tetano
Editor, Senior Moderator
PLoS One
. 2026 Jul 8;21(7):e0342637.
doi: 10.1371/journal.pone.0342637. eCollection 2026.
Beyond traditional outbreak investigation: Using genomic data for enhanced detection of COVID-19 disease clusters in Utah
Mary Jewell[SUP] 1 2 [/SUP], Abbey Marye[SUP] 2 [/SUP], Bree Barbeau[SUP] 1 [/SUP], Kelly Oakeson[SUP] 2 [/SUP]
Affiliations
Background: Traditional disease surveillance, such as manual case investigation, was the primary method for identifying disease clusters during the COVID-19 pandemic. However, the pandemic also provides an opportunity to explore how genomic data can be used to improve cluster detection and response. While genomic data can complement traditional methods, guidelines are needed to integrate genomic data into real-time outbreak response.
Methods: Using binomial and multinomial logistic regression, we compared two methods of disease surveillance in Utah: genomic sequencing of COVID-19 cases and manual case investigation. We evaluated whether these two methods reached the same populations geographically and demographically. Next, we performed genomic clustering using SNP distance thresholds and a logit regression model to identify potential transmission clusters. We compared genomic clusters with epi-identified clusters, defined by manual case investigation, using cluster validation metrics (Adjusted Rand Index, VI), and by assessing biological plausibility (monophyly).
Results: The odds of a case being sequenced varied significantly by jurisdiction and race/ethnicity, with patients in several non-White groups being less likely to undergo sequencing. The genomic clustering methods produced clusters that were notably different from epi-identified clusters. Genomic methods, particularly the logit model, resulted in strong clusters based on metrics of cluster validation and biological plausibility. Analysis of specific epi-defined clusters revealed significant discordance with genomic data. Many large clusters were likely composed of multiple distinct genomic introductions, or contained cases that were not genomically linked.
Conclusions: Genomic data provides an advanced level of resolution for defining disease clusters compared to traditional epidemiological data. The disparities in sequencing coverage necessitate demographically and geographically diverse sampling strategies. Furthermore, it is essential to prioritize sequencing cases in a suspected cluster to maximize the impact of genomic surveillance. Integrating genomic data into epidemiologic investigation enables more precise cluster definitions, strengthening outbreak investigation and public health mitigation.
. 2026 Jul 8;21(7):e0342637.
doi: 10.1371/journal.pone.0342637. eCollection 2026.
Beyond traditional outbreak investigation: Using genomic data for enhanced detection of COVID-19 disease clusters in Utah
Mary Jewell[SUP] 1 2 [/SUP], Abbey Marye[SUP] 2 [/SUP], Bree Barbeau[SUP] 1 [/SUP], Kelly Oakeson[SUP] 2 [/SUP]
Affiliations
- PMID: 42418459
- DOI: 10.1371/journal.pone.0342637
Background: Traditional disease surveillance, such as manual case investigation, was the primary method for identifying disease clusters during the COVID-19 pandemic. However, the pandemic also provides an opportunity to explore how genomic data can be used to improve cluster detection and response. While genomic data can complement traditional methods, guidelines are needed to integrate genomic data into real-time outbreak response.
Methods: Using binomial and multinomial logistic regression, we compared two methods of disease surveillance in Utah: genomic sequencing of COVID-19 cases and manual case investigation. We evaluated whether these two methods reached the same populations geographically and demographically. Next, we performed genomic clustering using SNP distance thresholds and a logit regression model to identify potential transmission clusters. We compared genomic clusters with epi-identified clusters, defined by manual case investigation, using cluster validation metrics (Adjusted Rand Index, VI), and by assessing biological plausibility (monophyly).
Results: The odds of a case being sequenced varied significantly by jurisdiction and race/ethnicity, with patients in several non-White groups being less likely to undergo sequencing. The genomic clustering methods produced clusters that were notably different from epi-identified clusters. Genomic methods, particularly the logit model, resulted in strong clusters based on metrics of cluster validation and biological plausibility. Analysis of specific epi-defined clusters revealed significant discordance with genomic data. Many large clusters were likely composed of multiple distinct genomic introductions, or contained cases that were not genomically linked.
Conclusions: Genomic data provides an advanced level of resolution for defining disease clusters compared to traditional epidemiological data. The disparities in sequencing coverage necessitate demographically and geographically diverse sampling strategies. Furthermore, it is essential to prioritize sequencing cases in a suspected cluster to maximize the impact of genomic surveillance. Integrating genomic data into epidemiologic investigation enables more precise cluster definitions, strengthening outbreak investigation and public health mitigation.