  A pipeline to evaluate the evolution of viral dynamics based on a new
model-driven approach has been developed in the present study. The proposed
methods exploit real data and the multiscale structure of the infection
dynamics to provide robust predictions of the epidemic dynamics. We focus on
viral load kinetics whose dynamical features are typically available in the
symptomatic stage of the infection. Hence, the epidemiological evolution is
obtained by relying on a compartmental approach characterized by a varying
infection rate to estimate early-stage viral load dynamics, of which few data
are available. We test the proposed approach with real data of SARS-CoV-2 viral
load kinetics collected from patients living in an Italian province. The
considered database refers to early-phase infections, whose viral load kinetics
are not affected by mass vaccination policies in Italy. Our contribution is
devoted to provide an effective computational pipeline to evaluate in real time
the evolution of infectivity. Comprehending the factors influencing the in-host
viral dynamics represents a fundamental tool to provide robust public health
strategies. This pilot study could be implemented in further investigations
involving other respiratory viruses, to better clarify the process of viral
dynamics as a preparatory action for future pandemics.
