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COVID-19 Regional Safety Assessment
30 Countries & Regions
COVID-19 Regional Safety Assessment
Safety Score
COVID-19 Regional Safety Assessment
Regional Top-30 Ranking
A comprehensive and quantitative analysis of the far-reaching global pandemic arising from the novel coronavirus is a critical challenge that must be carried out in order to plan the best strategic measures to reduce and neutralize negative repercussions of the outbreak until the final solution of a vaccine are within the reach of the scientific and medical community. With this in mind, Deep Knowledge Group’s new COVID-19 special analytical case study is designed to classify, analyze and rank the economic, social and health stability achieved by each of the 30 regions included in its analysis, as well as the strengths, weaknesses, opportunities, and threats or risks that they present in the battle against the global health and economic crisis triggered by COVID-19.
The pool of the 30 selected regions is made up of Switzerland, New Zealand, South Korea, Germany, Japan, China, Australia, Austria, Singapore, United Arab Emirates, Israel, Canada, Taiwan, Hong Kong, Norway, Saudi Arabia, Liechtenstein, Iceland, Monaco, Kuwait, Finland, Denmark, Bahrain, Luxembourg, Hungary, Cyprus, Netherlands, Qatar, Andorra, Oman, and more than 130 qualitative and quantitative parametric variables have been developed, tuned, and grouped into 6 broad and top-level categories capable of comprehensively describing the health and economic status of each region in terms of their absolute and relative stability and risks.
It is Deep Knowledge Group’s aim that, regardless of whether the conclusions and recommendations presented in this special analytical case study are adopted wholesale, the present analysis can serve as a starting point for discussion and a resource for governments to optimize current and post-pandemic safety and stability, and as a toolset for establishing the best possible action plans for each particular region, in order to maintain the health and economic well-being of their populations and reverse the collateral damage caused by COVID-19.
COVID-19 Regional Safety Assessment
Ranking Development
Unlike most other COVID-19 analytics which limit themselves to the use of raw data such as fatality rates, Deep Knowledge Group's latest Regional Safety Assessment applies a fundamentally different approach of comparative and correlative analysis that enables strategic decision making and avoids heuristic oversimplification and other misleading practices. We agree with other analytics agencies on many parameters central to the analysis, but also understand that COVID-19 requires a more sophisticated and comprehensive approach. As such our methodology is based on an institutional analysis, which, in summary, embraces six different dimensions of a region’s safety (first layer), each comprised of a distinct set of underlying indicators (second layer), which in turn consists of an additional set of sub-parameters (third layer).
The regions and countries most successful at addressing the challenges of the first wave of the pandemic have been those with the necessary effective national institutional arrangements already in place, while the less successful nations have faced problems with restructuring and rescaling their existing systems for prevention, detection and treatment of disease. COVID-19 has placed an enormous burden on governments and healthcare systems, and has created a need for further improvement of inter-governmental communication for coordinated action. As such the most COVID-resilient regions and countries have been small nations with robust healthcare systems, which, owing to their small scale, have been able to prepare for a rapid growth in emergency activities, successfully avoiding the need to choose between healthcare crisis and economic crisis.
Most of the regions and countries examined in our previous Safety Assessment have successfully enhanced their national policies. Since then however, the pressures on social and economic systems over time mean that a strategic reliance on quarantining is no longer an option. New strategies pertaining to the realities of daily life and daily behaviour, that do not adversely affect the nation's well-being and basic civil liberties, are now required.
COVID-19 Regional Safety Assessment
30 Countries & Regions
COVID-19 Regional Safety Assessment
Methodology
The framework comprises 6 top-level categories (Quarantine Efficiency, Government Efficiency of Risk Management, Monitoring and Detection, Health Readiness, Regional Resilience and Emergency Preparedness).
Each category consists of a matrix of sub-parameters (referred to here as Indicators), which relate to specific factors of importance impacting the stability of current regional circumstances, of the effectiveness of various regions’ emergency response efforts, and these variables will also address post-pandemic planning measures in future studies.
