| Educational guide School of Engineering |
english |
| Biomedical Data Science (2022) |
 Subjects |
| COMPUTATIONAL EPIDEMIOLOGY |
| Contents |
| IDENTIFYING DATA | 2023_24 |
| Subject | COMPUTATIONAL EPIDEMIOLOGY | Code | 17705117 | |||||
| Study programme |
|
Cycle | 2nd | |||||
| Descriptors | Credits | Type | Year | Period | ||||
| 4.5 | Compulsory | Second | 1Q |
|||||
| Competences | Learning outcomes | Contents |
| Planning | Methodologies | Personalized attention |
| Assessment | Sources of information | Recommendations |
| Topic | Sub-topic |
| 1. HISTORICAL BACKGROUND: | 1.1. Overview of Past Pandemics 1.2. The Role of Computational Epidemiology in COVID-19 1.3. Course Outline |
| II. MODELING EPIDEMICS | 2.1. Basics of Epidemiology Modeling 2.2. Contact Networks in Epidemics 2.3. Spatial Models in Epidemics 2.4. The Impact of Human Behavior on Epidemics |
| III. CONTROLLING EPIDEMICS |
3.1. Vaccination Strategies 3.2. Isolation and Contact Tracing 3.3. The Role of Quarantine in Controlling Epidemics |
| IV. INFERENCE AND ESTIMATION |
4.1. Understanding The Basic Reproduction Number (R0) 4.2. Implicit Assumptions in Estimation Models 4.3. Bayesian Estimation of Rt |
| V. SURVEILLANCE |
5.1. Traditional vs Syndromic Surveillance 5.2. Surrogate Data Sources for Surveillance 5.3. Pros and Cons of Different Surveillance Strategies |
