Table.

Recommendations for successful public health modelling efforts

	Specific actions
Integrate modelling into decision making in the early stages of the outbreak response	Develop models with succinct, actionable outputs (eg, estimates of health-care system needs under various intervention strategies; risk of infection or death for key populations) that address the specific needs of policy makers and stakeholders; continually refine models to address the changing needs of policy makers and stakeholders and to incorporate new data or knowledge about disease transmission. Clearly communicate to policy makers and stakeholders any changes to model and consequences of these changes
Integrate data in both construction and evaluation stages	Evaluate and constrain models with empirical data. At minimum, ensure that the results are consistent with the observed data. Ideally, use data to quantify the uncertainty or bias in model performance and to improve model accuracy via advanced fitting methods; incorporate uncertainty in the data, including report processes if possible, into modelling results. Consider how biases in the data would propagate through to model outcomes
Ensure reproducible and transparent modelling practices	Make model code publicly accessible and easily reproducible; publish results publicly (eg, through preprint servers); where available, participate in modelling consortia or other collaborative modelling efforts to confront and explore assumptions in model structure and data uncertainty; share modelling results in accessible ways to appropriate audiences. Pay special attention to communicating the assumptions and uncertainties in modelling results
Contextualise modelling results	Consider the specific context in which policy recommendations are to be made; adapt models to cover the population of interest, transmission patterns, or behaviours, and potential interventions being considered; explicitly state assumptions and possible biases underlying modelling results. Clarify the heterogeneities and questions that the model can address and the heterogeneities that the model ignores or simplifies; present modelling results with an appropriate degree of uncertainty, and, if applicable, over a relevant time frame