Descripción del proyecto
La próxima generación de modelos epidemiológicos
Las matemáticas se utilizan a menudo para abordar cuestiones biológicas en sistemas reales, como las enfermedades infecciosas en entornos reales. Los modelos epidemiológicos utilizan datos empíricos para extraer información sobre las interacciones hospedador-patógeno, pero la complejidad de esta interacción plantea un reto. El objetivo principal del proyecto COMPLEXDYNAMICS-PHIM, financiado con fondos europeos, es desarrollar los modelos más sencillos que sean capaces de abordar cuestiones de salud pública específicas. Los investigadores evalúan la función de las interacciones hospedador-patógeno, la dinámica de la vacunación y el control de las poblaciones de vectores. En conjunto, su trabajo ayudará a identificar los mecanismos subyacentes de la complejidad propia de las enfermedades infecciosas y ofrecerá una importante herramienta de predicción para guiar la intervención pública.
Objetivo
The dynamics of infectious diseases are by nature non-linear and the understanding of such processes is mathematically difficult, demanding concepts from various fields of mathematics tackling biological questions for real life systems. To be descriptive and predictive, models try to include relevant information on the host-pathogen-vector interactions via the available empirical data. These models have shown rich dynamic structures, with bifurcations up to chaotic attractors able to describe large fluctuations observed in real world disease incidence data.
In this project, the origin of the chaotic dynamics in multi-strain epidemiological models will be studied and the mechanisms needed to generate such complex behavior will be identified in basic models, disentangling it from external forcing such as seasonality for example. Multi-strain models will be extended, in collaboration with Prof. Andrea Pugliese (Trento University) providing his experience on vector dynamics, age and space-structured epidemic modeling. The dynamics of vaccine implementation and the control of vector populations, combined with the host-pathogen interactions, will be rigorously evaluated. The over-riding aim of this project is to develop the simplest models able to address specific public health questions, taking into account the chaotic behavior found in such systems, a challenging and new approach.
The developed models will be investigated using innovative methods from dynamical systems theory and stochastic processes, including an ambitious and novel application of a recently developed technique for parameter estimation in such complex systems, a method called maximum likelihood iterated filtering including dynamic noise in likelihood functions for multi-strain dynamics. This proposal requires a highly interdisciplinary approach with results applied well beyond the state-of-the-art.
Ámbito científico
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Régimen de financiación
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
38122 Trento
Italia