Modeling the transmission mitigation impact of testing for infectious diseases.


Journal

Science advances
ISSN: 2375-2548
Titre abrégé: Sci Adv
Pays: United States
ID NLM: 101653440

Informations de publication

Date de publication:
14 Jun 2024
Historique:
medline: 14 6 2024
pubmed: 14 6 2024
entrez: 14 6 2024
Statut: ppublish

Résumé

A fundamental question of any program focused on the testing and timely diagnosis of a communicable disease is its effectiveness in reducing transmission. Here, we introduce testing effectiveness (TE)-the fraction by which testing and post-diagnosis isolation reduce transmission at the population scale-and a model that incorporates test specifications and usage, within-host pathogen dynamics, and human behaviors to estimate TE. Using TE to guide recommendations, we show that today's rapid diagnostics should be used immediately upon symptom onset to control influenza A and respiratory syncytial virus but delayed by up to two days to control omicron-era severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, while rapid tests are superior to reverse transcription quantitative polymerase chain reaction (RT-qPCR) to control founder-strain SARS-CoV-2, omicron-era changes in viral kinetics and rapid test sensitivity cause a reversal, with higher TE for RT-qPCR despite longer turnaround times. Last, we illustrate the model's flexibility by quantifying trade-offs in the use of post-diagnosis testing to shorten isolation times.

Identifiants

pubmed: 38875334
doi: 10.1126/sciadv.adk5108
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

eadk5108

Auteurs

Casey Middleton (C)

Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA.
BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA.

Daniel B Larremore (DB)

Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA.
BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA.
Santa Fe Institute, Santa Fe, NM, USA.

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Classifications MeSH