Future Projections of Climate Change and Health: Advanced Simulation Tools for Public Health Decision Support
This project develops cutting-edge simulation methods for climate change projections and health impacts to guide evidence-based public health interventions. By combining advanced climate-health modeling with epidemiological observation studies, we can forecast health outcomes under different climate scenarios and test strategies before implementation.
Our approach integrates high-resolution climate exposure data with geographical health information, downscaling broad climate projections to targeted communities most at risk. We account for critical factors including population vulnerability, age demographics, individual susceptibility, and changing exposure to climate-related health hazards such as drought, flood, extreme heat, air pollution, and vector-borne diseases.
The research will produce interactive mapping tools that visualise complex climate-health relationships, making sophisticated projections accessible to public health decision-makers. A transformative element involves extending beyond population-level surveillance to incorporate individual health records, enabling unprecedented precision in climate health risk assessment.
The ultimate goal is to provide health and environmental stakeholders with robust, predictive tools to quantify climate health impacts and evaluate proposed adaptation and mitigation strategies, enabling more effective and targeted public health preparedness for our changing climate.
Aim
Primary aim: To enhance the understanding of population health intervention efficacy to identify critical opportunities to mitigate or adapt to environmental health risks through dynamic simulation using high resolution spatial and temporal health and climate data that enables targeted actions in small neighbourhood areas.
Specific aims:
- To facilitate local risk estimates by conducting epidemiological studies using study populations in the same region and period as the impact assessment. This approach enables the estimation of localised health risks associated with different factors, contributing to a more comprehensive understanding of the population’s health.
- To develop and validate high-resolution simulated datasets by creating spatially and temporally downscaled environmental and health data, then linking and validating these simulated datasets with observed small-area health data derived from large cohorts and national databases. This approach ensures both the creation of novel high-resolution data products and the accuracy and reliability of the statistical downscaling techniques.
Objectives
The research has the following objectives:
- Develop and validate environmental health simulation models: The research project will simulate high-resolution health and environmental exposure data, and factors such as population at risk, age, and susceptibility.
- Build a statistical downscaling framework for targeted health impact assessment: The coupled data modelling system will statistically downscale available broad geographical health information to targeted small areas, specifically focusing on highly exposed neighbourhoods, weighted by indicators of vulnerability and risk factors.
- Conduct scenario-based simulations for intervention planning: Scenarios will use the Monte Carlo approach for repeated randomised simulations that are used in tandem with cohort data and registry records linked with patient-level details to quantify health impacts through simulation-based epidemiological analysis. Indicators of impact may include attributable excess absolute risk and measures such as person-years-of-life-lost as a measure of the impact on health outcomes of various unplanned environmental changes or actions to reduce modifiable risk factors.
- Create an integrated simulation-surveillance system with visualisation tool: A key output will be visualisation tools, such as interactive mapping, to aid research translation and demonstrate the components of vulnerability, including population exposure and levels of exposures to decision makers under various synthetic ‘what-if’ scenarios.
Significance
The vision for this PhD study is to build a modelling system supported by simulation technology alongside national monitoring and public health surveillance. This research method will assist developing supercomputing infrastructure and modelling resources that will enable users to access, visualise and analyse data and indicators about the health of Australians in the context of their changing environment. The proposed research is innovative and will deliver significant benefits for Australia’s research community, health managers and policy makers, leading to a significant impact on Australians’ health.
Internship
This project may offer an internship opportunity through its collaboration with the NHMRC Healthy Environments and Lives (HEAL) network, focusing on the health impacts of environmental change and climate.
Ideal Candidate
We are seeking a talented and motivated environmental health scientist to conduct this study. The main skills needed are dynamical simulation modelling and ability to build novel technological applications based on environmental and health statistical frameworks. The successful candidate will be responsible for data collection, building computer workflows, and implementing training frameworks for large-scale models. This project provides an excellent opportunity to gain hands-on experience in the field of machine learning in a practical and applied context relevant to public health while contributing to cutting-edge projects. Must be eligible to enrol in PhD programs at Curtin.
This project is open to Domestic applicants only.
Scholarship
If you are identified as the preferred candidate for this project, you may be considered for an RTP scholarship.
Enquires and How to Apply
For enquires about this opportunity contact Associate Professor Ivan Hanigan via ivan.hanigan@curtin.edu.au
To formally apply submit an Expression of Interest to Associate Professor Ivan Hanigan during the Central Scholarship round (July 1st – July 31st 2026)