Population network structures, graph theory, algorithms to match subgraphs may lead to better clustering of households and communities in epidemiological studies

Arni S.R.Srinivasa Rao

doi:10.1017/S0950268819002255

Population network structures, graph theory, algorithms to match subgraphs may lead to better clustering of households and communities in epidemiological studies

Research output: Contribution to journal › Letter › peer-review

Original language	English (US)
Article number	e10
Journal	Epidemiology and Infection
DOIs	https://doi.org/10.1017/S0950268819002255
State	Accepted/In press - 2020

ASJC Scopus subject areas

Epidemiology
Infectious Diseases

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1017/S0950268819002255

Cite this

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title = "Population network structures, graph theory, algorithms to match subgraphs may lead to better clustering of households and communities in epidemiological studies",

author = "Rao, {Arni S.R.Srinivasa}",

year = "2020",

doi = "10.1017/S0950268819002255",

language = "English (US)",

journal = "Epidemiology and Infection",

issn = "0950-2688",

publisher = "Cambridge University Press",

}

TY - JOUR

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AU - Rao, Arni S.R.Srinivasa

PY - 2020

Y1 - 2020

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DO - 10.1017/S0950268819002255

M3 - Letter

C2 - 31959273

AN - SCOPUS:85078096083

SN - 0950-2688

JO - Epidemiology and Infection

JF - Epidemiology and Infection

M1 - e10

ER -

Population network structures, graph theory, algorithms to match subgraphs may lead to better clustering of households and communities in epidemiological studies

ASJC Scopus subject areas

UN SDGs

Access to Document

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