The Assessment of Alternative Interventions to Manage Health Emergencies

Dear colleagues,

The actual health emergency caused by the COVID-19 pandemic has shown how the capability of the health systems to provide a rapid response to unexpected circumstances is critical.

Moreover, the pandemic has shown how the attitude of health organizations to operate on the edge of an efficiency frontier whose bounds mainly rely on current and ordinary activities/operations needs to be revisited to account for physical and human resources to face extraordinary situations. This raises inevitable challenges on the design and implementation of cost–benefit frameworks aimed at evaluating the value for money of health interventions.

In this Special Issue, we aim to present evidence showing how cost–benefit models can be useful tools in order to inform and support the decision-making process and to lead the design and implementation of interventions aimed at preventing and controlling the consequences of health emergencies such as pandemics. More concretely, we present the economics of emergency as the approach aimed at defining how to identify which interventions would be more efficient to be implemented in order to minimize the risk of unexpected circumstances/events. The rationale of the Special Issue is that when dealing with complex decisions, potentially leading to extraordinary circumstances, the first step for a decision maker should be to minimize the sources of uncertainty by means of further research. This would inevitably entail investments and reallocation of resources.

In this Special Issue, we will host scientific contributions showing the effectiveness and efficiency of different tools, interventions, and technologies aimed at minimizing the risk of incurring health emergencies as well as to support decision making when emergencies occur. Applied and methodological contributions, as well as case studies are welcome. Papers showing the potentialities of new analytical tools such as artificial neural networks, big data, and machine learning, as well as papers highlighting the role of different health professions are particularly encouraged.

Prof. Dr. Matteo Ruggeri
Prof. Dr. Carlo Drago
Guest Editors

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