Finally, each indicator itself consists of a matrix of 2-10 quantitative or qualitative sub-parameters, relating to the specific topic, analytical focus and end-point of their parent indicator. Quantitative parameters are numeric, and are obtained from a variety of reputable, publicly available sources of data. Qualitative parameters are binary, and regions are assigned either a 1 or a 0, which represent an answer to a specific yes/no question.
The index utilizes a combination of publicly available databases (including but not limited to indexes and region statistics), as well as manually-curated and researched quantitative and qualitative data obtained by manual searches using search engines, media and governmental reports, and the use of expert opinions and consultations in cases where data was not available.
In utilizing three qualitatively distinct sources of data, Deep Knowledge Group analysts have attempted to overcome barriers in conducting a robust and comprehensive, yet reliable and methodologically-rigorous analysis by utilizing the largest and most reputable databases (usually constructed by an unbiased international group or foundation) where possible, by consulting region-specific resources in cases when open-source international databases are not possible, and finally by utilizing expert opinion in all cases where publicly-accessible regional and/or international sources of data are unavailable.
By utilizing this approach, the present analysis attempts to find an optimal balance between using maximally transparent and reliable sources of data, and including data which are only obtainable from expert consultation.
Geographic Distribution of Key Healthcare Parameters
Deep Knowledge Group’s general methodological approach is to utilize reputable public sources of data as the raw input for specifically-designed analytical frameworks and methodologies that apply specific data and parameter categorization and weighting in order to relevantly and realistically account for importance and impact of different factors, as well as for potential issues with data unreliability.
As such, while the present special analytical case study utilizes a wide variety of public sources of data, the extent with which they are utilized, and the degree with with they are weighted in the report’s overall analytical framework, varies in accordance with these considerations (i.e., with the relative importance and degree of data unavailability or unreliability as determined by Deep Knowledge Group analysts).
The following section presents average geography-specific levels of a number of datapoints and parameters, which serve as a high-level visual overview of various relevant positive and negative factors impacting the COVID-19 resiliency or vulnerability of different regions, including access to basic sanitation facilities, size of elderly population, the prevalence and death rate of specific diseases such as diabetes, obesity, endocrine disorders, tuberculosis, and key healthcare parameters such as density of hospital beds and doctors, and healthcare access and quality index rankings.
In some cases, there are specific reasons to cautiously doubt the official public numbers and records of specific parameters of specific geographic regions, often as a result of the same factors impacting general levels of data unavailability and unreliability associated with particular geographic territories.
In the following pages, any public numbers that Deep Knowledge Group analysts consider potentially divergent with the real status of the region, and which therefore should be taken with some degree of caution, have been marked with an asterisk.
Aging and COVID-19
Deep Knowledge Group's Aging and COVID-19 HeatMap applies data visualization techniques to intuitively display the size of different region's elderly demographic and the prevalence of aging population, with warm colors (yellow, orange, red) representing a high prevalence, and cool colors (variations of blue) representing a low prevalence. Given that COVID-19 disproportionately affects the elderly, both in terms of infection risk, morality and likelihood of developing severe complications, a higher degree of population aging can be considered as a major risk factor for regional safety and stability. Meanwhile, the chart on the right visualizes statistical correlation between the levels of population aging vs. total region-specific deaths, providing a visual approximation of the degree with which population aging impacts total death rate and likelihood of COVID-related mortality.
Endocrine Disorders and COVID-19
Deep Knowledge Endocrine Disorders and COVID-19 HeatMap applies data visualization techniques to intuitively display the size of different region's population-level prevalence of endocrine disorders, with warm colors (yellow, orange, red) representing a high prevalence, and cool colors (variations of blue) representing a low prevalence. A higher prevalence of endocrine disorders can be considered as a major risk factor for regional safety and stability. Meanwhile, the chart on the right visualizes statistical correlation between the levels of endocrine disorders per 100,000 people vs. total region-specific deaths, providing a visual approximation of the degree with which a high prevalence of endocrine disorders affects total death rate and the likelihood of COVID-related mortality.
