Multiple infectious diseases with high socioeconomic impacts have significant environmental drivers. Although there is a wealth of earth observation data freely available on the internet, this data is often stored in formats not typically used in epidemiology. Consequently, earth observations are rarely incorporated into disease prediction models to inform planning and mitigation actions.
This 4-day intensive training course aims to introduce several tools to access, collate, visualise and analyse environmental, climatic, health and other ancillary datasets. Participants will learn how such data can be incorporated into early warning prediction models to mitigate the impact of infectious disease epidemics.
The course will be delivered by experts at the School working at the interface of climate prediction science and disease risk forecasting, based between the Centre for Mathematical Modelling of Infectious Diseases and the Centre on Climate Change and Planetary Health. Guest lecturers include Dr Diarmid Campbell-Lendrum (WHO), Dr Madeleine Thomson (Wellcome Trust), Prof Sir Andy Haines (LSHTM) and Prof Antonio Gasparrini (LSHTM).
Lectures and practical sessions will cover fundamentals of environmental and infectious disease interactions; climate services for health; the use of observational, model and forecast climate data at seasonal to decadal time scales; exploratory data analysis; time and space statistical modelling and Bayesian inference.
The objective of this course is to provide participants with the technical skills to access, collate, and analyse climatic and environmental data, and combine these with disease surveillance data using space-time statistical modelling techniques in R and R-INLA.
Participants will gain a working knowledge of accessing and processing large climate databases and using R to formulate and evaluate statistical disease risk models. By the end of the course, participants will be able to:
- access and visualise weather and climate observations, seasonal forecasts and decadal projections.
- formulate statistical regression models using spatio-temporal disease datasets in R.
- understand the principles of Bayesian statistical modelling.
- assess the added value of accounting for climate variation in disease risk models.
Who should apply?
PhD students, early career researchers, data managers, public health and environment agency practitioners working on the impacts of climate on public health. Participants are encouraged to bring specific scientific questions and data to be analysed for participant presentations and 1-2-1 tutorials.
The student is responsible for obtaining any visa or other permissions to attend the course, and is encouraged to start the application process as early as possible as obtaining a visa for the UK can sometimes take a long time. The Short Courses team can provide supporting documentation if requested.
A list of hotels located in the vicinity of LSHTM, along with further resources for short term accommodation, can be found on our accommodation pages.
- If you have been offered a place on the course you will not be able to register without bringing formal ID (Passport) and without having obtained the correct visa if required.
- It is essential that you read the current visa requirements for short course students.
- LSHTM may cancel courses two weeks before the first day of the course if numbers prove insufficient. In those circumstances, course fees will be refunded.
- LSHTM cannot accept responsibility for accommodation, travel and other losses incurred as a result of the course being cancelled.