Leveraging global genomic sequencing data to estimate local variant dynamics
ABSTRACT Accurate, reliable, and timely estimates of pathogen variant risk are essential for informing public health responses. Unprecedented rates of genomic sequencing have generated new insights into variant dynamics. However, estimating the fitness advantage of a novel variant shortly after emergence, or its dynamics more generally in data-sparse settings, remains difficult. This challenge is exacerbated in countries where surveillance is limited or intermittent. To stabilize inference in these data-sparse settings, we develop a hierarchical modeling approach to estimate variant fitness advantage and prevalence by pooling data across geographic regions. We demonstrate our method by reconstructing SARS-CoV-2 BA.5 variant emergence, and assess performance using retrospective, out-of-sample validation. We show that stable and robust estimates can be obtained even when sequencing data are sparse. Finally, we discuss how this method can inform risk assessment of novel variants and provide situational awareness on circulating variants for a range of pathogens and use-cases..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 23. März Zur Gesamtaufnahme - year:2023 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Susswein, Zachary [VerfasserIn] |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| Themen: |
|---|
| doi: |
10.1101/2023.01.02.23284123 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
XBI038332051 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | XBI038332051 | ||
| 003 | DE-627 | ||
| 005 | 20231205145752.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230105s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1101/2023.01.02.23284123 |2 doi | |
| 035 | |a (DE-627)XBI038332051 | ||
| 035 | |a (biorXiv)10.1101/2023.01.02.23284123 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Susswein, Zachary |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Leveraging global genomic sequencing data to estimate local variant dynamics |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a ABSTRACT Accurate, reliable, and timely estimates of pathogen variant risk are essential for informing public health responses. Unprecedented rates of genomic sequencing have generated new insights into variant dynamics. However, estimating the fitness advantage of a novel variant shortly after emergence, or its dynamics more generally in data-sparse settings, remains difficult. This challenge is exacerbated in countries where surveillance is limited or intermittent. To stabilize inference in these data-sparse settings, we develop a hierarchical modeling approach to estimate variant fitness advantage and prevalence by pooling data across geographic regions. We demonstrate our method by reconstructing SARS-CoV-2 BA.5 variant emergence, and assess performance using retrospective, out-of-sample validation. We show that stable and robust estimates can be obtained even when sequencing data are sparse. Finally, we discuss how this method can inform risk assessment of novel variants and provide situational awareness on circulating variants for a range of pathogens and use-cases. | ||
| 650 | 4 | |a Biology |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Johnson, Kaitlyn E. |4 aut | |
| 700 | 1 | |a Kassa, Robel |4 aut | |
| 700 | 1 | |a Parastaran, Mina |4 aut | |
| 700 | 1 | |a Peng, Vivian |4 aut | |
| 700 | 1 | |a Wolansky, Leo |4 aut | |
| 700 | 1 | |a Scarpino, Samuel V. |0 (orcid)0000-0001-5716-2770 |4 aut | |
| 700 | 1 | |a Bento, Ana I. |0 (orcid)0000-0001-7728-007X |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t bioRxiv.org |g (2023) vom: 23. März |
| 773 | 1 | 8 | |g year:2023 |g day:23 |g month:03 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1101/2023.01.02.23284123 |z kostenfrei |3 Volltext |
| 912 | |a GBV_XBI | ||
| 912 | |a SSG-OLC-PHA | ||
| 951 | |a AR | ||
| 952 | |j 2023 |b 23 |c 03 | ||