Obesity and COVID-19
Deep Knowledge Obesity and COVID-19 HeatMap applies data visualization techniques to intuitively display the size of different region's population-level prevalence of obesity (as a percentage of the total population), with warm colors (yellow, orange, red) representing a high prevalence, and cool colors (variations of blue) representing a low prevalence. A higher prevalence of obesity can be considered as a significant risk factor for regional safety and stability. Meanwhile, the chart on the right visualizes statistical correlation between the population-level prevalence of obesity vs. total region-specific deaths, providing a visual approximation of the degree with which a high rate of obesity affects total death rate and likelihood of COVID-related mortality.
Diabetes and COVID-19
Deep Knowledge Diabetes and COVID-19 HeatMap applies data visualization techniques to intuitively display the size of different region's population-level prevalence of diabetes (as a percentage of total population), with warm colors (yellow, orange, red) representing a high prevalence, and cool colors (variations of blue) representing a low prevalence. A higher prevalence of diabetes can be considered as a significant risk factor for regional safety and stability. Meanwhile, the chart on the right visualizes statistical correlation between the population-level prevalence of diabetes vs. total region-specific deaths, providing a visual approximation of the degree with which a high rate of diabetes affects total death rate and likelihood of COVID-related mortality.
Human Development Index and COVID-19
Deep Knowledge Human Development Index and COVID-19 HeatMap applies data visualization techniques to intuitively display the degree of different region's Human Development Index scoring (as of 2018), with warm colors (yellow, orange, red) representing a HDI ranking, and cool colors (variations of blue) representing a low HDI ranking. The Human Development Index is a statistic composite index of life expectancy, education (Literacy Rate, Gross Enrollment Ratio at different levels and Net Attendance Ratio) , and per capita income indicators, which are used to rank countries into four tiers of human development. Thus, regions with a high HDI ranking would be expected to be better equipped both in terms of healthcare efficiency, as well as in terms of economic resiliency, to withstand the negative repercussions of COVID-19 upon public health and safety, as well as economic sustainability, better than regions with a lower HDI score. Meanwhile, the chart on the right visualizes statistical correlation between region-specific HDI scoring vs. total region-specific deaths, providing a visual approximation of the degree with which HDI ranking affects total death rate and likelihood of COVID-related mortality.
Access to basic sanitation facilities and COVID-19
Deep Knowledge Access to Basic Sanitation Facilities and COVID-19 HeatMap applies data visualization techniques to intuitively display the degree of different region's access to basic sanitation facilities, with warm colors (yellow, orange, red) representing a high level of access, and cool colors (variations of blue) representing a low level of access. Given that sanitation is one of the most effective and impactful strategies for preventing COVID_19 community transmission and risk of infection, low access to basic sanitation facilities can be considered as a significant risk factor for regional safety and stability. Meanwhile, the chart on the right visualizes statistical correlation between the population-level access to basic sanitation facilities vs. total region-specific deaths, providing a visual approximation of the degree with which access to sanitation affects total death rate and likelihood of COVID-related mortality.
Tuberculosis and COVID-19
Deep Knowledge Tuberculosis and COVID-19 HeatMap applies data visualization techniques to intuitively display the size of different region's population-level prevalence of tuberculosis (per 100,000 people), with warm colors (yellow, orange, red) representing a high prevalence, and cool colors (variations of blue) representing a low prevalence. Given that tuberculosis has substantial negative affects on the respiratory system, and that the mechanism of action for COVID-19 mortality and lomng-term complications largely acts through the respiratory system, a higher prevalence of tuberculosis can be considered as a significant risk factor for regional safety and stability. Meanwhile, the chart on the right visualizes statistical correlation between the population-level prevalence of tuberculosis vs. total region-specific deaths, providing a visual approximation of the degree with which a high rate of tuberculosis affects total death rate and likelihood of COVID-related mortality.
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