Evidence map›Paper›PMID 41406178›Full record

ArticlePLoS computational biology2025

Quantifying viral pandemic potential from experimental transmission studies.

Elizabeth D Somsen, Kayla M Septer, Cassandra J Field, Devanshi R Patel, Anice C Lowen, Troy C Sutton, Katia Koelle

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Elizabeth D SomsenGraduate Program in Population Biology, Ecology, and Evolution, Emory University, Atlanta, Georgia, United States of America.ORCID 0000-0003-2331-470X
Kayla M SepterDepartment of Veterinary and Biomedical Sciences, The Pennsylvania State University, State College, Pennsylvania, United States of America.
Cassandra J FieldDepartment of Veterinary and Biomedical Sciences, The Pennsylvania State University, State College, Pennsylvania, United States of America.
Devanshi R PatelDepartment of Veterinary and Biomedical Sciences, The Pennsylvania State University, State College, Pennsylvania, United States of America.
Anice C LowenDepartment of Microbiology and Immunology, Emory University, Atlanta, Georgia, United States of America.
Troy C SuttonDepartment of Veterinary and Biomedical Sciences, The Pennsylvania State University, State College, Pennsylvania, United States of America.
Katia KoelleEmory Center of Excellence for Influenza Research and Response (Emory CEIRR), Atlanta, Georgia, United States of America.

Funding

NIAID Centers of Excellence for Influenza Research and Response: Universal Influenza Vaccine Research Activities75N93021C00017 · NIAID · EMORY UNIVERSITY · PI LOWEN, ANICE · 2021 to 2025
$27.3M
Infectious Diseases Across Scales Training ProgramT32AI138952 · NIAID · EMORY UNIVERSITY · PI Jacobus de Roode · 2019 to 2026
$1.8M
Developing novel methods to assess the pandemic potential of influenza A viruses using mathematical modeling and evolutionary methodsF31AI186550 · NIAID · EMORY UNIVERSITY · PI Elizabeth Somsen · 2024 to 2026
$149k
NIAID NIH HHS 75N93021C00017NIAID NIH HHS F31 AI186550NIAID NIH HHS T32 AI138952
6 · The paper itself

Abstract

In an effort to avert future pandemics, surveillance studies aim to identify animal viruses at high risk of spilling over into humans. These studies have revealed substantial diversity in identified viruses. However, the number of tools currently available to assess pandemic risk is limited. Methods currently in use include the characterization of candidate viruses using in vitro laboratory assays and experimental transmission studies in animal models. However, transmission experiments yield relatively low-resolution outcomes that are not immediately translatable to projections of viral dynamics at the level of a host population. To address this gap, we present an analytical framework to extend the use of measurements from experimental transmission studies to generate more quantitative risk assessments. Specifically, we use within-host viral titer data from index and contact animals to estimate parameters relevant to transmission between pairs of individuals. We then extend this model to estimate epidemiological parameters, such as reproduction numbers and generation intervals. We present our analytical framework in the context of two influenza A virus (IAV) ferret transmission experiments: one using influenza A/California/07/2009 (Cal/2009) and one using influenza A/Hong Kong/1/1968 (Hong Kong/1968). In a head-to-head comparison, we find that Cal/2009 has higher pandemic potential than Hong Kong/1968. Our results depend on several assumptions, including that within-host viral dynamics in humans and those in the model animal used (here, ferrets) share quantitative similarities and that viral transmissibility between model animals reflects viral transmissibility between humans. The methods we present to assess pandemic risk of viral isolates can be used to improve relative risk assessment of other emerging viruses of pandemic concern.

Indexed as

Influenza, HumanOrthomyxoviridae InfectionsPandemicsAnimalsComputational BiologyFerretsHumansInfluenza A virusModels, BiologicalRisk Assessment

Identifiers

PMID41406178
PMCPMC12721528

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.