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New disease challenges, societal demands and better or novel types of data, drive innovations in the structure, formulation and analysis of epidemic models. Innovations in modelling can lead to new insights into epidemic processes and better use of available data, yielding improved disease control and stimulating collection of better data and new data types. Here we identify key challenges for the structure, formulation, analysis and use of mathematical models of pathogen transmission relevant to current and future pandemics. • Disease challenges, societal needs, and new data demand better modelling tools. • To better support ethical public health policy, modelling must adequately account for variation across host populations. • Disease transmission modelling must better account for human behaviour and social structure at multiple scales. • Further challenges include modelling: contact tracing; data collection; pathogen diversity; and within-host dynamics. • Epidemiological modelling must be more open and transparent to engender public trust. [ABSTRACT FROM AUTHOR]

• Mathematical modelling has become prominent in public health decision-making. • We define seven broad challenges on the interface of modelling and policymaking. • Preparedness, collaboration, and communication are essential challenges in pandemic modelling for policy. The COVID-19 pandemic has seen infectious disease modelling at the forefront of government decision-making. Models have been widely used throughout the pandemic to estimate pathogen spread and explore the potential impact of different intervention strategies. Infectious disease modellers and policymakers have worked effectively together, but there are many avenues for progress on this interface. In this paper, we identify and discuss seven broad challenges on the interaction of models and policy for pandemic control. We then conclude with suggestions and recommendations for the future. [ABSTRACT FROM AUTHOR]