| Autor: |
Rojas-Venegas JA; Departamento Administrativo Nacional de Estadística (DANE), Bogotá 111321, Colombia.; Departamento de Física, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia., Gallarta-Sáenz P; Departamento de Física de la Materia de Condensada, Universidad de Zaragoza, 50009 Zaragoza, Spain.; GOTHAM Lab, Instituto de Biocomputación y Sistemas Complejos (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain., Hurtado RG; Departamento de Física, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia., Gómez-Gardeñes J; Departamento de Física de la Materia de Condensada, Universidad de Zaragoza, 50009 Zaragoza, Spain.; GOTHAM Lab, Instituto de Biocomputación y Sistemas Complejos (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain., Soriano-Paños D; GOTHAM Lab, Instituto de Biocomputación y Sistemas Complejos (BIFI), Universidad de Zaragoza, 50018 Zaragoza, Spain.; Departament d'Enginyería Informática i Matemátiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain. |
| Abstrakt: |
Obtaining accurate forecasts for the evolution of epidemic outbreaks from deterministic compartmental models represents a major theoretical challenge. Recently, it has been shown that these models typically exhibit trajectory degeneracy, as different sets of epidemiological parameters yield comparable predictions at early stages of the outbreak but disparate future epidemic scenarios. In this study, we use the Doi-Peliti approach and extend the classical deterministic compartmental models to a quantum-like formalism to explore whether the uncertainty of epidemic forecasts is also shaped by the stochastic nature of epidemic processes. This approach allows us to obtain a probabilistic ensemble of trajectories, revealing that epidemic uncertainty is not uniform across time, being maximal around the epidemic peak and vanishing at both early and very late stages of the outbreak. Therefore, our results show that, independently of the models' complexity, the stochasticity of contagion and recovery processes poses a natural constraint for the uncertainty of epidemic forecasts. |
