The Frailty of the Invincible

Abstract

The COVID-19 pandemic has unveiled the frailty of our societies from too many points of view to look away. We need to understand why we were all caught unprepared. On the one hand, we have all short memories. As we forget too quickly, we were unable to recognize key factors influencing response and preparedness to public health threats. For many years, economic evaluation pushed governments all over the world to cut resources for public health systems, with COVID-19 pandemic the question arises: do we spend too much or too little on health care? What is the right amount to spend on health? Moreover, in many countries, the privatisation, or semi-privatisation, of healthcare may give rise to inequitable access to health care for everyone. Although COVID-19 is very "democratic", its consequences aren't. According to OECD, income inequality in OECD countries is at its highest level for the past half century. Three main causes have been recognized, technological revolution, globalization, and "financialisation". In this scenario, lockdown measures adopted to save lives are showing dramatic economic consequences. To address post COVID-19 reconstruction we need to go beyond GDP. As an economic measure this has many shortcomings in describing the real well-being of a country, and since what we measure affects what we do, new paradigms will have to guide the post COVID-19 reconstruction strategies, as the fate of countries and their citizens is at stake.

Evaluating Similarities and Differences between Machine Learning and Traditional Statistical Modeling in Healthcare Analytics

“…Unfortunately, many of these applications resulted in inaccurate or irrelevant research results, as proper research protocols were not fully followed [2]. On the other hand, statistics has been the basis of analysis in healthcare research for decades, especially, in the areas of clinical trials and health economics and outcomes research (HEOR), where the precision and accuracy of analyses have been the primary objectives [3]. Furthermore, the classical statistics methodologies are often preferred in those research areas to ensure the ability to replicate and defend the results and ultimately, the ability to publish the research content in peer-reviewed medical journals [3].…”

“…On the other hand, statistics has been the basis of analysis in healthcare research for decades, especially, in the areas of clinical trials and health economics and outcomes research (HEOR), where the precision and accuracy of analyses have been the primary objectives [3]. Furthermore, the classical statistics methodologies are often preferred in those research areas to ensure the ability to replicate and defend the results and ultimately, the ability to publish the research content in peer-reviewed medical journals [3]. The increased availability of data, including data from wearables, provided the opportunity to apply a variety of analytical techniques and methodologies to identify patterns, often hidden, that could help with optimization of healthcare access as well as diagnostic and treatment process [4].…”