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  • 2 Apr 2019
  • 5:45 PM - 7:00 PM (AEDT)
  • Queensland University of Technology, Gardens Point, J Block, GP-J-212

TIME: 5:45 pm for refreshments followed by talk at 6pm

There will be an annual general meeting held at 5pm prior to the talk

COST: Free

TITLE: Statistical Modelling to Support Successful Mosquito Biocontrol Programs

Dr Dan Pagendam, Senior Research Scientist, CSIRO Data61

ABSTRACT: Aedes aegypti is a mosquito, originating from Africa but now ubiquitous across many of the tropical and subtropical regions of the world. This small insect is responsible for vectoring several life-threatening viruses including Zika, Dengue and Chikungunya, and consequently it is regarded as a serious threat to public health. In recent years, a number of biocontrol strategies have shown great promise for either suppressing Ae. Aegypti populations or blocking the disease vectoring capability of the mosquito completely. A popular suppression strategy is the Sterile Insect Technique (SIT), which involves rearing and releasing large numbers of sterile male mosquitoes into the wild population, effectively reducing the number of viable offspring produced per mated female. Repeated release of sterile male insects has proven to be effective for complete eradication of some insect pests (e.g., the screw-worm fly in North America).

In this talk, I will discuss the role of statistics in the Debug Innisfail project and how stochastic and statistical modelling underpinned a successful mosquito suppression program in Innisfail, Queensland over the summer of 2017/18.  In particular, I will discuss : (i) the use of physically motivated statistical models for mosquito dispersal in urban landscapes; (ii) the use of Markov Population processes for SIT program design; and (iii) how R Shiny can be used as a collaborative tool for geographically isolated teams.

BIOGRAPHY: Dan is a senior research scientist at CSIRO Data61 where he works on problems at the interface of the environmental sciences and statistics.  Dan holds a Bachelor of Environmental Science in Ecology, a Master of Science in Statistics and Doctor of Philosophy in Mathematics and Statistics.  Dan’s main research interests are in stochastic modelling, Bayesian statistics and the use of machine learning methods for model emulation.  In recent years, Dan has been part of multidisciplinary project teams that won the CSIRO Impact from Science medal (2017) and the CSIRO Chairman’s Award (2018).

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