ABSTRACT
Intense and frequent changes increase uncertainty and complexity in decision-making. The COVID-19 pandemic exacerbates this situation. Therefore, the decision-maker seeks to reduce risks and meet these challenges. The manuscript aims to identify cause-effect relationships between variables affecting countries and changes caused by the COVID-19 pandemic and propose an algorithm to facilitate decision-making by identifying forgotten effects. The authors use thematic analysis to synthesize the semi-systematic literature review findings. The applied research uses a quantitative approach through modeling and simulation. The results highlight that the pandemic effects are associated with causes such as health care, political and economic stability, social justice, and the level of corruption. Decision-makers must prioritize the management of these variables guided by science. The main contribution is to show an algorithm that identifies forgotten effects in pandemics' socio-economic and health management, preventing future crises. In addition, the study advances the frontier of knowledge by addressing identified gaps and contributes to academia and policy makers. The most critical limitation is the number of variables included in this research. Future investigations could include analyses on the impact of climate change and sustainable development of nations and country-specific studies on the forgotten effects of the COVID-19 pandemic. © 2022 Taylor & Francis Group, LLC.
ABSTRACT
Humanity is experiencing intense and frequent changes caused by the COVID-19 pandemic, which increases uncertainty and complexity in decision-making. New challenges become urgent, mainly to issues related to the aging population, since the coronavirus affects the elderly more than adults. Finding alternatives to preserve the quality of life and work support of the elderly becomes a great challenge for society. In this context, science plays a vital role in proposing new solutions to solve the problems derived from this crisis, being this the primary motivation. The objectives of the article are to learn about the forgotten effects of the pandemic on economically active older adults and to indicate how fuzzy logic can help reduce risks by facilitating decision-making. The main contribution would be to correctly identify its causes and effects, such as the digital gap and job loss by this age group, and to point out corrective measures. The study's methodology is based on applied research, with a quantitative modeling and simulation approach through the Forgotten Effects Theory. The results allow us to predict and act more effectively on problems, seeking to increase the welfare, employability, and life expectancy of the elderly. The study highlights future lines of research on the subject.