Population network structures, graph theory, algorithms to match subgraphs may lead to better clustering of households and communities in epidemiological studies-Reference-Cited by-同舟云学术

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

Published:2020 Issue: Volume:148 Page:
ISSN:0950-2688
Container-title:Epidemiology and Infection
language:en
Short-container-title:Epidemiol. Infect.

Author:

Rao Arni S. R. Srinivasa^ORCID

Publisher

Cambridge University Press (CUP)

Subject

Infectious Diseases,Epidemiology

Reference6 articles.

1. A subgraph isomorphism algorithm and its application to biochemical data

2. Population, genetic, and antigenic diversity of the apicomplexanEimeria tenellaand their relevance to vaccine development

3. Clustering of hepatitis C virus antibody positivity within households and communities in Punjab, India

4. The burden of hepatitis C virus infection in Punjab, India: A population-based serosurvey

5. Understanding chicken walks on n × n grid: Hamiltonian paths, discrete dynamics, and rectifiable paths

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Modeling, analyzing and simulating the dynamics of Lassa fever in Nigeria;Journal of the Egyptian Mathematical Society;2022-01-25

2. Network models in epidemiology;Handbook of Statistics;2021

3. Population network structures, graph theory, algorithms to match subgraphs may lead to better clustering of households and communities in epidemiological studies: a response;Epidemiology and Infection;2020

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