Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak

On Ambiguity Reduction and the Role of Decision Analysis during the Pandemic

The COVID-19 pandemic has created a multitude of decision problems for a variety of fields. Questions from the seriousness and breadth of the problem to the effectiveness of proposed mitigation measures have been raised. We assert that the decision sciences have a crucial role to play here, as the questions requiring answers involve complex decision making under both uncertainty and ambiguity. The collection, processing, and analysis of data is critical in providing a useful response-especially as information of fundamental importance to such decision making (base rates and transmission rates) is lacking. We propose that scarce testing resources should be diverted away from confirmatory analysis of symptomatic people, as laboratory diagnosis appears to have little decision value in treatment choice over clinical diagnosis in patients presenting with symptoms. In contrast, the exploratory use of testing resources to reduce ambiguity in estimates of the base rate of infection appears to have significant value and great practical import for public policy purposes. As these stances may be at odds with triage practices among medical practitioners, they highlight the important role the decision analyst can play in responding to the challenges of the COVID-19 pandemic.