Unlinking super-linkers: the topology of epidemic response (Covid-19)
Shishir Nagaraja

TL;DR
This paper analyzes the topology of contact networks to identify key transmission paths and proposes targeted interventions like isolating super-links and super-spreaders to effectively prevent epidemic outbreaks, especially when contact tracing is limited.
Contribution
It introduces a novel focus on super-links and super-spreaders in contact networks, demonstrating their critical role in epidemic control and proposing targeted isolation strategies.
Findings
Isolating super-links prevents outbreaks with 35% visibility and 25% population isolation.
Targeted isolation of super-spreaders is more effective than contact tracing alone.
Topology-based interventions complement traditional contact tracing and testing.
Abstract
A key characteristic of the spread of infectious diseases is their ability to use efficient transmission paths within contact graphs. This enables the pathogen to maximise infection rates and spread within a target population. In this work, we devise techniques to localise infections and decrease infection rates based on a principled analysis of disease transmission paths within human-contact networks (proximity graphs). Experimental results of disease spreading shows that that at low visibility rates contact tracing slows disease spreading. However to stop disease spreading, contact tracing requires both significant visibility (at least 60%) into the proximity graph and the ability to place half of the population under isolation. We find that pro-actively isolating super-links -- key proximity encounters -- has significant benefits: targeted isolation of a fourth of the population…
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Taxonomy
TopicsComplex Network Analysis Techniques · COVID-19 epidemiological studies · Misinformation and Its Impacts
