































Highlights in BioScience
ISSN:2682-4043
DOI:10.36462/H.BioSci.202204

Perspectives

Open Access

1 Independent Researcher, Pharmaceutical Re-

search Facility, Cairo, Egypt.
2 National Centre for Radiation Research and

Technology, Cairo, Egypt.
3 Royal Oldham Hospital, Oldham, United King-

dom.

* To whom correspondence should be ad-
dressed: mostafaessameissa@yahoo.com

Editor: Hatem Zayed, College of Health and
Sciences, Qatar University, Doha, Qatar.

Reviewer(s):
Alsamman M. Alsamman, African Genome Center,
Mohammed VI Polytechnic University,Morocco..

Morad M. Mokhtar, Agricultural Genetic
Engineering Research Institute, Agricultural
Research Center, Giza, Egypt.

Received: March 31, 2022

Accepted: May 1, 2022

Published: August 24, 2022

Citation: Eissa ME , Rashed ER , Eissa
DE. Implementation of the Pareto principle
in focus group generation based on global
coronavirus disease morbidity and mortality
rates. 2022 Aug 24;5:bs202204

Copyright: © 2022 Eissa et al.. This is an
open access article distributed under the terms
of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.

Data Availability Statement: All relevant data are
within the paper and supplementary materials.

Funding: The authors have no support or
funding to report.

Competing interests: The authors declare
that they have no competing interests.

Implementation of the Pareto principle in focus group generation
based on global coronavirus disease morbidity and mortality rates

Mostafa Essam Eissa*1
><, Engy Refaat Rashed2

>< Dalia Essam Eissa3
>< 

Abstract
The recent pandemic that has hit the world has affected humanity in all aspects of life.

Since the outbreak of this worldwide epidemic, a huge amount of data has been generated.
In this article, we have provided a new simplified insight into the 2019 coronavirus disease
(COVID -19) using the Pareto principle to highlight the main contributors to morbidity
and mortality. A time series database of confirmed cumulative cases and deaths for all
countries was processed from the Humanitarian Data Exchange website provided by the
United Nations Office for the Coordination of Humanitarian Affairs. More than 85% of the
incidents recorded worldwide were from the AMRO, EURO, and SEARO WHO regions,
and the United States, Russia, and India were found to account for the largest proportion
of cases and deaths in these affected areas. The application of Pareto analysis is useful in
finding focus groups for further study and modeling.

Keywords: COVID-19, Morbidity, Mortality, Pareto principle, WHO

Introduction
The new coronavirus disease (COVID -19), which has been causing a worldwide pandemic for

about two years, has affected every aspect of human life, and the world has changed significantly

since then [1]. A large amount of data has been compiled showing morbidity and mortality rates as

cumulative numbers of cases and deaths on a daily basis for each affected country and/or area [2].

The database was processed based on the World Health Organization classification (WHO) and then

subdivided according to the daily records of the political region.

Implementation of the Pareto principle on big data of COVID-19 morbidities

and mortalities
Before assessing the distribution of COVID -19 deaths and morbidities, a brief look at the distri-

bution of the world population based on the census should be taken to find a match with the reported

cases and deaths according to the 60/40 and 80/20 rule using Minitab version 17.0.1. This software

was implemented as it has been used in other research. [3-6]. The Asian population accounts for

about 60% of the world population, followed by Africa and Europe (Figure 1A ) with a cumulative

total of over 85% of the world population. However, the COVID -19 cases and deaths show a dif-

ferent descending order, with the American regional office (AMRO) zone ranking first, followed by

the European regional office (EURO) and the Southeastern regional office (SEARO) with 0.40, 0.32,

and 0.17 cases and 0.48, 0.30, and 0.12 deaths, respectively, as a proportion of the world population

(Figure 1B and 1C ).

Contribution of countries by cumulative cases and deaths in WHO regions
An examination of contributing countries and/or areas with cumulative COVID -19 mortality

and morbidity rates based on the WHO classification revealed that few regions are particularly af-

fected. The main contributing countries in the AMRO region were, in descending order of cases,

the United States of America (USA), Brazil, Argentina, and Colombia, with a cumulative share of

more than 80%. For deaths, the same order was maintained for the first two countries, followed by

Mexico and Pero with a cumulative contribution of about 82% [7].

