Covid-19 Global Analysis

Paras Gupta, IIT Gandhinagar, gupta.paras@iitgn.ac.in

Krishnam Hasija, IIT Gandhinagar, krishnam.h@iitgn.ac.in

Shreyshi Singh, IIT Gandhinagar, shreyshi.s@iitgn.ac.in

Kanishk Singh, IIT Gandhinagar, kanishk.s@iitgn.ac.in

Repo

The unexpected onset of the deadly COVID-19 pandemic stirred up chaos in the entire world as it rapidly spread to every corner of the world. Vigilant and proactive countries could slow their growth and decrease the damage done, while countries with incompetent governments and inadequate measures suffered miserably. The economic status of a country is one of the best indicators of how it would react to a crisis. Quality of life improvements such as frequent sanitization, social distancing, and distribution of masks are effective ways to at least slow the spread of a virus, but not barred to it. As this study will show, there are many outliers to the expectations; economically-well-off countries buckled under the virus because they couldn’t enforce strong enough measures quick enough, or because the people didn’t trust their governments enough to follow protocol; small, underdeveloped countries with poor living conditions and a population not privy with a pandemic could stifle the growth of the virus with appropriate measures. In the end, the only hopeful solution to the pandemic was the rollout of effective vaccines.

Our dataset consists of a total of 67 metrics spread across various topics relating to the pandemic such as vaccinations, tests and positivity, hospital and ICU, confirmed cases, confirmed deaths, policy responses etc. It has data from the beginning of 2020 to April 2022 for over 180 countries and is also being regularly updated. The information is collected from official sources around the world and the dataset can be found at https://github.com/owid/covid-19-data/tree/master/public/data.

Fig. 1 - GDP per Capita vs Population density for all the countries grouped by their respective continents.The size of the graoh marker represent the Total Cases Per Million Population for that particular country.

Starting at the top, with the countries grouped under their respective continents (Fig. 1), we can see in Fig. 2.1 and 2.2 that Europe leads in average case density, taking up 39.3% of the graph, and is a major contributor in death density with 32.3%. Europe also has the highest GDP per capita (30.7%) and a lower population density (13.4%). The low population essentially assuages the high case and death numbers, but the GDP suggests that these well-faring countries were underprepared for the pandemic. Next up in case density, is South America (16.2%), whilst having the highest death density (33.7%), GDP per capita spanning 12.7% of the graph and the lowest population density (2.05%). The difference between the case and death density shows a lack of proper medical facilities, which is also reflected by the average GDP, but the low population density can be considered a good measure from a utilitarian point of view. North America is close behind with a case density taking up 15.9% of the graph and death density taking up 18.1%. Its population density (21.3%) presents a problem if seen with respect to the death density. Similarly, Asia has a low death density (9%) but its high population density (49.8%) negates what should have been a good value. Evidently, these measures aren’t enough to coherently analyze the impact of the virus. The following indices when put against the case densities provide a solution to it.

The stringency index is a composite measure based on nine response indicators including school closures, workplace closures, travel bans, restriction of public events, and stay-at-home policies, rescaled to a value from 0 to 100 (100 being the strictest). It gives us an idea of how strongly the countries enforced COVID prevention measures and how it impacted the mortality rate over time. Average stringency index plotted against case density (Fig. 3) gives us a comprehensive measure of how the countries have fared so far, and allows us to identify outliers such as the countries that couldn’t flatten the curve despite enforcing strong measures. The COVID-19 virus originated in China, naturally putting it high on the stringency index axis with an average of 71.54. Most of its neighboring countries like India (68.94), Bhutan (67.31), Bangladesh (71.79), Vietnam (65.31), Pakistan (61.12), Myanmar (75.19), Philippines (70.73), and Kazakhstan (70.72) also exhibited relatively high stringency index and low overall case density (<100k), unlike Greece (69.69) and Italy (71.25) that have a high case density despite (>200k) having high stringency index. Amongst the countries with high population, China has a case density of just 199, Nigeria 1208, and Pakistan 6775. Nigeria managed to be one the countries with the lowest case density despite having a stringency index of 53.32. Indonesia has a case density of 21.76k, and India 30.88k; despite being relatively low on the graph, these aren’t good numbers, especially for India with a population of 1.4 billion. Things get worse with Brazil and the United States that have a case density of 140.26k and 240.84k respectively. The United States had it coming with a stringency index of 57.94, but Brazil seems to be in an unfortunate situation despite having a stringency index of 63.47. Countries with the highest case density include Denmark (527.92k), Andorra (517.41k), Cyprus (495.73k), Iceland (493.04k), and Slovenia (468.89k); these are devastating numbers considering their low population but expected as their stringency indices are lower than 60, with Iceland being the lowest at a disappointing 39.59. Some less populous countries like Yemen, Niger, and Burkina Faso exhibit impressively low case density despite being on the low end of the stringency index axis.

As the stringency index increases, the number of cases should decrease, which can be seen from the graph given in Fig 3.3 where we have perfomed the same fitting but filtered out the countries with a very low number of total cases.

In the graph given in Fig 3.2, a positive relationship arises due to the fact that many countries have low case density despite having a low stringency index. These are the countries where the virus didn’t spread as much due to their low HDI and disconnectivity with the rest of the world. Thus, the country’s stringency index is not the reason behind their low number of cases.

