COVID-19 Variant Surveillance Data as an Early Indicator of COVID-19 Case Surges Open Access

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

About this Master's Thesis

Rights statement

• Permission granted by the author to include this thesis or dissertation in this repository. All rights reserved by the author. Please contact the author for information regarding the reproduction and use of this thesis or dissertation.

School	Rollins School of Public Health
Department	Epidemiology
Subfield / Discipline	Epidemiology - MPH & MSPH
Degree	M.P.H.
Submission	Master's Thesis
Language	English
Research Field	Biology, Bioinformatics Health Sciences, Public Health Health Sciences, Epidemiology
Keyword	Epidemiology Delta Variant COVID-19 Genomics Surveillance Variant
Committee Chair / Thesis Advisor	Dr. Kristin Bratton Nelson, Emory University
Committee Members	Dr. Benjamin J. Silk, CDC
Partnering Agencies	CDC

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