Epidemic dynamics on higher-dimensional small world networks.

Haiying Wang, Jack Murdoch Moore, Michael Small, Jun Wang, Huijie Yang, Changgui Gu

Abstract read

In one paragraph

Article in Applied mathematics and computation, 2022. 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

6 authors.

Haiying WangBusiness School, University of Shanghai for Science and Technology, 334 Jungong Road, Shanghai, 200093, China.

Jack Murdoch MooreSchool of Physics Science and Engineering, Tongji University, 1239 Siping Road, Shanghai, 200092, Western Australia, China.

Michael SmallComplex Systems Group, Department of Mathematics and Statistics, University of Western Australia, 35 Stirling Highway, Crawley, 6009, Australia.

Jun WangSchool of Economics and Management, Beihang University, 37 Xueyuan Road, Beijing, 100191, China.

Huijie YangBusiness School, University of Shanghai for Science and Technology, 334 Jungong Road, Shanghai, 200093, China.

Changgui GuBusiness School, University of Shanghai for Science and Technology, 334 Jungong Road, Shanghai, 200093, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dimension governs dynamical processes on networks. The social and technological networks which we encounter in everyday life span a wide range of dimensions, but studies of spreading on finite-dimensional networks are usually restricted to one or two dimensions. To facilitate investigation of the impact of dimension on spreading processes, we define a flexible higher-dimensional small world network model and characterize the dependence of its structural properties on dimension. Subsequently, we derive mean field, pair approximation, intertwined continuous Markov chain and probabilistic discrete Markov chain models of a COVID-19-inspired susceptible-exposed-infected-removed (SEIR) epidemic process with quarantine and isolation strategies, and for each model identify the basic reproduction number

Indexed as

Epidemic spreadingModel errorNetwork dimensionSmall world modelSpreading dynamics

Identifiers

PMID35068617

DOI10.1016/j.amc.2021.126911

PMCPMC8759951

What OpenQuestion holds

Textmetadata

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.