We advocate a more systematic approach, by merging two well-established research fields.
Thus, valuable insights and information from other models may be overlooked, limiting the opportunity for decision-makers to account for risk and uncertainty and resulting in more lives lost or resources used than necessary. Disparate predictions during any outbreak can hinder intervention planning and response by policy-makers ( 2, 3), who may instead choose to rely on single trusted sources of advice, or on consensus where it appears.
Such models can often differ substantially in their projections and recommendations, reflecting different policy assumptions and objectives, as well as scientific, logistical, and other uncertainty about biological and management processes ( 1). The coronavirus disease 2019 (COVID-19) pandemic has triggered efforts by multiple modeling groups to forecast disease trajectory, assess interventions, and improve understanding of the pathogen.
