Michael Meehan
- michael.meehan1@jcu.edu.au
https://orcid.org/0000-0003-1332-332X- Senior Lecturer, Biostatistics
- PHTM - Research Methods
Contact Details
- 07 4781 4573
- michael.meehan1@jcu.edu.au
Biography
I am a Senior Lecturer in Biostatistics in Public Health and Tropical Medicine at James Cook University and an applied mathematician specialising in infectious-disease modelling, phylodynamics and statistical inference. My research develops mathematical, statistical and computational methods for understanding pathogen transmission, reconstructing hidden epidemic dynamics and translating complex epidemiological and genomic data into evidence for public-health decision-making.
My research program spans emerging infections, vaccine-preventable diseases, tuberculosis, antimicrobial resistance and pathogens affecting commercial aquaculture. It has been supported by an Australian Research Council Discovery Early Career Researcher Award and commissioned projects for the World Health Organization. My modelling has contributed to Australian vaccination policy and supported decision-making by government, community and international health organisations, including the Queensland Aboriginal and Islander Health Council and health ministries in the Asia-Pacific region.
A major focus of my current work is developing genomic and phylodynamic methods that integrate pathogen sequence data with epidemiological surveillance. These methods aim to reconstruct unobserved transmission, distinguish sustained local spread from repeated importation, correct for incomplete or preferential sampling and improve the targeting of disease-control measures. This work includes the development of reproducible, open-source research software and new statistical methods for birth–death–sampling models, population reconstruction and outbreak inference.
I establish and lead interdisciplinary collaborations connecting mathematical method development with epidemiology, genomic surveillance and public-health practice. My collaborators include researchers and health partners at the University of Oxford, Institut Pasteur, Monash University, CSIRO, the University of Queensland and organisations throughout the Asia-Pacific. I also develop quantitative capability through postgraduate supervision, researcher mentoring, statistical collaboration and applied training in infectious-disease modelling and research methods.
I teach undergraduate research methods and postgraduate biostatistics, with an emphasis on making complex quantitative ideas accessible, professionally relevant and applicable across diverse health disciplines and learning contexts.
Before moving into epidemiology and public health, I completed a PhD in theoretical astrophysics and cosmology at JCU, investigating dark matter in non-standard cosmological scenarios. This interdisciplinary background continues to shape my approach to mathematical modelling, computation and statistical inference.
Research
Research Interests
Infectious-disease modelling — Mathematical and computational models of pathogen transmission, disease progression and intervention impact, with applications to tuberculosis, emerging infections, vaccine-preventable diseases, antimicrobial resistance and pathogens affecting commercial aquaculture.
Genomic epidemiology and phylodynamics — Integration of pathogen genomic, epidemiological and surveillance data to reconstruct transmission histories, distinguish sustained local spread from repeated importation, quantify hidden transmission and support disease-control decisions.
Statistical and computational methods — Bayesian inference, birth–death–sampling models, likelihood-based and simulation methods, parameter identifiability, preferential and incomplete sampling, population reconstruction and open-source software development.
Quantitative public health and decision support — Translation of mathematical and statistical evidence into policy, surveillance and operational decision-making, including vaccine allocation, outbreak response, health-risk forecasting and evaluation of intervention strategies.
Projects
Research Collaborators and Partners
Teaching
Teaching Interests
Statistics and biostatistics — Introductory, intermediate and advanced statistical theory and applications, including probability, estimation, hypothesis testing, regression, analysis of variance, generalised linear models, survival analysis, longitudinal and multilevel methods, Bayesian statistics, statistical modelling and interpretation of quantitative evidence.
Research methods — Study design, research questions and hypotheses, sampling, measurement, bias and confounding, data management, critical appraisal, reproducibility, research integrity and the effective communication of quantitative findings, particularly within health and biomedical research.
Programming and statistical computing — Reproducible data analysis, simulation, visualisation and statistical programming using R and related computational tools, with an emphasis on translating statistical concepts into transparent, efficient and reusable analytical workflows.
Higher Degree Research training — Quantitative research design, statistical reasoning, computational methods, reproducible research, scientific writing and the development of independent research capability among HDR candidates and early-career researchers.
Infectious-disease modelling — Mathematical and computational modelling of infectious-disease transmission, including compartmental models, stochastic simulation, parameter estimation, model calibration, uncertainty analysis and the interpretation of models for epidemiological and public-health decision-making.
Research Advisor Accreditation
Advisor Type
Primary