A time-modulated Hawkes process to model the spread of COVID-19 and the impact of countermeasures

TL;DR

This paper introduces a time-modulated Hawkes process model for COVID-19 spread that captures virus-specific features and the effects of countermeasures, enabling scalable analysis of epidemic dynamics and intervention impacts.

Contribution

It presents a novel stochastic epidemic model based on a time-modulated Hawkes process that incorporates virus characteristics and time-varying interventions, improving over traditional models.

Findings

Model effectively captures COVID-19 transmission dynamics.

Analysis of Italian COVID-19 waves reveals impact of mobility restrictions.

Model assesses effectiveness of contact tracing and mass testing.

Abstract

Motivated by the recent outbreak of coronavirus (COVID-19), we propose a stochastic model of epidemic temporal growth and mitigation based on a time-modulated Hawkes process. The model is sufficiently rich to incorporate specific characteristics of the novel coronavirus, to capture the impact of undetected, asymptomatic and super-diffusive individuals, and especially to take into account time-varying counter-measures and detection efforts. Yet, it is simple enough to allow scalable and efficient computation of the temporal evolution of the epidemic, and exploration of what-if scenarios. Compared to traditional compartmental models, our approach allows a more faithful description of virus specific features, such as distributions for the time spent in stages, which is crucial when the time-scale of control (e.g., mobility restrictions) is comparable to the lifetime of a single infection. We apply the model to the first and second wave of COVID-19 in Italy, shedding light into several effects related to mobility restrictions introduced by the government, and to the effectiveness of contact tracing and mass testing performed by the national health service.