 1,449 SARS-CoV-2-positive cases detected in Humboldt County as of mid-March 2021. Phylogenetic analysis of these data was used to develop a comprehensive understanding of SARS-CoV-2 introductions to the county and to support contact tracing and epidemiologic investigations of all large outbreaks in the county. In the case of an outbreak on a commercial farm, viral genomic data were used to validate reported epidemiologic links and link additional cases within the community who did not report a farm exposure to the outbreak. During a separate outbreak within a skilled nursing facility, genomic surveillance data were used to rule out the putative index case, detect the emergence of an independent Spike:N501Y substitution, and verify that the outbreak had been brought under control. These use cases demonstrate how developing genomic surveillance capacity within local public health departments can support timely and responsive deployment of genomic epidemiology for surveillance and outbreak response based on local needs and priorities.

Estimation of secondary household attack rates for emergent SARS-CoV-2 variants detected by genomic surveillance at a community-based testing site in San Francisco

BackgroundSequencing of the SARS-CoV-2 viral genome from patient samples is an important epidemiological tool for monitoring and responding to the pandemic, including the emergence of new mutations in specific communities. MethodsSARS-CoV-2 genomic sequences were generated from positive samples collected, along with epidemiological metadata, at a walk-up, rapid testing site in the Mission District of San Francisco, California during November 22-December 2, 2020 and January 10-29, 2021. Secondary household attack rates and mean sample viral load were estimated and compared across observed variants. ResultsA total of 12,124 tests were performed yielding 1,099 positives. From these, 811 high quality genomes were generated. Certain viral lineages bearing spike mutations, defined in part by L452R, S13I, and W152C, comprised 54.9% of the total sequences from January, compared to 15.7% in November. Household contacts exposed to "West Coast" variants were at higher risk of infection compared to household contacts exposed to lineages lacking these variants (0.357 vs 0.294, RR=1.29; 95% CI:1.01-1.64). The reproductive number was estimated to be modestly higher than other lineages spreading in California during the second half of 2020. Viral loads were similar among persons infected with West Coast versus non-West Coast strains, as was the proportion of individuals with symptoms (60.9% vs 64.1%). ConclusionsThe increase in prevalence, relative household attack rates, and reproductive number are consistent with a modest transmissibility increase of the West Coast variants; however, additional laboratory and epidemiological studies are required to better understand differences between these variants. SummaryWe observed a growing prevalence and elevated attack rate for "West Coast" SARS-CoV-2 variants in a community testing setting in San Francisco during January 2021, suggesting its modestly higher transmissibility.

Identification of a polymorphism in the N gene of SARS-CoV-2 that adversely impacts detection by a widely-used RT-PCR assay

We identify a mutation in the N gene of SARS-CoV-2 that adversely affects annealing of a commonly used RT-PCR primer; epidemiologic evidence suggests the virus retains pathogenicity and competence for spread. This reinforces the importance of using multiple targets, preferably in at least 2 genes, for robust SARS-CoV-2 detection. Article Summary LineA SARS-CoV-2 variant that occurs worldwide and has spread in California significantly affects diagnostic sensitivity of an N gene assay, highlighting the need to employ multiple viral targets for detection.

Using viral genomics to estimate undetected infections and extent of superspreading events for COVID-19

Asymptomatic infections and limited testing capacity have led to under-reporting of SARS-CoV-2 cases. This has hampered the ability to ascertain true infection numbers, evaluate the effectiveness of surveillance strategies, determine transmission dynamics, and estimate reproductive numbers. Leveraging both viral genomic and time series case data offers methods to estimate these parameters. Using a Bayesian inference framework to fit a branching process model to viral phylogeny and time series case data, we estimated time-varying reproductive numbers and their variance, the total numbers of infected individuals, the probability of case detection over time, and the estimated time to detection of an outbreak for 12 locations in Europe, China, and the United States. The median percentage of undetected infections ranged from 13% in New York to 92% in Shanghai, China, with the length of local transmission prior to two cases being detected ranging from 11 days (95% CI: 4-21) in California to 37 days (9-100) in Minnesota. The probability of detection was as low as 1% at the start of local epidemics, increasing as the number of reported cases increased exponentially. The precision of estimates increased with the number of full-length viral genomes in a location. The viral phylogeny was informative of the variance in the reproductive number with the 32% most infectious individuals contributing 80% of total transmission events. This is the first study that incorporates both the viral genomes and time series case data in the estimation of undetected COVID-19 infections. Our findings suggest the presence of undetected infections broadly and that superspreading events are contributing less to observed dynamics than during the SARS epidemic in 2003. This genomics-informed modeling approach could estimate in near real-time critical surveillance metrics to inform ongoing COVID-19 response efforts. FundingAWS provided computational credit via the Diagnostic Development Initiative.