INTRODUCTION:

`O Mouse, do you know the way out of this pool? I am very tired of swimming about here, O Mouse!', but it said nothing to Alice.

Lewis Carroll, Alice's Adventures in Wonderland, 1865

The history of the 1918-19 influenza showed that one-third of the world population (more than 500 million persons)^1,2 was affected with the pandemic and the total deaths were reportedly more than 50 million worlwide^3-5. The researchers have been trying to understand what happened for 100 years and miserably failed to answer the common question as to “why was it so fatal?”⁶.

The dawn of the 21^st century, had witnessed two major outbreaks: severe acute respiratory syndrome (SARS) (2002-2004)⁷ and Middle East respiratory syndrome (MERS) (2012)⁸ due to novel coronavirus. At the very end of the year 2019 (December 31^st), a few pneumonia cases with unknown cause were reported in China⁹. After a month, the World Health Organization (WHO) declared the COVID 19 outbreak as a public health emergency of international concern (January 30^th, 2020)¹⁰, and 6 weeks later, COVID 19 outbreak was declared as pandemic¹¹. As of September 20^th, 2020, COVID 19 pandemic had resulted in more than 30.6 million confirmed cases and 950,000 deaths¹²that occurredin 214 countries around the world.

WHO has recently initiated an interactive dashboard that provides the statistics of COVID 19 cases daily and country wise.¹³ The WHO has divided the world into six regions: Africa, Americas, South-East Asia, Europe, Eastern Mediterranean, and Western Pacific.¹⁴ Based on the WHO regions, the dashboard updates the daily numbers of confirmed and death cases and projects the proportion of distribution of the COVID 19 cases in each region. Similarly, the worldometer is another international and oldest dashboard which provides global COVID 19 statistics and updates the daily data continent wise. This reference website was voted as the best free dashboard by the American Library Association¹⁵.

The historic records of the 1918 pandemic showed that it was first developed in Asia and spread to the rest of the world unevenly. The most affected regions were Europe, Asia, and North America. However, the data were inadequate to describe the distribution of the virus¹⁶. The epidemiologists explained the possible correlation of 3 pandemic waves 1918-19, which happened in spring-summer, summer-autumn and winter respectively, but the data were inadequate to confirm the same⁶. The online dashboards like WHO and worldometer shows the global numbers regions and continent wise respectively, but not projecting any association or correlation between the distribution of pandemic waves and outcomes with the other demographic data of the countries such as climatic condition, population strength, economic status, health care index, human development index, quality of life, etc. Such projections will give an idea of the commonality of the distribution of pandemic waves and can help the policymakers to strengthen the mitigation to face outbreaks in the future. There are a few pieces of literature available on global epidemiology conducted during the pre-pandemic period of COVID 19. The data collection of COVID 19 cases from the respective countries done through the publicly available official data from the websites to conduct a rapid analysis to stimulate the public health response and this approach is called infodemiology under web-based surveillance^17,18. A web-based surveillance study conducted between December 31^st, 2019, and April 14^th, 2020 was able to project correlation with the travel links, age distribution, occupation, and transmission setting of 99 affected countries¹⁹. Similarly, another three months updated study conducted between December 31^st, 2019, and April 2^nd, 2020 on the association of COVID 19 global distribution and environmental and demographic factors showed that the number of cases and deaths is higher in high-income countries, higher altitudes, and colder climates²⁰. Nonetheless, there is no doubt that COVID 19 cases spread rapidly to 214 countries all over the world irrespective of regions, economic status, and climatic conditions. As we are standing now in the 10^th month of the COVID 19 pandemic, the investigators attempted to pool the COVID 19 case numbers and demographic data of all affected countries using a cross-sectional infodemiological approach and looked for any significant association between the demographic characteristics of countries and proportion of COVID 19 distribution in the respective areas. These results give a glimpse of cross-sectional data of the 10^th month of the COVID 19 pandemic and assess the need to conduct follow-up studies periodically if needed.

MATERIALS AND METHODS:

The investigators employed the Worldometer¹⁵ (the primary source of data collection) and WHO¹³(secondary source) dashboards as the main sources of COVID 19 data and have taken the cross-sectional cumulative data last updated on September 20^th, 2020. A total of 156 countries with more than 1 million total population among 214 affected countries worldwide with COVID 19 were selected. Based on the primary COVID 19 data, 7 main variables were selected; total cases, total deaths, active cases, recovered cases, proportion of cases to population per 1000, the proportion of deaths to population per 100,000, and proportion of tests to population per 100 as for September 20^th, 2020. The demographic variables of the countries included are total population strength, continent, per capita gross domestic product (GDP), climate, Physical Quality of Life Index (PQLI), human Development Index (HDI), and health care index which were extracted from different websites^21-23and official government websites of the respective countries. All the data used in the study were publicly available. This type of web-based data collection is referred to here as infodemiology, to get a snapshot of the pattern of distribution of COVID 19 all over the world.

