Predictability of viral load dynamics in the early phases of SARS-CoV-2 through a model-based approach

TL;DR

This study introduces a model-based pipeline that leverages real patient data and multiscale infection dynamics to predict early SARS-CoV-2 viral load evolution, aiding understanding of infection progression.

Contribution

The paper presents a novel model-driven approach that combines real data with multiscale infection modeling to predict early viral load dynamics in SARS-CoV-2 infections.

Findings

Successfully predicted early-phase viral load kinetics in SARS-CoV-2 patients.

Validated approach with real data from Italian patients.

Provides insights into infection dynamics before vaccination effects.

Abstract

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 have not been affected by the mass vaccination policies in Italy.