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

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.

Brain Functional Connectivity Scans, Acquired Years Before the Pandemic, Predict COVID-19 Infections in Older Adults: Data From 3,662 Participants (preprint)

BackgroundOur behavioral traits, and subsequent actions, could affect the risk of exposure to the coronavirus disease of 2019 (COVID-19). The current study aimed to determine whether unique brain endophenotypes predict the COVID-19 infection risk. MethodsThis research was conducted using the UK Biobank Resource. Functional magnetic resonance imaging scans acquired before the COVID-19 pandemic in a cohort of general population older adults (n=3,662) were used to compute the whole-brain functional connectomes. A network-informed machine learning approach was used to identify connectome and nodal fingerprints that predicted positive COVID-19 status during the pandemic up to February 4th, 2021. ResultsBrain scans, acquired an average of 3 years before COVID-19 testing, significantly predicted the infection results. The predictive models successfully identified 6 fingerprints that were associated with COVID-19 positive, compared to negative status (all p values < 0.005). Overall, lower integration across the brain modules and increased segregation, as reflected by internal within module connectivity, were associated with higher infection rates. More specifically, COVID-19 infections were predicted by 1) reduced connectivity between the central executive and ventral salience, as well as between the dorsal salience and default mode networks; 2) increased internal connectivity within the default mode, ventral salience, subcortical and sensorimotor networks; and 3) increased connectivity between the ventral salience, subcortical and sensorimotor networks. ConclusionIndividuals are at increased risk of COVID-19 infections if their brain connectome is consistent with reduced connectivity in the top-down attention and executive networks, along with increased internal connectivity in the introspective and instinctive networks.