Evidence map›Paper›PMID 42160132›Full record

ArticleJMIR formative research2026

Transmission Dominance Under Random-Contact Intensification in Epidemic Networks: Multilayer Contact Network Simulation Study.

Mingxuan Zhang, Tomohide Maekawa, Kaira Sekiguchi, Tadahiko Murata, Yukio Ohsawa

Abstract read
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

5 authors.

Mingxuan ZhangDepartment of Systems Innovation, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan, Bunkyo-ku, Tokyo, 113-0033, Japan, 81 3-5841-6533.ORCID 0009-0004-2449-8517
Tomohide MaekawaData Engineering, Trust Architecture Inc., Minato-ku, Tokyo, Japan.ORCID 0009-0003-6779-7093
Kaira SekiguchiDepartment of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan, Bunkyo-ku, Tokyo, 113-8656, Japan.ORCID 0000-0001-6473-3391
Tadahiko MurataD3 Center & Graduate School of Information Science and Technology, The University of Osaka, Ibaraki, Osaka, Japan.ORCID 0000-0002-1654-3945
Yukio OhsawaDepartment of Systems Innovation, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan, Bunkyo-ku, Tokyo, 113-8656, Japan.ORCID 0000-0003-2943-2547

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In the context of COVID-19, infection spread through human contact networks remains a major public health challenge. Beyond cumulative infections and deaths, it is necessary to understand which contacts matter most, and which population segments contribute most to transmission under different social conditions. In multilayer urban networks with community structure, routine contacts coexist with incidental encounters, and it remains unclear whether incidental encounters can alter epidemic burden and the main contributors to transmission when per-layer contact caps and routine-contact minima are unchanged (for the nonrandom layers). Objective: Under explicit daily-contact constraints, we examined (1) how changing overall contact opportunities affects epidemic speed and burden when incidental encounters are held fixed, and (2) whether increasing incidental encounters alone, per-layer contact caps, and routine-contact minima fixed (for the nonrandom layers), shifts the main contributors to transmission from a high-contact group to a medium-contact group, and the underlying network mechanism. Methods: We constructed a multilayer potential contact network for a synthetic urban population of 10,038 individuals, representing household, school, workplace, distance-driven activities, and incidental encounters as separate layers. Daily contact networks were sampled from the potential network each day, and transmission was simulated for 120 days using a Susceptible-Exposed-Infectious-Removed model with vaccination. Individuals were classified into high-contact and medium-contact groups based on baseline contact intensity, and group contribution combined each group's share of infectious individuals and its per-infectious effective transmission yield. Contact-constraint parameters were calibrated using an online survey in Tokyo and Kanagawa (n=1089), and scenario comparisons and parameter sweeps were used to locate the transition point. Results: With incidental encounters held fixed, higher overall contact opportunities produced earlier and higher epidemic peaks and larger cumulative infections and deaths, whereas reduced opportunities slowed and prolonged spread. Holding overall contact opportunities and routine contacts fixed, increasing incidental encounters shifted the main contributors to transmission: higher-contact individuals accounted for more effective transmissions at low incidental contact, whereas medium-contact individuals accounted for more beyond a clear transition point. Network visualization and schematics suggest a bridge-allocation mechanism, where stronger incidental contact adds cross-community bridges that more often terminate at medium-contact individuals and carry infection into less-affected communities. Across R=30 replicate runs under fixed settings, the dominance flip was consistently observed, and the estimated threshold W∗ showed a narrow but nonzero distribution (reported as median and IQR). Conclusions: In multilayer urban contact networks with community structure, our results indicate that intensifying incidental encounters can change the main contributors to transmission even when overall contact opportunities and routine contacts are unchanged. We present an analysis framework under explicit daily-contact constraints to identify this contributor shift and its transition point, supporting comparisons of intervention priorities across social contact conditions.

Indexed as

Contact TracingCOVID-19Computer SimulationHumansPandemicsSARS-CoV-2agent-based simulationcontact networksCOVID-19digital epidemiologyepidemic modelingmultilayer networkspublic health informaticsrandom contactsSEIRSusceptible-Exposed-Infectious-Removedtransmission heterogeneity

Identifiers

PMID42160132
PMCPMC13191084

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.