Understanding infectious diseases by fusing epidemiology, genetics and modelling, 3 ECTS

The course is arranged by the Graduate School for Veterinary Medicine and Animal Science (GS-VMAS) at the Swedish University of Agricultural Sciences and funded by the Nordic Forestry, Veterinary and Agricultural University Network (NOVA) and GS-VMAS.

Course description and learning outcome

The course focusses on the collection, curation, statistical & quantitative genetic analysis of experimental and field data of infectious diseases, and on dynamic prediction models. Infectious disease data are incomplete by its very nature and the course will clearly outline all the challenges related to handling these data and how to make meaningful inferences and prediction models.  The main focus of the course is on farmed animals, but the majority of theory and tools covered can be adapted to plant or human diseases.

The course will provide participants with an understanding of and the ability to apply basic principles of epidemiology, statistical and genetic analyses of infectious disease data and epidemiological modelling. Students will be able to assess the quality and scope of quantitative genetic and epidemiological analyses of disease data and interpret the results of diverse modelling studies.

The maximum number of participants in the course is 20 students. The language of instruction is English.

Target participants

The course is primarily targeted at PhD students in animal or veterinary science, quantitative genetics or epidemiology. However, PhD students, post-docs or other professionals with a strong interest in infectious disease data analyses and mathematical modelling are also encouraged to attend.

Prerequisites

• Admitted to a postgraduate program or actively involved in research in animal science, biology, epidemiology, veterinary medicine or related subjects;
• Experience with using R (at least 2 weeks of course work in R or experience of using R in their own research project).

Course contents and structure

The students should read some introductory course literature (to be provided) before the course week (2 days). The course week will consist of a series of lectures, computational exercises and group projects to get hands-on experience of the topics covered. Students will present results of the group work and will also have the opportunity to present and discuss their own research related to the course. There are plenty of networking opportunities with other students and course teachers throughout the week. 

The course week is structured into 5 complementary (~daily) modules:

• Applied epidemiology (Leads: Tamminen & U. Emanuelson)
• Epidemiological probability modelling & statistical inference (Lead: Britton)
• Infectious disease genetics (Lead: Tsairidou)
• Genetic-epidemiological models (Lead: Doeschl-Wilson)
• Estimation of genetic and non-genetic effects underlying disease spread (Lead: Pooley)

After the course week, students will make an assignment where they relate knowledge from the course to their own research (2 days). The students are also expected to participate in an online workshop (1 day), where they give a 5-10 minutes’ presentation on how to potentially apply the concepts or tools learnt at the course in their own research. This workshop ends the course.

Evaluation and credits

Credits: 3 ECTS. The course will be pass or fail on the basis of successful completion of the course assignments and on active participation in the learning activities, including a post-course synthesis. Students are expected to participate in an online one day event (after the course week), where they give a 5-10 minutes’ presentation on how to potentially apply the concepts or tools learnt at the course in their own research.

Computers and software

Students will need to bring their own laptops for the computational tutorials. We will be working with the most recent stable versions of R and RStudio, as well as with additional course software. Students will need to install R, RStudio with the necessary packages, and the course software on their own computer prior to the course. Detailed instructions will be provided to the registered course participants prior to the course.

Teachers

Prof. Andrea Doeschl-Wilson is professor in infectious disease genetics and mathematical modelling at the University of Edinburgh, UK, and KSLA-Wallenberg guest professor at the Swedish University of Agricultural Sciences (SLU). Her research combines quantitative genetics and epidemiological modelling approaches to reduce infectious disease prevalence and severity in livestock and human populations.

Dr Lena-Mari Tamminen is a veterinary epidemiologist at SLU with particular focus on identifying host, environmental and pathogen-specific components underlying infectious diseases in dairy cattle and other farmed animal species.

Prof. Tom Britton is professor in mathematical statistics (Holder of the Cramér chair) at Stockholm University. His research interests lie in applied probability models and statistical inference for such, in particular epidemic models, networks and applications towards genetics and molecular biology including phylogenetics. He is also a member of S-GEM (Stockholm Group for Epidemic Modeling).

Prof. Ulf Emanuelson is professor in epidemiology at SLU. His research is based on available secondary databases (e.g. disease occurrence), many of which are unique for Sweden, but also on large-scale field studies designed and conducted for specific research projects. His expert area is in analytical epidemiology. Prof. Emanuelson will contribute to the applied epidemiology module.

Dr Smaragda Tsairidou is a Teaching Fellow at the Global Academy of Agriculture and Food Systems of the University of Edinburgh, UK. Smaragda’s research expertise lies in modelling the use of genomics tools for animal breeding and quantitative genetic epidemiology.

Dr Christopher Pooley is a mathematical epidemiologist at Biomathematics & Statistics Scotland (BioSS). Chris’ expertise lies in developing software to estimate epidemiological parameters from experimental and field data.

Registration

Registration fees for NOVA-affiliated participants are 2000 SEK, and 2500 SEK for all other participants. Accommodation and meals during the course are included in the registration. Participants are expected to organise and pay for their own travels to and from the course venue. The course starts with lunch on 12.00h on September 25 until 12.00h 29 September. To register for the course, please use the link: https://www.slu.se/gs-vmas-courses.

Please note that registration does not necessarily guarantee participation. In case the number of applicants exceeds the capacity, priority will be given to students from NOVA universities and based on relevance of the course to the participants’ own research. Applicants are asked to provide a short statement about the relevance of the course for their own research during registration to assist with the selection process.

Deadline

The registration deadline is 12 June. Applicants will be notified by 16 June if their registration was successful.

Accomodation & Food

Students and teachers are housed in single rooms in the Ekenäs Herrgård manor house.  Meals consist of authentical Swedish home cooking.  Students will have access to the lakes and the beautiful peaceful surroundings close by.