Highlights in BioScience Page 1 of 4 August 2022|Volume 5

https://doi.org/10.36462/H.BioSci.202204
https://creativecommons.org/licenses/by/4.0/
mostafaessameissa@yahoo.com
https://orcid.org/0000-0003-3562-5935
engyrefaat@yahoo.com
https://orcid.org/0000-0002-6593-378X
daliaessameissa@yahoo.co.uk
https://orcid.org/0000-00002-6340-8973
http://bioscience.highlightsin.org/


Eissa et al., 2022 Implementation of the Pareto principle to global coronavirus disease rates

Both the USA and Brazil had a cumulative contribution of
more than 50% of the total number of people affected in the
AMRO region, as shown in Figure 2A and 2B . Another area
WHO - which made a relatively small contribution - was stud-
ied. The West Pacific Regional Office (WPRO) showed that
the Philippines accounted for about one-third of the cumulative
daily morbidity and mortality rates. However, the order var-
ied for the other nations that followed, as Japan and Malaysia
showed an alternating order in cases and deaths. Interestingly,
the most populous country in the world, from which the first epi-
demic cases originated that triggered the global outbreak, con-
tributed only marginally (≈ 11% in deaths and fourth in cumula-
tive rate) because of rapid and rigorous government and public
health interventions [8]. A detailed visualization of the sequence
of records is shown in Fig. 3 Figure 2C and 2D .

Figure 1. Pareto chart showing major continents in population census (A) and
WHO main contributors in morbidities (B) and mortalities (C).

A look at a third important region, SEARO, showed that In-
dia had the highest number of deaths and morbidities at daily
rates in the WHO region. This is evident in Figure 2E and
2F with a proportion of over 70%. Adding Indonesia and India
to the cumulative deaths gives a proportion of about 0.90 from
the SEARO region. Another marginally affected WHO area,
the Eastern Mediterranean Regional Office (EMRO), showed a
different pattern, where only the first country was strongly af-
fected by COVID -19, followed by a group of seven and six

Figure 2. Pareto chart showing major countries in mortalities and morbidities
from COVID-19 from AMRO (A and B), WPRO (C and D) and SEARO ( E
and F), respectively.

countries showing relatively small variations among them. For
the cumulative case rate, the descending order was the Islamic
Republic of Iran, Iraq, Pakistan, Morocco, Jordan, the United
Arab Emirates (UAE), Saudi Arabia, and Lebanon, with con-
tribution factors of approximately 0.32, 0.13, 0.09, 0.06, 0.06,
0.05, 0.05, and 0.04, respectively (Figure 3). For the cumu-
lative mortality rate, the descending order was Iran, Pakistan,
Iraq, Egypt, Tunisia, Morocco, and Saudi Arabia with percent-
age contributions of 42.1, 9.7, 8.8, 7.1, 6.7, 4.8, and 3.9%, re-
spectively (Figure 3A and 3B). Data from the African Regional
Office (AFRO) - which is also a minority - show similar be-
havior to EMRO, but with a greater contrast between the first
country affected and the other nations that follow, as shown in
Figure 3C and 3D. Moreover, the reported cumulative cases
show a broader range of joint contributions. South Africa alone
accounted for approximately 50% and 60% of the total cumula-
tive cases and deaths, respectively. In terms of morbidity and
mortality for the remaining countries, the order was as follows:
Ethiopia (6.0%), Kenya (4.2%), Nigeria (4.1%), Algeria (3.0 %),
Zambia (3.6%), Ghana (2.5%), Mozambique (2.1%), Botswana

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Eissa et al., 2022 Implementation of the Pareto principle to global coronavirus disease rates

