Abstract
In responding to the COVID-19 pandemic, evidence-based policymaking and risk mitigation have been confronted with limited decision-making mechanisms under conditions of increased uncertainty. Such methods are particularly called for in contexts where reliable data to a large extent are missing and where the chosen policy would impact a variety of sectors. In this paper, we present an application of an integrated decision-making framework under ambiguity on how to contain the COVID-19 virus spread from a national policy point of view. The framework was applied in Jordan and considered both local epidemiologic and socioeconomic estimates in a multistakeholder multicriteria context. In particular, the cocreation process for eliciting attitudes, perceptions, and preferences amongst relevant stakeholder groups has often been missing from policy response to the pandemic, even though the containment measures’ efficiency largely depends on their acceptance by the impacted groups. For this, there exist several methods attempting to elicit criteria weights, values, and probabilities ranging from direct rating and point allocation methods to more elaborated ones. To facilitate the elicitation, some of the approaches utilise elicitation methods whereby prospects are ranked using ordinal importance information, while others use cardinal information. Methods are sometimes assessed in case studies or more formally by utilising systematic simulations. Furthermore, the treatment of corresponding methods for the handling of the alternative’s values has sometimes been neglected. We demonstrate in our paper an approach for cardinal ranking in policy decision making in combination with imprecise or incomplete information concerning probabilities, weights, and consequences or alternative values. The results of our cocreation process are aggregated in the evaluation of alternative mitigation measures for Jordan, showcasing how a multistakeholder multicriteria decision mechanism can be employed in current or future challenges of pandemic situations, to facilitate management and mitigation of similar crises in the future, in any region.
Highlights
The COVID-19 pandemic showed many countries’ low preparedness for such crisis events [1]
In deciding which policy measures to adopt, many countries behaved in uncoordinated manners, with several inconsistencies appearing in the disaster risk handling of the COVID-19 pandemic; for instance, different measures were often undertaken in bordering countries or regions with similar 14 day notification rates, and decisions to impose lockdowns were often not determined by the number of confirmed cases alone
Since a detailed analysis of all sectors is beyond the scope of this paper, we evaluate the different effects of these policy measures, both epidemiologically and socio-economically, by looking at their different consequences on criteria such as education, human development, and mental health, and wellbeing
Summary
The COVID-19 pandemic showed many countries’ low preparedness for such crisis events [1]. The framework was first applied in Romania during Q3 and Q4 of 2020 [8] and adapted to Jordan to attest and refine our method to accommodate any context-specific relevant data and stakeholders into COVID-19 decision-making responses and mitigation activities This includes establishing a set of criteria and alternative mitigation measures that could be adopted locally, value estimates on the chosen criteria including modelling the epidemiologic evolution in every alternative scenario, and socioeconomic estimates for several criteria. The resulting integrated multistakeholder and multicriteria framework can be used for better emergency preparedness for the COVID-19 pandemic, as well as for future catastrophe scenarios We recognise that both socioeconomic conditions and medical healthcare capabilities may vary greatly, which will affect the feasibility of certain policy measures in specific regions, as well as affect the quality of data. Because of the significant variety of factors involved, decision frameworks should be able to manage uncertainties, perceptions, their causes, and various preference structures
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