Spatial Epidemiology (4560) |
| Language of instruction : English |
| Credits: 6,0 | | | | | Period: semester 1 (6sp) | | | | | 2nd Chance Exam1: Yes | | | | | Final grade2: Numerical |
| | | Exam contract: not possible |
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Sequentiality
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Mandatory sequentiality bound on the level of programme components
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Following programme components must have been included in your study programme in a previous education period
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Concepts of Epidemiology (3567)
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4.0 stptn |
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Introduction of Bayesian Inference (3562)
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4.0 stptn |
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The student should be familiar with statistical inference and statistical models (GLM and mixed models). The student should be familiar with programming in R.
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The geographical representation of the occurrence of a disease and investigation of the relationship between risk of disease and environmental factors are important topics in the analysis of public health. The objective of this course is to give an introduction to the theory and practice of spatial data analysis in the context of disease mapping, geostatistical data and point pattern analysis. The student acquires knowledgde about spatial data modeling. The student can apply these to real data problems, using R and INLA. Topics: Part 1: Introduction - Types of Spatial Data - INLA Part 2: Disease Mapping - Moran's I - Spatial autoregressive models (CAR, BYM, Leroux, BYM2) - Spatio-temporal modeling Part 3: Geostatistical Modelling - Kriging - Model-based geostatistics - SPDE Part 4: Point Pattern Analysis - F, G, K-functions - LGCP models - Preferential sampling
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Period 1 Credits 6,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 30 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | Slides, course notes and project. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | Score for project is carried over to the retake exam. |
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| Compulsory course material |
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All course material (slides, code, papers) will be made available via Blackboard. The R software will be used in this course. |
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Learning outcomes Master of Statistics and Data Science
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- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art software. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | | | - DC
| The student is an effective writer in their own field. | | | - DC
| The student is an effective writer, both within their own field as well as across disciplines. | - EC
| The student is capable of acquiring new knowledge. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
| Offered in | Tolerance3 |
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2nd year Master Bioinformatics
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J
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2nd year Master Biostatistics
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J
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2nd year Master Data Science
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J
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2nd year Master Quantitative Epidemiology
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J
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2nd year Master Quantitative Epidemiology - icp
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J
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Exchange Programme Statistics
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J
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.15.1, section 3. |
3 Education, Examination and Legal Position Regulations art.16.9, section 2.
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