Global case fatality rate of coronavirus disease 2019 (COVID-19) by continents and national income: a meta-analysis

Abstract

The aim of this study is to provide a more accurate representation of COVID-19's CFR by performing meta-analyses by continents and income, and by comparing the result with pooled estimates. We used multiple worldwide data sources on COVID-19 for every country reporting COVID-19 cases. Based on the data, we performed random and fixed meta-analyses for CFR of COVID-19 by continents and income according to each individual calendar date. CFR were estimated based on the different geographical regions and level of income using three models: pooled estimates, fixed- and random-model. In Asia, all three types of CFR initially remained approximately between 2.0% and 3.0%. In the case of pooled estimates and the fixed model results, CFR increased to 4.0%, by then gradually decreasing, while in the case of random-model, CFR remained under 2.0%. Similarly, in Europe, initially the two types of CFR peaked at 9.0% and 10.0%, respectively. The random-model results showed an increase near 5.0%. In high income countries, pooled estimates and fixed-model showed gradually increasing trends with a final pooled estimates and random-model reached about 8.0% and 4.0%, respectively. In middle-income, the pooled estimates and fixed-model have gradually increased reaching up to 4.5%. in low-income countries, CFRs remained similar between 1.5% and 3.0%. Our study emphasizes that COVID-19 CFR is not a fixed or static value. Rather, it is a dynamic estimate that changes with time, population, socioeconomic factors and the mitigatory efforts of individuals countries.

Full text

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/358249085
Global case fatality rate of coronavirus disease 2019 (COVID-19) by continents
and national income: a meta-analysis
ArticleinJournal of Medical Virology · January 2022
DOI: 10.1002/jmv.27610
CITATIONS
0
READS
89
90 authors, including:
Some of the authors of this publication are also working on these related projects:
Delirium and cardiac surgery View project
Medical Student's Psychological status View project
Ramy Abou Ghayda
Brigham and Women's Hospital
83 PUBLICATIONS379 CITATIONS
SEE PROFILE
Sojung Yoon
Severance Hospital
13 PUBLICATIONS341 CITATIONS
SEE PROFILE
Gwang Hun Jeong
Gyeongsang National University
25 PUBLICATIONS226 CITATIONS
SEE PROFILE
Jae Won Yang
Yonsei University
97 PUBLICATIONS898 CITATIONS
SEE PROFILE
All content following this page was uploaded by Mehrdad Amir-Behghadami on 26 February 2022.
The user has requested enhancement of the downloaded file.