Quartile clustering technique was selected to generate meaningful clusters for COVID 19 distribution and demographic data of the affected countries with minimum and maximum value. This process was carried out with the main objective of partitioning observations in groups and explores any significant existing association between the selected categorical variables. Descriptive statistics like frequency distribution and percentage and inferential statistics like chi-square and two-proportion Z tests were used to analyze the data using PSPP 1.4.1 version software. All the statistical analyses were performed at a 5% significance level.

RESEARCH IN CONTEXT:

Pre-preparation:

We searched the four electronic free databases (PubMed, Medline, Embase, and Cochrane central) between December 2019 and September 2020 using Medical Subject Heading (MeSH) terms COVID 19 AND Global OR Distribution AND Demographic OR Variables. We identified 4 articles by citation matching published between March 2020 and July 2020. To date, there is a paucity of studies available on the perspective of global epidemiology and its association with the determinants of affected countries.

The credibility of the study:

We have used global epidemiological and demographic data from the confirmed COVID 19 cases and affected countries respectively. As we have retrieved data from the publicly available information management tools such as worldometer and WHO for confirmed COVID 19 cases and other government official websites for demographic determinants of the affected countries. We have clustered the variables using an interquartile technique to develop meaningful groups of data for exploring the pattern of distribution of COVID 19 worldwide. In addition to the Chi-square test, we have done the two proportion Z test to determine the results are valid or repeatable.

LIMITATIONS:

There is a possibility of under-reporting of COVID 19 by the affected countries in the dashboards. We have excluded 58 affected countries with the population of less than 1 million among the overall total of 214 affected countries.

IMPLICATIONS OF THE FINDINGS:

Our categorical analysis of 156 affected countries provides insights about the significant association between the global distribution of COVID 19 and the selected demographic variables of the affected countries. The current study results which summarized the categorical determinants from a global perspective stimulate the critical thinking of researchers to conduct further studies to shed light on this gap.

RESULTS AND DISCUSSION:

From December 31, 2019, to September 20, 2020 (corresponding to epidemiological weeks 1–38^thof the COVID-19 outbreak), 214 countries reported the confirmed cases of COVID 19. As mentioned above, we have included 156 (72.8%) affected countries with more than 10 million population for ensuring the homogeneity of the sample data. Similarly, the same process was adapted in a study to describe the global spread of COVID-19 in which they had collected the data of COVID-19 confirmed cases from 99 affected countries from Dec 31, 2019, to March 10, 2020 (corresponding to epidemiological weeks 1–11 of the COVID-19 outbreaks) using web-based cross-sectional surveillance¹⁹.

It is showed that the majority of the selected countries are having a total population below 100 million 142 (91.03%), are from Africa 49 (31.41%) and Asia 45 (28.85%) continents, and have tropical climate 68 (43.59%). Most of the selected countries’ per capita gross domestic product (GDP) is less than 10,000 (102 (65.38%)) and majority of the countries 89 (57.05%) are having the health care index between 69 and 40. In contrast, the majority of countries selected for the study have a very high human development index 53 (33.97%) as per the United Nations Development Programme 2019 ranking scale.

Table I. Distribution of COVID 19 in the selected countries* (n=156)

Variables

Number (%)

Total cases

<100,000

100,000-1 M

>1M

119 (76.28)

33 (21.15)

4 (2.56)

Total deaths

<10,000

10,000-100,000

>100,000

139 (89.10)

15 (9.62)

2 (1.28)

Total Recovered

<100,000

100,000-1 M

>1M

130 (83.33)

23 (14.74)

3 (1.92)

Active cases

<100,000

>100,000

145 (92.95)

11 (7.05)

Total cases per 1000 population

1-10

>10

67 (42.95)

71 (45.51)

18 (11.54)

Deaths per 100,000 population

1-10

>10

45 (28.85)

67 (42.95)

44 (28.21)

Total Tests per 100 population

1-10

11-20

>21

107 (68.59)

30 (19.23)

19 (12.18)

*For 20.09.2020

We have found four similar studies which were attempted to investigate the association between global COVID 19 distribution (as per status on April 2^nd 2020²⁰, March 10^th 2020¹⁹, February 29^th 2020²⁴ and April 21^st 2020²⁵) and other variables of the countries; GDP per capita^20,24, temperature²⁰, latitude²⁰, longitude²⁰, WHO regions¹⁹, health care index²⁴, population density²⁵ and tests per million²⁵.

