Decision support system for ranking relevant indicators for reopening strategies following COVID-19 lockdowns

Modeling a decision support system for Covid-19 using systems dynamics and fuzzy inference

Covid-19 has impacted the lives of people across the world with deaths and unprecedented economic damage. Countries have employed various restrictions and lockdowns to slow down the rate of its spread with varying degrees of success. This research aims to propose an optimal strategy for dealing with a pandemic taking the deaths and economy into account. A complete lockdown until vaccination is not suitable as it can destroy the economy, whereas having no restrictions would result in more Covid-19 cases. Therefore, there is a need for a dynamic model which can propose a suitable strategy depending on the economic and health situation. This paper discusses an approach involving a systems dynamics model for evaluating deaths and hospitals and a fuzzy inference system for deciding the strategy for the next time period based on pre-defined rules. We estimated Gross Domestic Product (GDP) as a sum of government spending, investment, consumption, and spending. The resulting hybrid framework aims to attain a balance between health and economy during a pandemic. The results from a 30-week simulation indicate that the model has 2.9 million $ in GDP higher than complete lockdown and 21 fewer deaths compared to a scenario with no restrictions. The model can be used for the decision-making of restriction policies by configuring the fuzzy rules and membership functions. The paper also discusses the possibility of introducing virus variants in the model.

Economic expectations and anxiety during the COVID-19 pandemic: a one-year longitudinal evaluation on Italian university students

The COVID-19 pandemic has produced an extensive aggravation of people's anxiety level. Different policies aimed at fighting the spread of the virus could affect anxiety in various ways. We built an ad hoc web-based survey, administered to the student population of three Italian universities at the beginning of the pandemic and at one year's distance, to collect information on retrospective and current anxiety levels and the underlining reasons. The survey also included questions concerning sociodemographic, economic, labor, lifestyle, academic career, and on-line teaching features, which prevents students from identifying the main survey topic to be anxiety. This research aims at assessing the change in anxiety levels between the analyzed periods and the main determinants of such change, focusing on students' economic expectancies. Results from a Poisson regression model show that anxiety has increased compared to both the pre-pandemic level and the one quantified during the first lockdown. This increase is revealed to be mostly driven by economic and career-related uncertainties, rather than by job loss and proximity to COVID-19. Thus, policymakers should take action to provide certainties both in terms of economic prospects and reopening strategies, especially to avoid that the resulting increase in anxiety translates into an amplified suicide risk.