Article

Failure to balance social contact matrices can bias models of infectious disease transmission

2022-07-31

Abstract excerpt

<h4>ABSTRACT</h4> Spread of transmissible diseases is dependent on contact patterns in a population (i.e. who contacts whom). Therefore, many epidemic models incorporate contact patterns within a population through contact matrices. Social contact survey data are commonly used to generate contact matrices; however, the resulting matrices are often imbalanced, such that the total number of contacts reported by grou...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 99112a08-28bf-5cc7-a914-1a9edda2e3c0
DOI: 10.1101/2022.07.28.22278155

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Failure to balance social contact matrices can bias models of infectious disease transmissionDOI 10.1101/2022.07.28.22278155Read

Social contact patterns relevant for infectious disease transmission in CambodiaSemantic proximity Read

Social contact patterns across levels of deprivation in England, and implications for infectious disease transmissionSemantic proximity Read

Data-driven estimate of SARS-CoV-2 herd immunity threshold in populations with individual contact pattern variationsSemantic proximity Read

The illusion of personal health decisions for infectious disease management: disease spread in social contact networksSemantic proximity Read

Social inequalities in vaccine coverage and their effects on epidemic spreadingSemantic proximity Read

Improving estimates of social contact patterns for the airborne transmission of respiratory pathogensSemantic proximity Read

Data-Driven Construction of Age-Structured Contact NetworksSemantic proximity Read

High-Resolution Epidemic Simulation Using Within-Host Infection and Contact DataSemantic proximity Read

SOCRATES: An online tool leveraging a social contact data sharing initiative to assess mitigation strategies for COVID-19Semantic proximity Read

A Machine Learning Framework for Constructing Heterogeneous Contact Networks: Implications for Epidemic ModellingSemantic proximity Read

Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemicSemantic proximity Read

A comparison of random mixing in a structured agent-based model with empirical contact survey dataSemantic proximity Read