| Educational guide School of Engineering |
english |
| Biomedical Data Science (2022) |
 Subjects |
| COMPUTATIONAL EPIDEMIOLOGY |
 
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| IDENTIFYING DATA | 2023_24 | |||||||||||||||||
| Subject (*) | COMPUTATIONAL EPIDEMIOLOGY | Code | 17705117 | |||||||||||||||
| Study programme |
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Cycle | 2nd | |||||||||||||||
| Descriptors | Credits | Type | Year | Period | Exam timetables and dates | |||||||||||||
| 4.5 | Compulsory | Second | 1Q |
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| Modality and teaching language | See working groups | |||||||||||||||||
| Prerequisites | ||||||||||||||||||
| Department | Computer Engineering and Mathematics |
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| Coordinator |
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alexandre.arenas@urv.cat piergiorgio.castioni@urv.cat |
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| Lecturers |
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| Web | ||||||||||||||||||
| General description and relevant information |
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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| (*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation. | ||||||||||||||||||
