Evidence map›Paper›PMID 41513947›Full record

ArticleCommunications biology2026

SIMPLICITY is an agent-based, multi-scale mathematical model to study SARS-CoV-2 intra- and between-host evolution.

Pietro Gerletti, Nils Gubela, Jean-Baptiste Escudié, Denise Kühnert, Max Von Kleist

Abstract read
In one paragraph

Article in Communications biology, 2026. 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

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

5 authors.

Pietro GerlettiCenter for Artificial Intelligence in Public Health, Robert Koch Institute, Berlin, Germany. simplicity.twisty120@passfwd.com.ORCID http://orcid.org/0000-0002-2118-3956
Nils Gubela *Department of Mathematics & Computer Science, Freie Universität Berlin, Berlin, Germany.ORCID http://orcid.org/0009-0003-1391-3343
Jean-Baptiste Escudié *Center for Artificial Intelligence in Public Health, Robert Koch Institute, Berlin, Germany.
Denise KühnertCenter for Artificial Intelligence in Public Health, Robert Koch Institute, Berlin, Germany.
Max Von KleistDepartment of Mathematics & Computer Science, Freie Universität Berlin, Berlin, Germany.ORCID http://orcid.org/0000-0001-6587-6394

Funding

Bundesministerium für Gesundheit (Federal Ministry of Health, Germany) 2523DAT400Deutsche Forschungsgemeinschaft (German Research Foundation) 390685689
6 · The paper itself

Abstract

Computational tools are frequently used to describe pathogen evolutionary dynamics either within infected hosts or at the population level. However, there is a lack of models that capture the complex interplay between within-host and between-host evolutionary dynamics, leaving a knowledge gap with regard to realistic evolutionary dynamics. We present SIMPLICITY, a multi-scale mathematical model that combines within-host disease progression and viral evolution with a population-level model of virus transmission and immune evasion. We parameterize SIMPLICITY based on SARS-CoV-2 within-host viral dynamics, observed evolutionary rates, and dynamics of immune waning. We then apply it to study the dynamics and mechanisms driving SARS-CoV-2 evolution at the population level. We compare a baseline toy model of gradually increasing transmission fitness with an adaptive fitness landscape model that accounts for infection history and immune waning. Our simulations demonstrate that escape from population immunity generates evolutionary dynamics encompassing selective sweeps, which resemble SARS-CoV-2 evolution.

Indexed as

Biological EvolutionCOVID-19Host-Pathogen InteractionsModels, TheoreticalSARS-CoV-2Computer SimulationEvolution, MolecularHumansImmune EvasionModels, BiologicalPandemics

Identifiers

PMID41513947
PMCPMC12855898

What OpenQuestion holds

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Registered trials

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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.