Since 2016, Bath researchers have been supporting Data Science training and research projects in Mongolia. This involves a diverse range of professionals in the public, academic and NGO sectors, mainly concentrated in the capital, Ulaanbaatar. There has been a specific focus on understanding the interaction of public policy and mathematical modelling.
Two main examples include
- Modelling for air pollution and associated health implications: Until recently Ulaanbaatar topped the rankings for the most air-polluted capital city in the world. Smog produced from stoves in yurt dwellings surrounding the city causes a thick blanket to cover the city during the harsh winters. The knock-on effect to respiratory health is severe, particularly for children.
- COVID modelling: Mongolia has a small population, half of which is concentrated in the capital city. Moreover, an estimated 35% of the population is aged 18 or younger. Mongolia’s experience with COVID has been unique with different policy challenges.
- 2015 Stochastic Analysis and Applications Mongolia Project:
Please click here for material from the lectures and talks. Please click here for Uugnaa’s short article on the history of Mongolian mathematics and here as it appeared in Bernoulli news with commentary. Please click here for the final report on the meeting.
- 2016 Data Science workshop with focus on air pollution.
See the article here to find out more about what happened. See also the blog of Robbie Peck.
- 2017 summary report: of the policy and scientific view of the extreme air pollution problems in Ulaanbaatar and a proposed way forward.
- 2018 A course given: on basic statistics to students of NUM and professional data analysts. Lectures, Lab sheets
- 2019 GCRF sponsored workshop “Using data to inform air pollution policy in Ulaanbaatar” for government officials and working scientists to explore how the issues of economic development, migration, and expansion of infrastructure in Mongolia relate to the ongoing challenges of air pollution. See here for the end of grant report.
- 2019-2022 EPSRC GCRF Data-Science capacity building activities