(2.1%), Namibia (1.8%) and Zimbabwe (1.8%) and Algeria (4.4
%), Ethiopia (4.0%), Kenya (3.3%), Zimbabwe (2.4%), Nigeria
(2.3%) and Zambia (2.1%), respectively. EURO Lands showed
a unique pattern in Figure 3E and 3F . While this region was
considered one of the three main zones affected by daily death
and disease rates from COVID -19, much less variation was
shown with broader contributing countries, resulting in an ob-
servable low declining ramp in the Pareto diagram. Thus, 15
and 12 nations shared the top contributing countries (80%) in
morbidity and mortality data, respectively. The first two coun-
tries (the Russian Federation and the United Kingdom (UK)) ac-
counted for only about one-fifth and one-quarter, respectively,
of the cumulative cases and deaths in the data set. The descend-
ing list of remaining countries in the third and fourth morbid-
ity categories included France (with about the same contribution
as the United Kingdom), Turkey, Spain, Italy, Germany, Poland,
Ukraine, the Netherlands, the Czech Republic, Belgium, and Ro-
mania, with contribution ratios of 0.10, 0.09, 0.07, 0.07, 0.06,
0.04, 0.04, 0.03, 0.03, and 0.02, respectively. The descending
order of the remaining nations for the fourth three cumulative
deaths was as follows: Italy, France, Spain, Germany, Poland,
Turkey, Ukraine, and Romania, with proportions of 0.11, 0.09,
0.08, 0.07, 0.05, 0.04, 0.04, and 0.03, respectively.

Figure 3. Pareto chart showing major countries in mortalities and morbidities
from COVID-19 from EMRO (A and B), AFRO (C and D) , EURO (E and F).

Findings and limitations
The previously analyzed report is time-bound, as updating

the dataset with new inputs may show some discrepancies [9,
10]. In particular, the distribution of COVID -19 across the globe
seemed to be independent of the climatic nature of the regions,
as seen in Figure 4 , and showed a wide geographic dispersion
within different temperature and humidity ranges. Nevertheless,
Table 1 showed that countries near the equator had a higher pro-
portion of daily cumulative morbidity and mortality rates than
those farther away, where AMRO and SEARO countries had
about 45% and 43% of cumulative morbidity and mortality rates,
respectively.

Figure 4. Political map showing the distribution of more than 60% of morbidi-
ties and mortalities around the globe.

Since other researchers have concluded that hot and humid
climates can halt the spread of the disease to some degree, this
suggests that other factors played a greater role in these polit-
ical regions, including but not limited to public health policy
and agency actions, public perception and awareness of outbreak
control measures, general health status of citizens, racial factors,
extent and rate of vaccination, and population size and density,
in addition to political and economic conditions [11- 14]. An-
other important factor that should not be underestimated con-
cerns the government and public health officials, as well as the
routine system for recording, monitoring, and tracking affected
individuals, as this could affect the reliability and quality of the
derived databases.

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Eissa et al., 2022 Implementation of the Pareto principle to global coronavirus disease rates

Table 1. Major countries in the global distribution of morbidities and mortalities cases by WHO regions.

WHO Region A Regional Cumulative * Rate Contribution (%) Morbidity Country Cases Contribution (%) * Mortality Country Death Contribution (%) *

Cases Deaths

AMRO B 40.1 48.2 USA 19.93 USA 16.39

Brazil 9.38 Brazil 11.86

Argentina 2.13 Mexico 6.15

SEARO C 16.8 11.6 India 13.73 India 8.41

EURO D 31.8 30.0 Russia 3.50 Russia 3.90

UK 3.18 UK 3.60

France 3.18 Italy 3.30

Turkey 2.86 France 2.70

Spain 2.23 Spain 2.40

Italy 2.23 Germany 2.10

A World Health Organization , B American Regional Office, C Southeast Asia Regional Office, D European Regional Office, * Based on cumulative daily record.

Conclusion
In the world of Big Data, it will be useful to analyze a large

amount of information quickly, easily and effectively to derive a
useful conclusion. From the cumulative daily cases and deaths
in the coronavirus pandemic database, a focus study group could
be identified using Pareto analysis. These segregated countries
will be subjected to further quantitative study using a modeling
approach for morbidity and mortality that could serve as the ba-
sis for further outbreak investigations.

Abbreviations

AFRO: African Regional Office

AMRO: American Regional Office

COVID-19: Coronavirus disease of 2019

EMRO: Eastern Mediterranean Region

EURO: European Regional Office

SEARO: Southeast Asia Regional Office

WHO: World Health Organization

WPRO: West Pacific Regional Office

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https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases
https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases
https://seekingalpha.com/article/4198408-60-40-is-new-neutral-pareto-ratio
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https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
http://bioscience.highlightsin.org/

	Abstract
	Introduction
	Implementation of the Pareto principle on big data of COVID-19 morbidities and mortalities
	Contribution of countries by cumulative cases and deaths in WHO regions
	Findings and limitations
	Conclusion
	Abbreviations
	References

