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Title: Inferring COVID-19 testing and vaccination behavior from New Jersey testing data
Characterizing the relationship between disease testing behaviors and infectious disease dynamics is of great importance for public health. Tests for both current and past infection can influence disease-related behaviors at the individual level, while population-level knowledge of an epidemic’s course may feed back to affect one’s likelihood of taking a test. The COVID-19 pandemic has generated testing data on an unprecedented scale for tests detecting both current infection (PCR, antigen) and past infection (serology); this opens the way to characterizing the complex relationship between testing behavior and infection dynamics. Leveraging a rich database of individualized COVID-19 testing histories in New Jersey, we analyze the behavioral relationships between PCR and serology tests, infection, and vaccination. We quantify interactions between individuals’ test-taking tendencies and their past testing and infection histories, finding that PCR tests were disproportionately taken by people currently infected, and serology tests were disproportionately taken by people with past infection or vaccination. The effects of previous positive test results on testing behavior are less consistent, as individuals with past PCR positives were more likely to take subsequent PCR and serology tests at some periods of the epidemic time course and less likely at others. Lastly, we fit a model to the titer values collected from serology tests to infer vaccination trends, finding a marked decrease in vaccination rates among individuals who had previously received a positive PCR test. These results exemplify the utility of individualized testing histories in uncovering hidden behavioral variables affecting testing and vaccination.  more » « less
Award ID(s):
1917819
PAR ID:
10601086
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Editor(s):
Feldman, Marcus
Publisher / Repository:
PNAS Population Biology
Date Published:
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
121
Issue:
17
ISSN:
0027-8424
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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