Evidence map›Paper›PMID 42542443›Full record

ArticleScientific reports2026

Impact of temporal patterns in working contacts on epidemic spread.

Aleksandr Bryzgalov, Johannes Ponge, Janik Suer, Tyll Krueger, Beryl Musundi, Chao Xu, Wolfgang Bock, Johannes Horn, Mahreen Kahkashan, André Karch and 4 more

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

14 authors.

Aleksandr BryzgalovInstitute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, 06097, Halle (Saale), Germany. aleksandr.bryzgalov@uk-halle.de.
Johannes PongeDepartment of Information Systems, University of Münster, 48149, Münster, Germany.
Janik SuerInstitute of Epidemiology and Social Medicine, University of Münster, 48149, Münster, Germany.
Tyll KruegerWroclaw University of Science and Technology, 50-370, Wrocław, Poland.
Beryl MusundiInstitute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, 06097, Halle (Saale), Germany.
Chao XuInstitute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, 06097, Halle (Saale), Germany.
Wolfgang BockFaculty of Technology, Department of Mathematics, Linnaeus University, 351 95, Växjö, Sweden.
Johannes HornInstitute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, 06097, Halle (Saale), Germany.
Mahreen KahkashanInstitute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, 06097, Halle (Saale), Germany.
André KarchInstitute of Epidemiology and Social Medicine, University of Münster, 48149, Münster, Germany.
Julian PatznerInstitute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, 06097, Halle (Saale), Germany.
Mirjam KretzschmarDepartment of Epidemiology, University Medical Center Utrecht, 3584 CX, Utrecht, The Netherlands.
Rafael MikolajczykInstitute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, 06097, Halle (Saale), Germany.
OptimAgent Consortium

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most models for infectious disease spread simplify contact heterogeneity by assuming constant rates within a week. However, empirical studies show clear variation, such as reduced workplace contacts on weekends. In this work, we investigate the effects of daily variation in workplace contacts on the spread of respiratory infections using the individual-based framework GEMS (German Epidemic Micro-Simulation System) with a synthetic population of 5 million individuals. We compare the scenario with uniform daily contacts to the scenario with more contacts on workdays and fewer on weekends, keeping weekly totals constant. Simulations reveal that uniform contact rates yield higher prevalence for short latent periods (1-2 days) and lower prevalence for longer latencies (5-6 days). The effect diminishes for long infectious periods (≥ 7 days) but is more pronounced at lower basic reproduction numbers. Depending on the disease specification and the differences in weekday contacts, the impact of weekday heterogeneity can be very strong. These findings open new possibilities for weekday-dependent mitigation measures. We conclude that weekly contact dynamics should be explicitly incorporated into epidemic models to avoid systematic errors in reproduction number estimation.

Indexed as

Contact TracingEpidemicsRespiratory Tract InfectionsBasic Reproduction NumberComputer SimulationGermanyHumansTime FactorsWorkplaceGEMSLatent periodRedistribution of contactsSpectral radiusWeek structure

Identifiers

PMID42542443
PMCPMC13428738

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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.