This article is a Preprint
Preprints are preliminary research reports that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Preprints posted online allow authors to receive rapid feedback and the entire scientific community can appraise the work for themselves and respond appropriately. Those comments are posted alongside the preprints for anyone to read them and serve as a post publication assessment.
Early risk-assessment of pathogen genomic variants emergence (preprint)
medrxiv; 2023.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2023.01.02.23284123
ABSTRACT
Accurate, reliable, and timely estimates of pathogen variant risk are essential for informing effective public health responses to infectious diseases. Despite decades of use for influenza vaccine strain selection and PCR-based molecular diagnostics, data on pathogen variant prevalence and growth advantage has only risen to its current prominence during the SARS-CoV-2 pandemic. However, such data are still often sparse novel variants are initially rare or a region has limited sequencing. To ensure real-time estimates of risk are available in these types of data-sparse conditions, we develop a hierarchical modeling approach that estimates variant fitness advantage and prevalence by pooling data across geographic regions. We apply this method to estimate SARS-CoV-2 variant dynamics at the country-level and assess its stability with retrospective validation. Our results show that more stable and robust estimates can be obtained even when sequencing data are sparse, as compared to established, single-country estimation approaches. We discuss how this method can inform risk assessment of novel emerging variants and provide situational awareness on currently circulating variants, for a range of pathogens and use-cases.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Main subject:
Seizures
/
Communicable Diseases
Language:
English
Year:
2023
Document Type:
Preprint
Similar
MEDLINE
...
LILACS
LIS