In a recently published paper CHDS’ Lyndon James, Nicolas Menzies and colleagues reviewed the benefits and challenges of mathematical modeling as a tool to inform decision making for COVID-19. Since the start of the pandemic, a proliferation of models – often diverging widely in their projections – has been accompanied by criticism of the validity of modeled analyses and uncertainty as to when and to what extent results can be trusted.
Drawing on examples of models from infectious diseases of global importance, the authors identify common limitations that apply to COVID-19. In graphical exhibits, they show that models may produce very different estimates – even when calibrated to the same setting – and that models can be systematically biased. The authors discuss strategies to address these challenges, including model comparison studies, ensemble models, and validating against empirical data. They hope that these approaches will enable better decision making in the current COVID-19 crisis and beyond.
Learn more: Read the publication, The Use and Misuse of Mathematical Modeling for Infectious Disease Policymaking: Lessons for the COVID-19 Pandemic
Learn more: Explore the Resource Pack: Model Calibration and Validation
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