Mathematical Modelling

Mathematical modelling plays an increasingly important role in cancer control by helping to identify the most impactful and cost-effective ways to reduce the burden of cancer at a population level. Integrated with epidemiological research, analysis of large-scale health datasets, and evidence reviews, modelling is a powerful tool to inform health policy, programs and practice.

Key areas of focus include in this stream include:

Developing and enhancing Policy1 platforms

Policy1 is a powerful, flexible modelling platform that can be used to evaluate the effectiveness, cost-effectiveness, costs and harms of alternative cancer control strategies. The platform uses a world-leading disease modelling approach, and incorporates information about multiple aspects of cancer and risk behaviour, including:

  • the ‘natural history’ of different cancers i.e., the course a disease takes in individual people from its onset until its detection, and eventual resolution to recovery or death
  • prevention strategies, including vaccination and lifestyle interventions
  • individual risk factors and screening behaviours
  • cancer treatment type and uptake
  • variation in different subgroups of the population.

In addition to existing, mature models, Policy1-Cervix and Policy1-Bowel, the team is building six modelling platforms: Policy1-Prostate, Policy1-Melanoma, Policy1-Ovary, Policy-Lung, Policy1-Breast and Policy1-Lynch

Modelling the impact of COVID-19 on cancer

As key contributors to the COVID-19 and Cancer Global Modelling Consortium the team is developing new models to support decision-making in cancer control both during the pandemic and in recovery. The work includes three key themes of work: impact of COVID-19 infections and cancer treatment delays on cancer outcomes and healthcare systems; the impact on cancer screening and recovery strategies; and the impact on long-term cancer risk and recovery prevention strategies.

Research Team

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