Received: 22 September 2021
|
Accepted: 18 January 2022
DOI: 10.1002/jmv.27610
RESEARCH ARTICLE
The global case fatality rate of coronavirus disease 2019 by
continents and national income: A meta‐analysis
Ramy Abou Ghayda
1
|Keum Hwa Lee
2
|Young Joo Han
3
|Seohyun Ryu
4
|
Sung Hwi Hong
4
|Sojung Yoon
4
|Gwang Hum Jeong
5
|Jae Won Yang
6
|
Hyo Jeong Lee
4
|Jinhee Lee
7
|Jun Young Lee
6
|Maria Effenberger
8
|
Michael Eisenhut
9
|Andreas Kronbichler
10
|Marco Solmi
11,12,13,14
|Han Li
15
|
Louis Jacob
16,17
|Ai Koyanagi
17,18
|Joaquim Radua
19,20,21
|Myung Bae Park
22
|
Sevda Aghayeva
23
|MohamedL.C.B.Ahmed
24
|Abdulwahed Al Serouri
25
|
Humaid O. Al‐Shamsi
26,27
|Mehrdad Amir‐Behghadami
28,29,30
|Oidov Baatarkhuu
31
|
Hyam Bashour
32
|Anastasiia Bondarenko
33
|Adrian Camacho‐Ortiz
34
|
Franz Castro
35
|Horace Cox
36
|Hayk Davtyan
37
|Kirk Douglas
38
|
Elena Dragioti
39
|Shahul Ebrahim
40
|Martina Ferioli
41
|Harapan Harapan
42
|
Saad I. Mallah
43
|Aamer Ikram
44
|Shigeru Inoue
45
|Slobodan Jankovic
46
|
Umesh Jayarajah
47
|Milos Jesenak
48
|Pramath Kakodkar
49
|
Yohannes Kebede
50
|Meron Kifle
51
|David Koh
52
|Visnja K. Males
53
|
Katarzyna Kotfis
54
|Sulaiman Lakoh
55
|Lowell Ling
56
|Jorge Llibre‐Guerra
57
|
Masaki Machida
45
|Richard Makurumidze
58
|Mohammed Mamun
56,59,60,61
|
Izet Masic
62
|Hoang Van Minh
63
|Sergey Moiseev
64
|Thomas Nadasdy
65
|
Chen Nahshon
66
|Silvio A. Ñamendys‐Silva
67
|Blaise N. Yongsi
68
|
Henning B. Nielsen
69
|Zita A. Nodjikouambaye
70
|Ohnmar Ohnmar
71
|
Atte Oksanen
72
|Oluwatomi Owopetu
73
|Konstantinos Parperis
74
|
Gonzalo E. Perez
75
|Krit Pongpirul
76
|Marius Rademaker
77
|Sandro Rosa
78,79
|
Ranjit Sah
80
|Dina Sallam
81
|Patrick Schober
82
|Tanu Singhal
83
|Silva Tafaj
84
|
Irene Torres
85
|J. Smith Torres‐Roman
86
|Dimitrios Tsartsalis
87
|
Jadamba Tsolmon
88
|Laziz Tuychiev
89
|Batric Vukcevic
90
|Guy Wanghi
91
|
Uwe Wollina
92
|Ren‐He Xu
93
|Lin Yang
94,95
|Zoubida Zaidi
96
|Lee Smith
97
|
Jae Il Shin
2
1
Urology Institute, University Hospitals, Case Western Reserve University, Cleveland, Ohio, USA
2
Department of Pediatrics, Yonsei University College of Medicine, Seoul, Republic of Korea
3
Hospital Medicine Center, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
4
Yonsei University College of Medicine, Seoul, Republic of Korea
5
College of Medicine, Gyeongsang National University, Jinju, Republic of Korea
6
Department of Nephrology, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
J Med Virol. 2022;1–12. wileyonlinelibrary.com/journal/jmv © 2022 Wiley Periodicals LLC
|
1
Ramy Abou Ghayda, Keum Hwa Lee, Young Joo Han, Seohyun Ryu, Sung Hwi Hong, Sojung Yoon, Gwang Hun Jeong, and Jae Won Yang contributed equally to this study.

