Evidence map›Paper›PMID 41886375›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

VIBES: A multiscale modeling approach integrating within-host and between-hosts dynamics in epidemics.

Paulo Cesar Ventura, Yong Dam Jeong, Maria Litvinova, Allisandra G Kummer, Shingo Iwami, Hongjie Yu, Stefano Merler, Alessandro Vespignani, Keisuke Ejima, Marco Ajelli

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Paulo Cesar VenturaLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN 47405.ORCID 0000-0001-8441-9359
Yong Dam Jeonginterdisciplinary Biology Laboratory, Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan.
Maria LitvinovaDepartment of Epidemiology and Biostatistics, Indiana University, School of Public Health Bloomington, IN 47405.ORCID 0000-0001-6393-1943
Allisandra G KummerLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN 47405.ORCID 0000-0002-1076-3410
Shingo Iwamiinterdisciplinary Biology Laboratory, Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan.ORCID 0000-0002-1780-350X
Hongjie YuShanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai 200032, China.ORCID 0000-0002-6335-5648
Stefano MerlerCenter for Health Emergencies, Bruno Kessler Foundation, Trento 38123, Italy.ORCID 0000-0002-5117-0611
Alessandro VespignaniLaboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA 02115.ORCID 0000-0003-3419-4205
Keisuke EjimaLee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore.
Marco AjelliLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN 47405.ORCID 0000-0003-1753-4749

Funding

CDC HHS NU38OT000297EC | NextGenerationEU (NGEU) PE00000007HHS | Centers for Disease Control and Prevention (CDC) CDC-RFA-FT-23-0069Ministry of Science and ICT, South Korea (MSIT) RS-2024-00345478MOST | National Natural Science Foundation of China (NSFC) 82130093National Research Foundation of Korea (NRF) RS-2024-00345478NTU | Lee Kong Chian School of Medicine, Nanyang Technological University (LKCMedicine) #022487-00001Shanghai Science and Technology Development Foundation (Shanghai Science and Technology Foundation) ZD2021CY001
6 · The paper itself

Abstract

Infectious disease spread is a multiscale process composed of within-host (biological) and between-host (social) drivers and disentangling them from each other is a central challenge in epidemiology. Here, we introduce VIBES, a multiscale modeling framework that explicitly integrates viral dynamics based on patient-level data with population-level transmission on a data-driven network of social contacts. Using SARS-CoV-2 as a case study, we analyze three emergent epidemic properties, namely the generation time, serial interval, and presymptomatic transmission. First, we established a purely biological baseline, thus independent of the reproduction number (

Indexed as

COVID-19EpidemicsEpidemiological ModelsHumansModels, BiologicalSARS-CoV-2agent-based modelepidemiologymultiscale modelSARS-CoV-2

Identifiers

PMID41886375
PMCPMC13037879

What OpenQuestion holds

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LicenceCC BY
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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.