Henley Passport Index and COVID-19 Ranking by DKA
Correlations between the Global Citizenship Program and the Global Residence Program and COVID-19 Regional Safety Assessment
Some of the Parameters
An overview of COVID-19 Analytics by Deep Knowledge Ventures
This 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), whereas each indicator itself consists of a matrix of 2-10 quantitative or qualitative sub-parameters, relating to the specific topic and composed from available databases. They relate to specific important factors 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.
We are only just now emerging from a period of a spontaneous and widespread cessation or reduction of economic activity. Not all devastation wrought on the health and wealth of the global population are seen yet. As behavioural scientists have advised governments worldwide, a population will tolerate a total lockdown for about three months before they come to regard a citizen’s responsibility to hygiene as a matter of public behaviour rather than public seclusion. This is evident in the extent to which even the later, looser lockdown regulations were flaunted in the latter stages of lockdown while public behavior remained more precautious than ever, with social distancing and mask wearing practiced almost ubiquitously even where voluntary. Furthermore, being housebound for months takes a mental, physical and financial toll on people of all age groups.
As such, the peoples and economies of the world simply cannot abide another lockdown any time soon. And yet the health systems of the world cannot withstand a return to normal for as long as the risk of a future spike remains.
As with many historical predicaments, the best route out is technological innovation.
Many nations are locked in a race to develop a coronavirus vaccine before the end of the year. Britain, Germany, the USA, Japan, Israel and China all claim some measure of success, while Russia is claiming to have already approved mass vaccinations for October, bringing things to an alleged state of completion so swiftly that competitors suspect them of cutting corners. International development institutions and inter-governmental bodies and public persons already have made speeches addressed to policymakers warning them to avoid “supply nationalism”. Discrepancies in political matters between nations were put aside in order to concentrate on more collaborative actions aimed to provide treatment and vaccination. The European Commission, which is one of the most active players at the forefront of fighting COVID-19 has committed to bring about cooperation in order to enable not only members of the EU but also the rest of the world, especially less developed countries, to have access to vaccination and supply of other necessary equipment.
In any event, the main hindrance in the race for a vaccine for a novel pandemic is lack of data, which must be sampled from the wild as the crisis progresses and utilised as efficiently as possible in order that the crisis need not proceed a minute longer than is necessary. Much of this data is hard to come by. For example many statistically significant regions, such as the African islands, are hard to sample and the data is slow to arrive.
Metrics indicate that positive indicators of healthcare system efficiency do not necessarily correlate with a positive effect on mortality rates. On the contrary, the correlation is 0.1-0.3, which would mean that more health expenditures coincide with a higher ratio of fatal cases.
Total cases. Having watched the virus rampage from China all the way to the United States and Europe, we are now seeing it creep into other developing countries. And as we will show later, a second wave in Asia is clearly foreseeable. Among the other emerging trends is a broad-scale spread of infection in absolute terms in most of Latin America (especially Brazil), and rapid growth rate of total cases in Africa, where the increase since June is 8.1 times, although Africa’s share is only 5% yet from all cases (which might be attributed to false data as well). The global growth rate of total cases from 1st June to 16th August has been around 3.5 times, whereas in Latin America it was 5.7 times, and in Asia it was 6.9 times (mostly due to India and countries other than China). The most concerns in terms of both speed and scale of pandemic spread amid the weak infrastructure and high population density relate to India. An overall distribution of total cases per region has changed and shows a decrease of the share in Europe (from 34.1% to 16%) and North America (from 30.4% to 25.5%) from the 1st June to 16th August. The Middle East has exhibited slightly lower than average global growth rates. There have been outbreaks in April (possibly due to the onset of a stronger second wave in Asia), but no sharp growth has been recorded there since June (conversely there are signs of much lesser growth in such countries as UAE, Qatar, Saudi Arabia and even Egypt)
New cases (daily). For the period from June 1 to Aug, 16, in terms of an increase in daily cases, the greatest concern is the threat of a second wave in Asia, including the largest growth in Japan (28 times from 35 to 1000+ per day), the Philippines (from 550 to 3340 or 6 times), and a large-scale threat in India (from 7.7 thousand to 57 thousand new cases per day).
The total rate of increase in the number of new cases in Asia per day during this period was more than 4 times (from 17 to 70 thousand new cases per day), while in Latin America, Africa and North America this figure is approximately equal to 2.2-2, 3 times. The growth of new daily cases since the beginning of summer in Asia exceeds Africa and America by 1.8 times, despite the fact that the total number in Asia has become much lower.
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.