7
Department of Psychiatry, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
8
Department of Internal Medicine I, Gastroenterology, Hepatology, Endocrinology & Metabolism, Medical University Innsbruck, Innsbruck, Austria
9
Luton & Dunstable University Hospital NHS Foundation Trust, Luton, UK
10
Department of Internal Medicine IV, Nephrology and Hypertension, Medical University Innsbruck, Innsbruck, Austria
11
Department of Psychiatry, University of Ottawa, Ontario, Canada
12
Department of Mental Health, The Ottawa Hospital, Ontario, Canada
13
Ottawa Hospital Research Institute (OHRI) Clinical Epidemiology Program University of Ottawa, Ottawa, Ontario, Canada
14
School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada
15
University of Florida College of Medicine, Gainesville, Florida, USA
16
Faculty of Medicine, University of Versailles Saint‐Quentin‐en‐Yvelines, Versailles, France
17
Research and Development Unit, Parc Sanitari Sant Joan de Déu, CIBERSAM, Sant Boi de Llobregat, Barcelona, Spain
18
ICREA, Barcelona, Spain
19
Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
20
Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
21
Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
22
Department of Gerontology Health and Welfare, Pai Chai University, Daejeon, Republic of Korea
23
Department of Gastroenterology, Azerbaijan Medical University School of Medicine, Baku, Azerbaijan
24
Research Unit in Epidemiology and Diversity of Microorganisms, Department of Biology, University of Nouakchott Al Aasriya, Nouakchott, Mauritania
25
Yemen Field Epidemiology Training Program, Sana'a, Yemen,
26
College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
27
Burjeel Cancer Institute, Burjeel Medical City, Abu Dhabi, United Arab Emirates
28
Iranian Center of Excellence in Health Management (IceHM), School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
29
Student Research Committee (SRC), Tabriz University of Medical Sciences, Tabriz, Iran
30
Road Traffic Injury Research Center, Iranian International Safe Community Support Center, Tabriz University of Medical Sciences, Tabriz, Iran
31
School of Medicine, Mongolian National University of Medical Sciences, Ulan Bator, Mongolia
32
Department of Family and Community Medicine, Faculty of Medicine, Damascus University, Damascus, Syria
33
Shupyk National Healthcare University of Ukraine, Kyiv, Ukraine
34
Servicio de Infectología, Hospital Universitario “Dr José Eleuterio González”, Universidad Autónoma de Nuevo León, Monterrey, Mexico
35
Department of Research and Health Technology Assessment, Gorgas Memorial Institute for Health Studies, Panama City, Panama,
36
Ministry of Health Guyana, Georgetown, Guyana
37
Tuberculosis Research and Prevention Center NGO, Yerevan, Armenia
38
Centre for Biosecurity Studies, University of the West Indies, Cave Hill, St. Michael, Barbados
39
Department of Health, Medicine and Caring Sciences, Pain and Rehabilitation Centre, Linkoping University, Linkoping, Sweden
40
Faculty of Medicine, University of Sciences, Techniques, and Technology, Bamako, Mali
41
IRCCS Azienda Ospedaliero‐Universitaria di Bologna, Respiratory and Critical Care Unit, S. Orsola‐Malpighi Hospital, Bologna, Italy
42
Medical Research Unit, School of Medicine Universitas Syiah Kuala, Banda Aceh, Indonesia
43
School of Medicine, Royal College of Surgeons in Ireland‐Bahrain, Busaiteen, Kingdom of Bahrain
44
National Institute of Health, Islamabad, Pakistan
45
Department of Preventive Medicine and Public Health, Tokyo Medical University, Tokyo, Japan
46
Department of Pharmacology and Toxicology, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
47
Postgraduate Institute of Medicine, University of Colombo, Colombo, Sri Lanka
48
Department of Pediatrics, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
49
School of Medicine, National University of Galway Ireland, Galway, Ireland
50
Department of Health, Behavior, and Society, Faculty of Public Health, Jimma University, Jimma, Ethiopia
51
Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
52
Saw Swee Hock School of Public Health, National University of Singapore, Siangapore
53
Division of Endocrinology, Diabetes and Metabolic Disease in Split, Clinical Hospital Centre Split, School of Medicine Split, Šoltanska 1, Split, Croatia
54
Department Anaesthesiology, Intensive Therapy and Acute Intoxications, Pomeranian Medical University, Szczecin, Poland
55
College of Medicine and Allied Health Sciences, University of Sierra Leone, Freetown, Sierra Leone
2
|
ABOU GHAYDA ET AL.

