Dr. Su Kang

Postdoctoral Researcher


Dr Su Yun Kang is currently a Postdoctoral Computational Scientist in disease modelling in the Big Data Intitute at the University of Oxford as part of the Malaria Atlas Project. Her current role is to undertake research at the interface of malaria epidemiology, malaria spatial dynamics and control, statistical analysis, algorithm development, and policy.

Su obtained her PhD in Statistics from Queensland University of Technology (QUT), which focused on the investigation of the suitable spatial and spatio-temporal scales for modelling of cancer incidence in Queensland, Australia using sophisticated Bayesian hierarchical models. Prior to joining SEEG, Su was a Research Associate at QUT working on the Great Barrier Reef monitoring program. Here adaptive monitoring methods for spatio-temporal data were developed to improve the long-term health of the Great Barrier Reef at minimum cost with maximum efficiency.

Her current research interests include spatial and spatio-temporal disease modelling, infectious disease epidemiology, Bayesian methodologies, and stochastic processes.

Su left the MAP group in the summer of 2018 to live in Australia.

Selected Biography

URLDOIKang SY., Battle KE., Gibson HS., Ratsimbasoa A., Randrianarivelojosia, M, Ramboarina S., Zimmerman PA., Weiss DJ., Cameron E., Gething PW., Howes RE.,

Spatio-temporal mapping of Madagascar’s Malaria Indicator Survey results to assess Plasmodium falciparum endemicity trends between 2011 and 2016

BMC Medicine. May 2018 16 71.
DOIKang SY., McGree J., Drovandi CC., Caley MJ., Mengersen K.,

Bayesian adaptive design: improving the effectiveness of monitoring of the Great Barrier Reef

Ecological Applications. December 2016 26(8): 2637–2648.
URLDOIKang SY., Cramb S., White N., Ball SJ., Mengersen K.,

Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data

Geospatial Health. May 2016 11(2): 428.
URLDOIKang SY., McGree J., Baade P., Mengersen K.,

A case study for modelling cancer incidence using bayesian spatio-temporal models

Australian & New Zealand Journal of Statistics. 2015 57(3): 325–345.
URLKang SY.,

Bayesian models for spatio-temporal assessment of disease

Bulletin of the Australian Mathematical Society. 2015 91(03): 516–518.
URLKang SY., McGree J., Mengersen K.,

Bayesian hierarchical models for analysing spatial point-based data at a grid level: a comparison of approaches

Environmental and Ecological Statistics. 2015 22(2): 297–327.
URLKang SY., McGree J., Baade P., Mengersen K.,

An investigation of the impact of various geographical scales for the specification of spatial dependence

Journal of Applied Statistics. 2014 41(11): 2515–2538.
URLDOIKang SY., McGree J., Mengersen K.,

The choice of spatial scales and spatial smoothness priors for various spatial patterns

Spatial and spatio-temporal epidemiology. 2014 10 11–26.
URLDOIKang SY., McGree J., Mengersen K.,

The impact of spatial scales and spatial smoothing on the outcome of bayesian spatial model

PLoS ONE. October 2013 8(10): e75957.
URLKang SY., McGree J., Mengersen K.,

The impact of spatial scales on discretised spatial point patterns

20th International Congress on Modelling and Simulation, Adelaide, Australia. 2013