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P.G.S. and J.G.G. acknowledge financial support from the Departamento de Industria e Innovacion del Gobierno de Aragon y Fondo Social Europeo (FENOL group grant E36-23R) and from Ministerio de Ciencia e Innovacion through project PID2020-113582GB-I00/AEI/10.13039/501100011033. P.G.S. acknowledges financial support from the European Union-NextGenerationEU and Servicio Publico de Empleo Estatal through Programa Investigo 024-67. D.S.P. acknowledges financial support through grants JDC2022-048339-I and PID2021-128005NB-C21 funded by the European Union "NextGenerationEU"/PRTR" and MCIN/AEI/10.13039/501100011033. J.A.R.-V. and R.G.H. acknowledge financial support from Universidad Nacional de Colombia, Centro de Excelencia en Computacion Cientifica, through project HERMES 52625.

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Quantum-Like Approaches Unveil the Intrinsic Limits of Predictability in Compartmental Models

Publicat a:Entropy. 26 (10): 888- - 2024-10-01 26(10), DOI: 10.3390/e26100888

Autors: Rojas-Venegas, Jose Alejandro; Gallarta-Saenz, Pablo; Hurtado, Rafael G; Gomez-Gardenes, Jesus; Soriano-Panos, David

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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.

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