| Language of instruction : English |
| | | Exam contract: not possible |
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Sequentiality
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No sequentiality
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Change the language, year or choose another item in the dropdown list if it is available.
| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | second year Quantitative Epidemiology - distance learning | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
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| | | Learning outcomes |
- 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. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student is able to efficiently acquire, store and process data. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student has the habit to assess data quality and integrity. | - EC
| The student knows the international nature of the field of statistical science and data science. | - EC
| The student knows the societal relevance of statistics and data science. | - EC
| The student knows the ethical, moral, legal, policy making, and privacy context of statistics and data science, and always acts accordingly. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | - EC
| The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The pervasiveness of the Web and mobile technologies as well as the growing adoption of smart wearable sensors have significantly changed the landscape of epidemic intelligence data gathering with an unprecedented impact on global public health. The digital traces generated by a large number of individuals interacting with Web and mobile technologies as well as wearable sensors contain epidemiological indicators that are not easily accessed with traditional approaches. Collectively, these digital sources largely enhance the capabilities of epidemiology and global public health. Indeed, since more than a decade, the growing field of digital epidemiology (https://www.ncbi.nlm.nih.gov/pubmed/?term=29302758) has been using digital data generated outside the public health system to carry out epidemiological studies on an unprecedented scale and with an unprecedented precision.
The goal of this course is to provide an overview of the main success stories collected during the first ten years of this young and exciting field. During the course, the students will be guided through popular methodologies and approaches in digital epidemiology with a focus on a few case studies with a hands-on approach.
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Collective feedback moment ✔
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Distance learning ✔
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Project ✔
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Period 2 Credits 3,00
| Evaluation method | |
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| Other evaluation method during teaching period | 60 % |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Recommended course material |
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A list of recommended reading papers will be communicated on Blackboard. |
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| second year Master Biostatistics - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| second year Data Science - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- 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. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student is able to efficiently acquire, store and process data. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student has the habit to assess data quality and integrity. | - EC
| The student knows the international nature of the field of statistical science and data science. | - EC
| The student knows the societal relevance of statistics and data science. | - EC
| The student knows the ethical, moral, legal, policy making, and privacy context of statistics and data science, and always acts accordingly. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | - EC
| The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
The pervasiveness of the Web and mobile technologies as well as the growing adoption of smart wearable sensors have significantly changed the landscape of epidemic intelligence data gathering with an unprecedented impact on global public health. The digital traces generated by a large number of individuals interacting with Web and mobile technologies as well as wearable sensors contain epidemiological indicators that are not easily accessed with traditional approaches. Collectively, these digital sources largely enhance the capabilities of epidemiology and global public health. Indeed, since more than a decade, the growing field of digital epidemiology (https://www.ncbi.nlm.nih.gov/pubmed/?term=29302758) has been using digital data generated outside the public health system to carry out epidemiological studies on an unprecedented scale and with an unprecedented precision.
The goal of this course is to provide an overview of the main success stories collected during the first ten years of this young and exciting field. During the course, the students will be guided through popular methodologies and approaches in digital epidemiology with a focus on a few case studies with a hands-on approach.
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Collective feedback moment ✔
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Distance learning ✔
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Project ✔
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Period 2 Credits 3,00
| Evaluation method | |
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| Other evaluation method during teaching period | 60 % |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Recommended course material |
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A list of recommended reading papers will be communicated on Blackboard. |
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1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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