Table 1 - The table lists the top 10 outliers obtained using the curve fitted in the total case density versus the stringency index graph.

The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and having a decent standard of living. While the stringency index depicts the state of a nation in face of adversity, the HDI shows their general preparedness and expected response for such a situation. Regions and countries with a high human development index should have an easier time dealing with the virus, but it turns out that they generally have a higher case density (Fig. 4). This is due to the international connectedness and mobility of their population related to trade and tourism, vulnerability of older populations, and higher rates of non-communicable diseases. China has a HDI of 0.761 and appropriately responded to the crisis which is evident from its high stringency index. On the higher end of the HDI axis, we have countries like Iceland (0.949), Denmark (0.94), and Slovenia (0.917), and the United States (0.926) that have high case density owing to their low stringency index, but also countries like Japan (0.919), Saudi Arabia (0.854), and Kazakhstan (0.825) that have low case density but vary according to their stringency indices. On the lower end of the HDI axis, we have countries like Niger (0.394), Burkina Faso (0.452), and Yemen (0.47) that have surprisingly low case density (also shown with respect to stringency index). In the middle lie the countries of the Indian subcontinent - India (0.645), Bangladesh (0.632), Nepal (0.602), Myanmar (0.583), and Pakistan (0.557) with their respective case densities more or less fitting the general rising trend. Since these countries are also neighbors of China, they were already vigilant and imposed proper measures. Regardless, the case densities for India and Pakistan present a substantial problem as they have high populations, much higher than the others. It goes to show that the human development of a country does not have a substantial effect on its ability to deal with a crisis, in this case, a deadly virus that managed to cause great damage because the country wasn’t well equipped to battle it, regardless of how healthy or how literate or how well-off it was before.

Table 2 - The table lists the top 10 outlier countries obtained using the curve fitted in the total case density versus the human development graph.

Based on our observations, we have created a new metric by combining stringency index and human development index. When linear fitting was performed on the graph for this metric, we found that the error was lower as compared to the fitting for HDI and stringency index, which proves that this new metric is a good measure to quantify the spread of COVID. However, the error was not substantially lower, owing to the heterogeneity in the spread of the virus. Different countries have been impacted at very different levels; many outliers exist, making it difficult to establish a relationship between the number of cases and any single metric.

Table 3 - The table lists the top 10 outlier countries obtained using the curve fitted in the total case density versus the New Metric (HDI and Stringency Index) graph.

As we can see from all the graphs, there is a peak in the number of cases in all the countries, in May 2021 and January to March 2022. The first peak represents the second wave of COVID and the second one represents the third wave of COVID. Also, there is an increase in the number of people vaccinated throughout the timeline. The highlight here is the fact that while the number of cases during both the peaks are high, the number of deaths during these periods don’t show the same trend. The difference between the number of cases and number of deaths is low during the second wave and significantly higher during the third wave. This observation involves the following two speculations.

  1. As more people got vaccinated, the immunity to fight against covid increased. The number of cases may have been close, but the fatality of the disease decreased significantly.
  2. During the second COVID wave, the Delta variant had taken over, while in the third wave, we were hit by Omicron. Scientifically, the mortality rate of the Delta variant is higher compared to that of Omicron. Thus, a variation in the number of deaths is seen.

A steady increase in the number of vaccinations and booster doses can be seen in all countries. As long as the cases persist, the pharmaceuticals will keep pumping out effective vaccines to neutralize this constantly mutating virus, and more importantly, to generate revenue.

As unfortunate as the pandemic has been for the world, it’d be naive to ignore the flip-side of the coin, the boom it has produced for the pharmaceutical industry. With vaccines becoming a necessity for everyone, every capable pharma entered the rat race to conquer the market. Whether they were in it for the money or goodwill, they managed to produce results. Pfizer/BioNTech and Moderna, being American companies, produced the bulk of vaccines (> 500M) for the United States. Pfizer was the forerunner, holding a monopoly over the market of almost every first and third world country. They dished out 330M vaccines in the United States alone and around 100M in France, Germany, Italy and Japan. Moderna, although not as predominant as Pfizer, also managed to enter the market of almost every country, rolling out around 30M vaccines each in France, Germany and Italy. In the United States, they went head to head with Pfizer with a total of 210M vaccines sold. While Pfizer and Moderna battled it out for sales, Oxford/AstraZeneca left its mark on European countries and other second and third world countries, notably Argentina and South Korea. Johnson&Johnson tried their hand in the American market but were crushed by Pfizer and Moderna. Their traces also appear in Germany, South Africa, Nepal etc. but far behind the competition to make any difference. Evidently, popularity and trust was a major factor in the sales of vaccines with established pharmas selling more than their counterpart, regardless of the effectiveness in some cases.

This large-scale look at how the world handled the COVID-19 pandemic highlights both the strengths and vulnerabilities of all countries and the world as a whole. Many factors contributed to this pandemic response, directly or indirectly, significantly or insignificantly, and have led to the current state of the world, that can be best described as ‘healing’. The fabled vaccine at the beginning of the pandemic flooded the markets in under a year. The major disruption caused by the closure of academic institutions and workplaces is now a thing of the past as things are now almost back on track. The prolonged social isolation out of nowhere hit like a truck, but public places are bustling now, with appropriate measures to prevent spread of course. The world before the pandemic is gone, but the world after a pandemic, fully recovered, is soon to come.