Project description DEENESFRITPL The next generation of epidemiological models Mathematics are often employed to address biological questions for real-life systems, such as infectious diseases in real-world settings. Epidemiological models utilise empirical data to extract information on the host-pathogen interactions, but the complexity of this interplay poses a challenge. The key objective of the EU-funded COMPLEXDYNAMICS-PHIM project is to develop the simplest models able to address specific public health questions. Researchers are evaluating the role of host-pathogen interactions, the dynamics of vaccination and the control of vector populations. Collectively, the work will help identify the mechanisms underlying the complexity of infectious diseases, offering an important predictive tool for guiding public intervention. Show the project objective Hide the project objective Objective 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. Fields of science medical and health scienceshealth sciencespublic healthmedical and health scienceshealth sciencesinfectious diseasesnatural sciencesmathematicsapplied mathematicsdynamical systemsmedical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugsvaccines Keywords Stochastic processes Chaos Multi-strain dynamics Vaccine implementation Vector control Prediction Parameter estimation Lyapunov exponents Bayesian approach Bifurcation analysis Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2017 - Individual Fellowships Call for proposal H2020-MSCA-IF-2017 See other projects for this call Funding Scheme MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF) Coordinator UNIVERSITA DEGLI STUDI DI TRENTO Net EU contribution € 180 277,20 Address VIA CALEPINA 14 38122 Trento Italy See on map Region Nord-Est Provincia Autonoma di Trento Trento Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 180 277,20
