Abstract

Abstract:


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.
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. Disparate predictions during any outbreak
can hinder intervention planning and response by policy-makers, who may instead
choose to rely on single trusted sources of advice, or on consensus where it
appears. 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. We advocate a more systematic approach, by merging two
well-established research fields. The first element involves formal expert
elicitation methods applied to multiple models to deliberately generate,
retain, and synthesize valuable individual model ideas and share important
insights during group discussions, while minimizing various cognitive biases.
The second element uses a decision-theoretic framework to capture and account
for within- and between-model uncertainty as we evaluate actions in a timely
manner to achieve management objectives.



Resources:

·
Bjørnstad, O.,Shea, K., Krzywinski, M. &
Altman, N. (2020) Modelling Infectious Epidemics.Nature Methods(2020).https://doi.org/10.1038/s41592-020-0822-z

·
Associated
shiny app: https://github.com/martinkrz/posepi1.

·
Outreach
seminar “Disease outbreak control: Harnessing the power of multiple models to
work smarter, not harder”: https://science.psu.edu/frontiers

https://science.sciencemag.org/content/368/6491/577.summary