Sinking, swimming and surfing- risk communications of uncertainties of the pandemic

Uncertainties in the COVID-19 impact assessment are inevitable. In order to understand various approaches to uncertainty and model implications, they need to holistically analyze. An inherently high level of uncertainty is associated with pandemic assessments derived with parametric approaches. While higher degrees of accuracy may be achieved with data-intensive based models, uncertainties are still associated with each step of the process. In particular, uncertainties exist in direct, indirect and long-term reversible impact estimates. Uncertainty in the development of a pandemic impact can stem from two sources: natural variability (stochastic) and knowledge uncertainty (epistemic).

Science is full of epistemic uncertainty. Circling the unknowns, inching toward truth through argument and experiment is how progress is made. As the world gets back on its feet after sinking for COVID-19 and more and more reliable data comes in the COVID-19 pandemic, risk communication is rapidly becoming a constrained problem. Risk is yet another type of uncertainty, usually pertaining to things in the future that might turnout badly. Risk encompasses the known unknowns and can be calculated with probabilities. Risk governance for COVID-19 needs to provide a balance between risk assessment, risk management, and risk communication.

A framework is established on a (modified) hierarchy and connectivity among six types of differentiated uncertainties including measurement, completeness, inferences, disagreement, credibility and human error. In general, the process used to solve a problem of interest can be described in three stages, where each stage is associated with i) a more advanced state of data processing and ii) one of the six types of uncertainties expressed in the stages. Uncertainties relating to disagreement, credibility, and human error are considered at all three stages which are data acquisition (measurement), data sorting and manipulation (completeness) and data transformation to address objectives (inference).

uncer

More could be found in the detail paper!

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