A Framework for Reconstructing COVID-19 Transmission Network to Inform Betweenness Centrality-Based Control Measures

TL;DR

This paper introduces a framework that models COVID-19 transmission as a dynamic network, enabling targeted control measures based on network centrality to better contain the spread.

Contribution

It develops a novel autoregressive model decomposing infection sources and identifies the transmission network for targeted interventions.

Findings

Network analysis reveals key nodes for intervention

Control strategies based on centrality improve containment

The framework adapts to evolving transmission dynamics

Abstract

In this paper, we propose a general framework for optimal control measures, which follows the evolution of COVID-19 infection counts collected by Surveillance Units on a country level. We employ an autoregressive model that allows to decompose the mean number of infections into three components that describe: intra-locality infections, inter-locality infections, and infections from other sources such as travelers arriving to a country from abroad. We identify the inter-locality term as a time-evolving network and when it drives the dynamics of the disease we focus on its properties. Tools from network analysis are then employed to get insight into its topology. Building on this, and particularly on the centrality of the nodes of the identified network, a strategy for intervention and disease control is devised.