COVID-19 Variant Surveillance Data as an Early Indicator of COVID-19 Case Surges Open Access
Kalangara, Alisha (Spring 2022)
Abstract
Introduction: The COVID-19 pandemic is the first pandemic to occur in the modern genomic
sequencing era. Genomic surveillance has been utilized during the COVID-19 pandemic to study the
pandemic origins, aid in outbreak investigations, study potential immune escape, and to actively monitor
the emergence and prevalence of new SARS-CoV-2 variants. Given the public health significance of
SARS-CoV-2 variants, we investigate whether genomic surveillance data could help anticipate domestic
surges in case incidence at the state level.
Methods: The objective of this study was to assess whether modeling of Delta variant proportion data
could act as an early indicator for reported COVID-19 case surges. Publicly available datasets were
utilized to capture longitudinal data during the Delta variant’s circulation in the US (April 19 – October
24 of 2021). Case incidence for each state was calculated using July 2021 US Census Bureau population
estimates. Using generalized estimating equations with an autoregressive correlation matrix, the
relationship between changes in Delta variant proportion and changes in case incidence was estimated
using non-lagged, 2-week lagged, and 4-week lagged data, while adjusting for vaccination rates, infection
induced seroprevalence, and case age distribution.
Results: At the state level, the 2-week lagged model had the strongest association between the Delta
variant proportion data and a surge in COVID-19 case incidence (OR: 14.30, 95% CI: 7.12 - 29.08). The
model with four-week lag suggested a weaker association between change in Delta variant proportion and
a COVID-19 surge would occur 3 or 4 weeks later (OR: 2.12, 95% CI: 0.88 - 5.10). The non-lagged
model showed a strong positive association, demonstrating simultaneous rises of Delta variant
proportions and overall COVID-19 case incidence (OR: 8.85, 95% CI: 4.18 - 18.77).
Conclusion: Our results suggest that monitoring changes in COVID-19 variant proportion data can act as
a leading indicator of COVID-19 case incidence surges. This genomic surveillance strategy is important
for anticipating a surge, which allows for appropriate public health and healthcare capacity measures to be
prepared to lessen or avoid the consequences of a surge.
Table of Contents
Introduction………………………………………………………………………..………………………..1
Methods……………………………………………………………………………………………..………4
Time Period………………………………………………………………………….……………..4
Exposure Variable………………………………………………………………….……………....4
Outcome Variable………………………………………………………………………………….5
Confounders………………………………………………………………………….…………….6
Figure 1. Directed Acyclic Graph Diagram for Analysis …………………...……………..6
Modelling Strategy……………………………………………………………................................8
Results………………………………………………………………………………………………………8
COVID-19 case incidence………………………………………………………………………….8
Figure 2. COVID-19 Case Incidence per 100k…………………………………………….9
Prevalence of SARS-CoV-2 Delta Variant…………………………………………………………9
Figure 3. Percent of Cases Attributable to SARS-CoV-2 Delta Variant Among US States
Through Time……………………………………………………………………………10
Figure 4. 2-week Change in Delta Variant Proportion and Percent Change in Incidence
Across Time in Select US States………………………………………………………….11
Generalized Estimating Equation Models………………………………………………………...12
Table 1. Lagged and Nonlagged Model Results…………………………………………12
Discussion…………………………………………………………………………………………………12
Model Results…………………………………………………………………………………….12
Limitations…………………………………………………………………………………….….13
Context and Public Health Significance………………………………………………………….14
References…………………………………………………………………………………………………16
Appendix…………………………………………………………………………………………………..20
Emory IRB Exemption……………………………………………………………………………20
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