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

arXiv: https://arxiv.org/abs/2005.08907 · HTML: https://mlsanity.com/paper/2005.08907/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 2 of 2)

- [Cooperating Graph Neural Networks with Deep Reinforcement Learning for Vaccine Prioritization](https://mlsanity.com/paper/2305.05163/) (2023)
- [Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data](https://mlsanity.com/paper/2501.18531/) (2025)

## Abstract (arXiv)

Network scientists have proposed that infectious diseases involving person-to-person transmission may be effectively halted by targeting interventions at a minority of highly connected individuals. Can this strategy be effective in combating a virus partly transmitted in close-range contact, as many believe SARS-CoV-2 to be? Effectiveness critically depends on high between-person variability in the number of close-range contacts. We analyze population survey data showing that indeed the distribution of close-range contacts across individuals is characterized by a small fraction of individuals reporting very high frequencies. Strikingly, we find that the average duration of contact is mostly invariant in the number of contacts, reinforcing the criticality of hubs. We simulate a population embedded in a network with empirically observed contact frequencies. Simulations show that targeting hubs robustly improves containment.