ABSTRACT
The COVID-19 impacts go beyond healthcare systems as they also challenge global markets and society. A comprehensive knowledge involving the elements to contain the virus is fundamental for properly planning and implementing a quick response to the problems faced worldwide. Learning to coexist with the COVID-19 pandemic has become part of our daily life. Hence, the scientific community’s capabilities to continuously provide solutions for pandemics are crucial to mitigate the spread of the pandemic. The main contribution of this work is to propose applications of advanced analytics (AA) in healthcare treatment networks that predict epidemiology curves and the distribution of patients’ severity towards. These tools assist the optimization of such networks with innovative solutions aiming to increase the capacity, responsiveness, and preparedness of the infrastructure and management in healthcare systems. Such a decision-making environment can forecast the spread of the disease by utilizing given inputs such as social distance, out-of-stock of personal protective equipment (PPE) items, lockdown policies, environmental factors, etc. These forecasts are especially important to allow a) medical corporations to design and operate healthcare treatment systems and b) governments to develop policies aiming to maintain the balance between social progress and a sustainable economy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Academia is an arena where practitioners from industry are integrated to theoreticians. Such alliance has been intensified by the industry 4.0 (I4) age from which these counterparts are seeking to merge efforts towards society 5.0 (S5), enabling next generations to easily accept novelties and changes in well-established operations, process-of-work, behaviours, etc. In educational centres, such a pace into the I4-S5 state pushes new ways of adopting (or adapting current) sharing of work among peers since this may potentially become a tool for an efficient process-of-research. Thus, we particularly cover postgraduate centres with part-time (PT) and full-time (FT) students in the fields of process system engineering (PSE) and we are widely relying on computer aided process engineering (CAPE) tools, algorithms, software, packages, etc. A collaborative research and development of PSE-CAPE systems may a) involve PT and FT postgraduates in multi-disciplinary fields of science and engineering and b) go across physics, math, and technologies to include social sciences, public policies, and beyond. The proposition is to analyse PT-FT synergies considering their experiences, accessibility of data to validate models, viability to handle CAPE tools, etc. An example of collaboration between PT and FT students, involving a university, a research center, a consulting company, and a medical corporation, is highlighted to optimise healthcare treatment systems for social progress and sustainable development amid COVID-19. © 2021 Elsevier B.V.