PhD Math (Statistics), QUT.
B AppSc (Hons: Statistics), QUT.
B Math (Statistics), QUT.
B Bus (Accounting, International Business), QUT.
- Geographic information systems (GIS)
- Spatial and spatio-temporal modelling (e.g. disease mapping)
- Data visualisation (e.g. choropleth maps, cartograms, VR and AR, interactive visualisations)
- Bayesian estimation techniques (improvements to existing algorithms, ensemble algorithms)
- Public health and epidemiology
- Environmental sustainability
- Hemeroby (human influence on the natural environment)
- Urban planning
Recent and Current Projects
My most recent project was the development of the Australian Cancer Atlas. The output is a publicly accessible , online, interactive map (think Google Earth) showing estimated incidence and relative survival rates for many different types of cancers across Australia at the small area level (SA2s). This was joint work with the Cancer Council Queensland and the QUT Visualisation and eResearch (ViseR) team to combine our expertise in statistical spatial modelling, data visualisation, and UX and UI design. A screen-capture of the atlas is shown below.
For more information about the Australian Cancer Atlas, please visit the website: http://atlas.cancer.org.au/.
There are many extensions planned for the atlas, including:
- Spatio-temporal modelling to see how patterns of a given cancer change over time.
- Investigating different measures of survival (e.g. loss of life expectancy).
- Ecological modelling (adding additional covariates based on area-specific socio-demographics or clinical characteristics).
- Exploring relationships/clusters between cancers.
- Adding new data sources including estimates for small-area variation in cancer screening.
There are also plans to develop a new spatial model and investigate the issue of over- and under-smoothing. I am currently working on these research areas among developing other outputs and supervising HDR students. The Australian Cancer Atlas team is also continuing to collaborate with FrontierSI and the New Zealand Ministry of Health in providing technical support and consultation in the development of the NZ Cancer Atlas.
Duncan, E. W. 2017. Bayesian approaches to issues arising in spatial modelling. PhD Thesis, Queensland University of Technology. URL: https://eprints.qut.edu.au/112356/1/Earl_Duncan_Thesis.pdf.
Published (most recent first):
- Duncan, E. W., S. M. Cramb, J. F. Aitken, K. L. Mengersen, and P. D. Baade. 2019. Development of the Australian Cancer Atlas: Spatial Modelling, Visualisation, and Reporting of Estimates. International Journal of Health Geographics 18 (21). DOI: 10.1186/s12942-019-0185-9.
- Mengersen, K., E. Duncan, J. Arbel, C. Alston-Knox, and N. White. 2019. “Chapter 15: Applications in Industry”. In Handbook of Mixture Analysis, Handbooks of Modern Statistical Methods Series, edited by S. Früwirth-Schnatter, G. Celeux, and C. P. Robert. Milton: Chapman & Hall/CRC.
- Cramb, S. M., E. W. Duncan, K. L. Mengersen, and P. D. Baade. 2018. Australian Cancer Atlas, small-area incidence: technical report. Brisbane: Cancer Council Queensland and Queensland University of Technology (QUT). (Not publicly available).
- Cramb, S. M., E. W. Duncan, K. L. Mengersen, and P. D. Baade. 2018. Australian Cancer Atlas, small-area survival: technical report. Brisbane: Cancer Council Queensland and Queensland University of Technology (QUT). (Not publicly available).
- Cramb, S. M., E. W. Duncan, P. D. Baade, and K. L. Mengersen. 2018. Investigation of Bayesian spatial models. Brisbane: Cancer Council Queensland and Queensland University of Technology (QUT). URL: https://eprints.qut.edu.au/115590.
- Duncan, E. W., N. M. White, and K. Mengersen. 2017. Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference. International Journal of Health Geographics 16 (47). DOI: 10.1186/s12942-017-0120-x.
- Cramb, S. M., E. W. Duncan, N. M. White, P. D. Baade, and K. L. Mengersen. 2016. Spatial Modelling Methods. Brisbane: Cancer Council Queensland and Queensland University of Technology (QUT).
- Duncan, E. W., N. M. White, and K. Mengersen. 2016. Bayesian spatiotemporal modelling for identifying unusual and unstable trends in mammography utilisation. BMJ Open 6 (5): p.e010253. DOI: 10.1136/bmjopen-2015-010253.
- Pokorny, M. R., M. de Rooij, E. Duncan, F. H. Schröder, R. Parkinson, J. O. Barentsz, and L. C. Thompson. 2014. Prospective Study of Diagnostic Accuracy Comparing Prostate Cancer Detection by Transrectal Ultrasound–Guided Biopsy Versus Magnetic Resonance (MR) Imaging with Subsequent MR-guided Biopsy in Men Without Previous Prostate Biopsies. European Urology 66 (1): 22-29. DOI: 10.1016/j.eururo.2014.03.002.
Under review/ in progress:
- Aswi, A., S. Cramb, E. Duncan, W. Hu, G. White, and K. Mengersen. 2019. Climate variability and dengue fever in Makassar, Indonesia: Bayesian spatio-temporal modelling. Submitted to Spatial and Spatio-temporal Epidemiology; accepted 4 Dec 2019.
