OSC Scientific Seminar 2020-2021

Friday, 27th November 2020, 11:30 am / 1 pm (Zoom videoconference)

Halting SARS-CoV-2 by Targeting High-Contact Individuals

Gianluca Manzo

Research Fellow, CNRS-GEMASS Sorbonne University

This presentation is based on a paper writen with Arnout van de Rijt (European University Institute, Florence) and published by the JASSS in October 2020. 

Most policy measures that are currently used or considered to contain the novel coronavirus SARS-CoV-2 are aimed at broad groups of citizens (children, elderly, contact professions) or categories of meeting places (schools, restaurants, airports). At the same time, a fair amount of evidence now suggests that the spread of many person-to-person viruses is driven by a small fraction of individuals, sometimes referred to as “super-spreaders”, who are responsible for the vast majority of secondary infections.  

Estimates of the over-dispersion parameter K — which, unlike population-level estimates of the basic reproductive number, R0, quantifies heterogeneity across individuals in their capacity to generate secondary cases, consistently suggest that between 10% and 20% of cases are responsible for between 80% and 90% of secondary infections. Individuals generating an unusually high number of secondary infections are thought to have played a pivotal role in SARS-CoV-2 outbreak in many countries. This suggests that if one could identify and protect super-spreaders, the virus may be controlled through focused interventions at lower overall cost.

We consider the possibility that the phenomenon of superspreading in SARS-CoV-2 has a network-structural basis. Some individuals may have jobs, living conditions, or social behavior that generate many more close-range contacts than others. Their status as “hubs” in the network of close-range contacts could render them disproportionately instrumental in viral propagation, as they are both more likely to contract the virus, and once they have it, pass it on to more others. In some cases, these high levels of contact derive from specific roles that these individuals play in an event, e.g., when a waitress or priest transmits a virus through serial dyadic contact. An appreciation of the network structure of close-range interactions within these events would suggest a targeted policy protecting high-contact individuals.

The objective of this paper is to assess the effectiveness of hub targeting versus undifferentiated interventions for controlling SARS-CoV-2 spread in networks with empirically calibrated frequencies of close-range contact. We draw on nationally representative datasets containing information on close-range contacts in various meeting locations and the duration of each contact. Studies have shown that the spreading capacity of seeding hubs may be reduced when networks exhibit high clustering. We also aim to assess whether the effectiveness of hub targeting vis-à-vis undifferentiated intervention on networks with empirically-calibrated degree is stable across different network features for which lack of appropriate data impedes calibration.

Methodology: From the survey data we derive degree distributions for close-range contact on a country scale. We then impose this empirical degree distribution on a synthetic social network with a tunable level of clustering. In this network, we introduce a virus with the main empirical features of SARS-CoV-2, and by an agent-based implementation of a SEIR model, we allow the virus to spread through the network under various transmission conditions. We have designed various ways of reaching the best-connected nodes, and calculate how the trajectory of the epidemic varies under these interventions. From our simulation model, we have derived the hypothesis that interventions — such as vaccinations, medical testing, quarantining-if-positive, protections in high-risk professions, and informational campaigns — would be more effective when targeting hubs rather than random individuals.

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• REFERENCED PAPER