| Guia docent Escola Tècnica Superior d'Enginyeria |
català |
| Ciència de Dades Biomèdiques / Biomedical Data Science (2022) |
 Assignatures |
| EPIDEMIOLOGIA COMPUTACIONAL |
 
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| DADES IDENTIFICATIVES | 2023_24 | |||||||||||||||||
| Assignatura (*) | EPIDEMIOLOGIA COMPUTACIONAL | Codi | 17705117 | |||||||||||||||
| Ensenyament |
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Cicle | 2n | |||||||||||||||
| Descriptors | Crèd. | Tipus | Curs | Període | Horaris i dates d'examen | |||||||||||||
| 4.5 | Obligatòria | Segon | 1Q |
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| Modalitat i llengua d'impartició | Veure grups activitat | |||||||||||||||||
| Prerequisits | ||||||||||||||||||
| Departament | Enginyeria Informàtica i Matemàtiques |
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| Coordinador/a |
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Adreça electrònica | alexandre.arenas@urv.cat piergiorgio.castioni@urv.cat |
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| Professors/es |
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| Web | ||||||||||||||||||
| Descripció general i informació rellevant |
This course on Computational Epidemiology explores the role of computational approaches in understanding and controlling infectious diseases. It begins with historical context, discussing past pandemics and how computational epidemiology has contributed to COVID-19 response. It also outlines what topics won't be covered. The heart of the course is devoted to various modeling techniques. We start with basic definitions of diseases and their types, then delve into compartmental models like the SIR model and its variants. We then study the role of contact networks in disease spread, introducing network definitions and ways to model age structure. We'll examine epidemic spread in social networks through the heterogeneous mean field and the Discrete-time Markov chain approach. Spatial models of disease spread will be discussed, including metapopulation models, continuous models, and mentions of lattice models and cellular automata. The impact of human behavior on disease spread is discussed through the Aware-Unaware model, and potential exploration of game theory models. The course moves to control strategies, discussing vaccination and eradication thresholds, imperfect vaccines, targeted vaccination, and the use of quarantine. Estimation and inference are covered next, focusing on the basic reproduction number (R0). We will discuss how all estimations implicitly assume a model and demonstrate this using real-world examples. Bayesian estimation of Rt will also be addressed. Finally, surveillance methods are explored, including traditional and syndromic surveillance, and surrogate data sources like Twitter, Google Flu Trends, etc. The course concludes with brief discussions of stochastic simulations and agent-based modeling. This course is coordinated by Universitat Rovira i Virgili. |
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| (*)La Guia docent és el document on es visualitza la proposta acadèmica de la URV. Aquest document és públic i no es pot modificar, llevat de casos excepcionals revisats per l'òrgan competent/ o degudament revisats d'acord amb la normativa vigent | ||||||||||||||||||