- Aswi, A., S. Cramb, E. Duncan, W. Hu, G. White, and K. Mengersen. Bayesian spatial survival models for hospitalisation of dengue: A case study of Wahidin hospital in Makassar Indonesia. Submitted to International Journal of Environmental Research and Public Health on 24 Dec 2019.
- Duncan, E. W., S. M. Cramb, P. D. Baade, K. L. Mengersen, T. Saunders, and J. F. Aitken. 2019. A Practical Guide to Developing a Cancer Atlas using Bayesian Spatial Modelling. Brisbane: Queensland University of Technology (QUT) and Cancer Council Queensland. Available from: TBA.
- Cramb, S., E. W. Duncan, J. Aitken, K. Mengersen, P. Baade. 2019. Geographical patterns in melanoma incidence across Australia: can thickness differentials explain the key drivers? In preparation for submission to TBA.
- Cramb, S., E. W. Duncan, P. D. Baade, and K. Mengersen. “A comparison of Bayesian spatial models for cancer incidence at a small area level: theory and performance”. In Case Studies in Applied Bayesian Data Science, Volume 2259 of the Lecture Notes in Mathematics series, edited by P. Pudlo, C. Robert, and K. Mengersen. Forthcoming early 2020.
- Jahan, F., E. Duncan, S. Cramb, P. Baade, and K. Mengersen. Augmenting disease maps: a Bayesian meta-analysis approach. Submitted to Royal Society Open Science on 9 Dec 2019.
- Duncan, E. W. and K. L. Mengersen. 2019. Comparing Bayesian spatial models in the presence of spatial smoothing: goodness-of-smoothing criteria for assessing under- and over-smoothing. In preparation for submission to TBA.
RPubs and other Guides
- A Guide to Installing R, RStudio, R Packages, and Related Software: RPubs link.
- Computing centroids of areas – bounding box vs centre of gravity methods: (Coming soon)
Link to Earl’s RPubs: http://rpubs.com/Earlien
For a full list of verified peer reviews, please visit my Publons profile.
List of recent reviews:
|July 2018||Risk patterns of lung cancer mortality in Northern Thailand||BMC Public Health||Published
24 Sept 2018
|Sep 2018||Evaluation of Bayesian multiple stage estimation under spatial CAR model variants||Journal of Statistical Computation and Simulation||Published
24 Oct 2018
|Jan 2019||A comparison study on criteria to select the most adequate weighting matrix||Entropy||Published
8 Feb 2019
|Mar 2019||Spatial pattern consistency among different remote-sensing land cover datasets: A case study in Northern Laos||International Journal of Geo-Information||Published
1 May 2019
|July 2019||Mapping time-space brickfield development dynamics in peri-urban area of Dhaka, Bangladesh||International Journal of Geo-Information||Published
11 Oct 2019
|Nov 2019||Spatio-Temporal land-use changes and the response in landscape pattern to hemeroby in a resource-based city||International Journal of Geo-Information||Published
1 Jan 2020
|Dec 2019||Analysis of short-term effects of air pollution on cardiovascular disease using Bayesian spatio-temporal models||International Journal of Environmental Research and Public Health||Under review|
|Dec 2019||Mammography: density equalizing mapping of the global research architecture||Quantitative Imaging in Medicine and Surgery||Under review|
- 18 Aug 2016: Deriving the Full Conditionals
- 20 July 2017: Reversing Label Switching
- 29 March 2018: Cartograms
- 17 Jan 2019: Attempts to Quantify Under- and Over-smoothing (see the slides from the Newcastle University invited talk, 4 Sept 2019, for a more polished version; see also the Bayes on the Beach 2019 poster presentation)
- 7 Nov 2019: MCMC Sampling: creating a generic sampler & comparison of MCMC algorithms (revised 9 January 2020)
- 28 Aug 2019, GeoMed Conference, Glasgow: The Australian Cancer Atlas: mapping reliable small-area estimates of cancer incidence and survival
- 4 Sept 2019, Newcastle University Talk, UK: Comparing spatial models in the presence of spatial smoothing (also presented at the Alan Turing Institute, London (23 Aug), University of Glasgow (30 Aug), and University of Edinburgh (2 Sept))
- 22 Nov 2019, OzViz Conference, Brisbane: Novel visualisations for a digital, interactive cancer atlas
- 16 Dec 2019, Online presentation for Western Australian Dept. of Health (WADoH): Exploring the Australian Cancer Atlas: journey into (previously) uncharted territory
Awards and Special Recognition
- 18 Oct 2019: Asia-Pacific Spatial Excellence Awards for Spatial Enablement (regional Queensland Winner)
- 31 Oct 2019: ACEMS Impact and Engagement Award (awarded to Farzana Jahan, Kerrie Mengersen, Earl Duncan, Susanna Cramb, Dianne Cook, Stephanie Kobakian, and Nicole White).