Deep Uncertainty 101 for infectious disease modelers: what it is, why you should care and what we can do about it

The MIDAS Webinar Series features research by MIDAS members, and is open to the public.

Date: Friday, January 27th, 2023

Topic: Deep Uncertainty 101 for infectious disease modelers: what it is, why you should care and what we can do about it

Speaker: Dr. Pedro Nascimento de Lima, RAND Corporation

Abstract: The COVID-19 pandemic showed how valuable infectious disease (ID) modeling is but also demonstrated how informing decision-making with ID models can be challenging. As we respond to a pandemic in real-time, modelers face many puzzles, including integrating evolving scientific knowledge with incoming (and incomplete) data while acknowledging ever-changing biological and behavioral uncertainties. Further, unclear societal goals prevent modelers from making strong normative claims, since there is little consensus on how to weigh potentially conflicting goals – such as protecting healthcare systems, maximizing life, keeping kids at school, minimizing unemployment and going on with “normal” life. Nevertheless, pandemics do not wait for uncertainty or value conflicts to resolve. A result of this struggle is a massive and still increasing death toll and lack of consensus about how to manage complex decisions – such as whether and how to introduce and manage salient public health interventions, like lockdowns.

Although these challenges are daunting, they are not new. Deep Uncertainty – i.e., lack of knowledge about past or future events and consensus around values – exists in many decision-analytic policy areas. From climate change to warfighting, decision-makers always had to act in the face of the unknown. Given these challenges, researchers from fields plagued by deep uncertainty have proposed several decision-analytic approaches, collectively known as Decision Making Under Deep Uncertainty (DMDU) methods. This talk offers a brief introduction to this field for infectious disease modelers and an invitation for modelers to engage with DMDU ideas.

Bio: Pedro Nascimento de Lima is an Associate Engineer at the RAND Corporation. His research leverages simulation modeling, high-performance computing, and Decision Under Deep Uncertainty methods to inform health policy decisions. He has a B.S. and an M.S. in production engineering from UNISINOS University in Brazil, and a Ph.D. in policy analysis from Pardee RAND Graduate School. He was also a member of the MIDAS student committee during from 2020 through his graduation in 2022.