D
Daniel B. Larremore
Researcher at University of Colorado Boulder
Publications - 89
Citations - 6085
Daniel B. Larremore is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 26, co-authored 74 publications receiving 3878 citations. Previous affiliations of Daniel B. Larremore include Harvard University & Santa Fe Institute.
Papers
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Journal ArticleDOI
Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening.
Daniel B. Larremore,Bryan Wilder,Evan Lester,Evan Lester,Soraya Shehata,Soraya Shehata,James M. Burke,James A. Hay,Milind Tambe,Michael J. Mina,Michael J. Mina,Roy Parker +11 more
TL;DR: It is demonstrated that effective screening depends largely on frequency of testing and speed of reporting and is only marginally improved by high test sensitivity, and should prioritize accessibility, frequency, and sample-to-answer time.
Journal ArticleDOI
Rethinking Covid-19 Test Sensitivity - A Strategy for Containment.
TL;DR: A simple point-of-care test that is inexpensive enough to use frequently, even if it lacks sensitivity, is proposed for Covid-19 cases.
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Model-informed COVID-19 vaccine prioritization strategies by age and serostatus.
Kate M Bubar,Kyle Reinholt,Stephen M Kissler,Marc Lipsitch,Sarah Cobey,Yonatan H. Grad,Daniel B. Larremore +6 more
TL;DR: This article used a mathematical model to compare five age-stratified prioritization strategies for the SARS-CoV-2 vaccine and found that individual-level serological tests to redirect doses to seronegative individuals improved the marginal impact of each dose while potentially reducing existing inequities in COVID-19 impact.
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The ground truth about metadata and community detection in networks
TL;DR: It is proved that no algorithm can uniquely solve community detection, and a general No Free Lunch theorem for community detection is proved, which implies that there can be no algorithm that is optimal for all possible community detection tasks.
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Systematic inequality and hierarchy in faculty hiring networks
TL;DR: It is found that faculty hiring follows a common and steeply hierarchical structure that reflects profound social inequality and increased institutional prestige leads to increased faculty production, better faculty placement, and a more influential position within the discipline.