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

Andrea Bondesan; Antonio Piralla; Elena Ballante; Antonino Maria Guglielmo Pitrolo; Silvia Figini; Fausto Baldanti; Mattia Zanella; Andrea Bondesan; Antonio Piralla; Elena Ballante; Antonino Maria Guglielmo Pitrolo; Silvia Figini; Fausto Baldanti; Mattia Zanella

doi:10.3934/mbe.2025027

Mathematical Biosciences and Engineering

2025, Volume 22, Issue 4: 725-743. doi: 10.3934/mbe.2025027

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

1.
Department of Mathematical, Physical and Computer Sciences, University of Parma, Parma, Italy
2.
Microbiology and Virology Department, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
3.
Department of Political and Social Sciences, University of Pavia, Pavia, Italy
4.
Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
5.
Department of Mathematics "F. Casorati", University of Pavia, Pavia, Italy

Received: 05 November 2024 Revised: 13 February 2025 Accepted: 20 February 2025 Published: 03 March 2025

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 focused on viral load kinetics whose dynamical features are typically available in the symptomatic stage of the infection. Hence, the epidemiological evolution was 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 tested 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.
- mathematical epidemiology,
- SARS-CoV-2 viral load,
- SIR with age of infection,
- uncertainty quantification
Citation: Andrea Bondesan, Antonio Piralla, Elena Ballante, Antonino Maria Guglielmo Pitrolo, Silvia Figini, Fausto Baldanti, Mattia Zanella. Predictability of viral load dynamics in the early phases of SARS-CoV-2 through a model-based approach[J]. Mathematical Biosciences and Engineering, 2025, 22(4): 725-743. doi: 10.3934/mbe.2025027

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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 focused on viral load kinetics whose dynamical features are typically available in the symptomatic stage of the infection. Hence, the epidemiological evolution was 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 tested 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.

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