Evidence map›Paper›PMID 28573467›Full record

ArticleJournal of mathematical biology2018

Branching process approach for epidemics in dynamic partnership network.

Abid Ali Lashari, Pieter Trapman

Open access · hybridAbstract read
In one paragraph

Article in Journal of mathematical biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 26% of its field
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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 14 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Abid Ali LashariDepartment of Mathematics, Stockholm University, 106 91, Stockholm, Sweden. abid@math.su.se.
Pieter TrapmanDepartment of Mathematics, Stockholm University, 106 91, Stockholm, Sweden.
Stockholm University · SE

Funding

Swedish research counsel projectnr. 2010-5873.
6 · The paper itself

Abstract

We study the spread of sexually transmitted infections (STIs) and other infectious diseases on a dynamic network by using a branching process approach. The nodes in the network represent the sexually active individuals, while connections represent sexual partnerships. This network is dynamic as partnerships are formed and broken over time and individuals enter and leave the sexually active population due to demography. We assume that individuals enter the sexually active network with a random number of partners, chosen according to a suitable distribution and that the maximal number of partners that an individual can have at a time is finite. We discuss two different branching process approximations for the initial stages of an outbreak of the STI. In the first approximation we ignore some dependencies between infected individuals. We compute the offspring mean of this approximating branching process and discuss its relation to the basic reproduction number [Formula: see text]. The second branching process approximation is asymptotically exact, but only defined if individuals can have at most one partner at a time. For this model we compute the probability of a minor outbreak of the epidemic starting with one or few initial cases. We illustrate complications caused by dependencies in the epidemic model by showing that if individuals have at most one partner at a time, the probabilities of extinction of the two approximating branching processes are different. This implies that ignoring dependencies in the epidemic model leads to a wrong prediction of the probability of a large outbreak. Finally, we analyse the first branching process approximation if the number of partners an individual can have at a given time is unbounded. In this model we show that the branching process approximation is asymptomatically exact as the population size goes to infinity.

Indexed as

Models, BiologicalBasic Reproduction NumberComputational BiologyDisease OutbreaksEpidemicsFemaleHumansMaleMathematical ConceptsPopulation DensityProbabilitySexually Transmitted DiseasesSexual PartnersStochastic ProcessesBasic reproduction numberBranching processDynamic networkSI epidemicStochastic epidemic model

Identifiers

PMID28573467
PMCPMC5754507
OpenAlexW2620847367

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LicenceCC BY
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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.