It is showed that among the selected 156 countries, India, USA, Brazil and Russia reported more than 1 million confirmed cases of COVID 19; USA and Brazil had more than 100 K total reported deaths due to COVID 19; India, USA and Brazil had an account of more than 1 million recovered cases from COVID 19; India, USA, Brazil, Russia, Mexico, UK, France, Colombia, Spain, Argentina and Peru had more than 100 K active cases of COVID 19 as per the data on September 20^th, 2020. As per the data available on April 21^st,2020, which was the recently pooled data among the four identified articles on global COVID 19 distributions, in which the highest number of cases reported in the USA (37.55%), Spain (9.4%), Italy (8.4%), France (7.2%), Germany (6.8%) and UK (5.9%) out of total 17 affected countries²⁵.

The proportion of total COVID 19 cases per 1000 population among the 156 affected countries showed that the highest proportion of cases were reported in Qatar (44) and Bahrain (38) whereas India was reported with 4 total COVID 19 cases with 1000 population. Similarly, the proportion of total COVID 19 deaths per 100 K population was found highest in Peru (95) and Belgium (86), whereas in India had 6 deaths per 100 K population. We also analyzed the proportion of tests per 100 population as of September 20^th, 2020 in the 156 affected countries, the highest numbers reported in UAE (89), Bahrain (78), Denmark (59) and Singapore (43). India was reported with 5 COVID 19 tests per 100 population. It is showed in another study regarding the Global COVID 19 epidemiology data as on the date April 21^st,2020, the prevalence of COVID 19 was 4.6 per million in India and 4,367 per million in Spain and incidence was 0.04 per million in India to 14.7 per million in Belgium. It is seen that deaths were ranged from 0.5 per million in India to 517.5 per million in Belgium. The highest reported COVID 19 tests conducted per 1 million population was in Switzerland (26,292) and Portugal (26,671), whereas, 324 tests were reported in India²⁵.

It is showed that there is a significant association between the total population and the continents of the affected countries, and global COVID 19 distributions. In addition to that we have done the two proportion Z test, which is a hypothesis test to determine whether the difference between two proportions is significant or not. It is shown that the majority of the countries are reported with <100,000 confirmed COVID 19 cases (p<0.05), <10,000 deaths (p<0.05) and <100,000 recovered cases (p<0.05). It is suggested in a study that the total cases per million showed a weak correlation with population density (r = 0.14)²⁵. In contrast, it is identified that the high population density and probability of COVID 19 transmission are reported^26,27. We have found a significant association between the continents of affected countries and total tests per 100 population. It is seen that the majority of European countries conducted >10 tests per 100 population (p<0.05) whereas, <10 tests per 100 population were conducted in African countries (p<0.05). It is seen in a study that tests per million (r = 0.67) showed a strong correlation with total cumulative cases per million as per the data on April 21^st, 2020²⁵.

The current study shows a significant association between the per capita GDP and proportion of tests per 100 population; the majority of countries those GDP <10,000 conducted <10 tests per 100 population (p<0.05) whereas, >10 tests per 100 population were conducted in countries having per capita GDP between 10K-50K (p<0.05). It is seen in a study that countries with a higher GDP had more COVID-19 proportion of cases and deaths²⁰, however, we could not find the literature on the association of GDP and proportion of COVID 19 tests. It is also shown in the present study that the majority of the countries having Physical Quality of Life Index <99 were reported with <100,000 cases and had conducted <10 tests per 100 population (p<0.05). Similarly, it is also highlighted that the majority of the countries having Human Development Index (HDI) ranking ‘high and low’ categories were reported with <100,000 cases and had conducted <10 tests per 100 population (p<0.05).

It is showed in a study that the temperature of the affected countries had a reverse association with both COVID-19 proportions of cases (r = −0.50; p < 001) and deaths (r = −0.50; p < 001)²⁰. In contrast, there is no significant association found between the climate and confirmed cases of COVID 19. However, it is suggested in the present study that the majority of the tropical climate countries reported with <10 tests per 100 population (p<0.05), whereas the continental and temperate climate countries reported with >10 COVID 19 tests per 100 population (p<0.05).

CONCLUSION:

The current study findings do not provide any solution, nonetheless, it gives a cross-sectional glimpse of cumulative global COVID 19 distribution data in the 10^thmonth of the pandemic using an infodemiological approach. Overall, this report shows the pattern of distribution of COVID 19 all over the world, but continuous analysis of COVID 19 distribution is still needed until zero reporting of cases. Hopefully, these results enhance further understanding of the distribution of COVID 19 worldwide and help the policymakers of the affected countries to evaluate the ongoing COVID 19 preparedness and response measures to come out from the pandemic pool very soon.

AVAILABILITY OF DATA:

We have used the data that were publicly available.

CONFLICT OF INTEREST:

The author declares no conflict of interest.

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Received on 02.08.2021 Modified on 26.12.2021

Int. J. Nur. Edu. and Research. 2022; 10(2):99-103.

DOI: 10.52711/2454-2660.2022.00023