Molecular Communications at the Macroscale: A Novel Framework for Modeling Epidemic Spreading and Mitigation

TL;DR

This paper introduces a novel framework that models epidemic spreading as macroscale molecular communications, enabling better understanding and mitigation strategies through communication theory concepts.

Contribution

It establishes a new communication-inspired model for epidemics, mapping disease transmission to information transfer, and introduces metrics for analyzing spreading dynamics.

Findings

Effective distance simplifies epidemic wave modeling.

New metrics characterize transmission paths and delays.

Interventions can be understood through communication modules.

Abstract

Using the notion of effective distance proposed by Brockmann and Helbing, complex spatiotemporal processes of epidemic spreading can be reduced to circular wave propagation patterns with well-defined wavefronts. This hidden homogeneity of contagion phenomena enables the mapping of virtual mobility networks to physical propagation channels. Subsequently, we propose a novel communications-inspired model of epidemic spreading and mitigation by establishing the one-to-one correspondence between the essential components comprising information and disease transmissions. The epidemic processes can be regarded as macroscale molecular communications, in which individuals are macroscale information molecules carrying messages (epidemiological states). We then present the notions of normalized ensemble-average prevalence (NEAP) and prevalence delay profile (PDP) to characterize the relative impact and time difference of all the spreading paths, which are analogous to the classical description methods of path loss and power delay profile in communications. Furthermore, we introduce the metric of root mean square (RMS) delay spread to measure the distortion of early contagion dynamics caused by multiple infection transmission routes. In addition, we show how social and medical interventions can be understood from the perspectives of various communication modules. The proposed framework provides an intuitive, coherent, and efficient approach for characterization of the disease outbreaks by applying the deep-rooted communications theories as the analytical lens.