Evidence map›Paper›PMID 38630720›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2024

Inferring COVID-19 testing and vaccination behavior from New Jersey testing data.

Ari S Freedman, Justin K Sheen, Stella Tsai, Jihong Yao, Edward Lifshitz, David Adinaro, Simon A Levin, Bryan T Grenfell, C Jessica E Metcalf

Open access · hybridAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.9field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Ari S FreedmanDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544.ORCID 0009-0004-3742-9043
Justin K SheenDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544.ORCID 0000-0001-7842-1364
Stella TsaiNew Jersey Department of Health, Trenton, NJ 08625.
Jihong YaoNew Jersey Department of Health, Trenton, NJ 08625.ORCID 0009-0002-5852-6032
Edward LifshitzNew Jersey Department of Health, Trenton, NJ 08625.ORCID 0000-0002-0342-201X
David AdinaroNew Jersey Department of Health, Trenton, NJ 08625.
Simon A LevinDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544.ORCID 0000-0002-8216-5639
Bryan T GrenfellDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544.
C Jessica E MetcalfDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544.ORCID 0000-0003-3166-7521
Princeton University · USNew Jersey Department of Health · US

Funding

Google (Google LLC) Google Covid-19 AI for Social Good ProgramNational Science Foundation (NSF) CCF1917819National Science Foundation (NSF) CNS-2041952Princeton Catalysis Initiative N/ASchmidt Futures Foundation Schmidt DataX Fund
6 · The paper itself

Abstract

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.

Indexed as

COVID-19COVID-19 TestingHumansNew JerseyPandemicsVaccinationCOVID-19disease testing behaviorserology

Identifiers

PMID38630720
PMCPMC11047110
OpenAlexW4394892560

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.