56
Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
57
Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
58
Department of Community Medicine, Department of Primary Care Sciences, University of Zimbabwe, Faculty of Medicine and Health Sciences, Harare, Zimbabwe
59
Department of Public Health and Informatics, Jahangirnagar University, Savar, Dhaka, Bangladesh
60
Department of Public Health, Daffodil International University, Dhaka, Bangladesh
61
CHINTA Research Bangladesh, Dhaka, Bangladesh
62
Academy of Medical Sciences of Bosnia and Herzegovina, Sarajevo, Bosnia and Herzegovina
63
Center for Population Health Sciences, Hanoi University of Public Health, Hanoi, Vietnam
64
Sechenov First Moscow State Medical University, Moscow, Russia
65
Department of Dermatology, “St. Parascheva”Clinical Hospital of Infectious Diseases, Galati, Romania
66
Department of Gynecologic Surgery and Oncology, Carmel Medical Center, Haifa, Israel
67
Instituto Nacional de Cancerología, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
68
Institute for Training & Research in Population Studies (IFORD), The University of Yaoundé II, Soa, Cameroon
69
Department of Anaesthesia and Intensive Care, Zealand University Hospital Roskilde, Roskilde, Denmark
70
Mobile Laboratory for Hemorrhagic and Respiratory Viruses in Ndjamena, Ndjamena, Chad
71
Department of Medical Research (Lower Myanmar), Myanmar Health Ministry, Yangon, Myanmar
72
Tampere University, Tampere, Pirkanmaa, Finland
73
Department of Community Medicine, University College Hospital, Ibadan, Nigeria
74
Department of Internal Medicine, University of Cyprus Medical School, Nicosia, Cyprus
75
Division of Cardiology, Clínica Olivos, Buenos Aires, Argentina
76
Department of Preventive Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
77
Waikato Clinical School, Auckland University Medical School, Hamilton, New Zealand
78
College of Pharmacy, Federal Fluminense University, Niterói, Rio de Janeiro, Brazil
79
Pharmacy Division, National Institute of Industrial Property, Rio de Janeiro, Rio de Janeiro, Brazil
80
National Public Health Laboratory, Kathmandu, Nepal
81
Department of Pediatrics & pediatric nephrology, Faculty of Medicine, Ain Shams University, Cairo, Egypt
82
Department of Anesthesiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
83
Kokilaben Dhirubhai Ambani Hospital and Medical Research Institute, Mumbai, India
84
University Hospital Shefqet Ndroqi, Tirana, Albania
85
Fundación Octaedro, Quito, Ecuador
86
Universidad Cientifica del Sur, Lima, Perú
87
Department of Emergency Medicine, Hippokratio Hospital, Athens, Greece
88
Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia
89
The Tashkent Medical Academy, Tashkent, Uzbekistan
90
Faculty of Medicine, University of Montenegro, Podgorica, Montenegro
91
Unit of Physiology, Department of Basic Sciences, Faculty of Medicine, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
92
Department of Dermatology and Allergology, Städtisches Klinikum Dresden, Dresden, Germany
93
Centre of Reproduction, Development and Aging, Faculty of Health Sciences, Institute of Translational Medicine, University of Macau, Taipa, Macau, China
94
Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, Canada
95
Departments of Oncology and Community Health Sciences, University of Calgary, Calgary, Canada
96
Faculty of Medicine, University Ferhat Abbas, Setif, Algeria
97
The Cambridge Centre for Sport and Exercise Sciences, Anglia Ruskin University, Cambridge, UK
Correspondence
Jae Il Shin, Department of Pediatrics, Yonsei
University College of Medicine, Yonsei‐ro 50,
Seodaemun‐gu, C.P.O. Box 8044, Seoul 03722,
Republic of Korea.
Email: shinji@yuhs.ac
Abstract
The aim of this study is to provide a more accurate representation of
COVID‐19's case fatality rate (CFR) by performing meta‐analyses by continents and
income, and by comparing the result with pooled estimates. We used multiple
ABOU GHAYDA ET AL.
|
3

worldwide data sources on COVID‐19 for every country reporting COVID‐19 cases.
On the basis of data, we performed random and fixed meta‐analyses for CFR of
COVID‐19 by continents and income according to each individual calendar date.
CFR was estimated based on the different geographical regions and levels of income
using three models: pooled estimates, fixed‐and random‐model. In Asia, all three
types of CFR initially remained approximately between 2.0% and 3.0%. In the case of
pooled estimates and the fixed model results, CFR increased to 4.0%, by then gra-
dually decreasing, while in the case of random‐model, CFR remained under 2.0%.
Similarly, in Europe, initially, the two types of CFR peaked at 9.0% and 10.0%,
respectively. The random‐model results showed an increase near 5.0%. In high‐
income countries, pooled estimates and fixed‐model showed gradually increasing
trends with a final pooled estimates and random‐model reached about 8.0% and
4.0%, respectively. In middle‐income, the pooled estimates and fixed‐model have
gradually increased reaching up to 4.5%. in low‐income countries, CFRs remained
similar between 1.5% and 3.0%. Our study emphasizes that COVID‐19 CFR is not a
fixed or static value. Rather, it is a dynamic estimate that changes with time, po-
pulation, socioeconomic factors, and the mitigatory efforts of individual countries.
KEYWORDS
case fatality rate, continents, COVID‐19, proportion meta‐analysis
1|INTRODUCTION
Pandemics are defined as the global spread of epidemics, causing
excess mortality and morbidity worldwide and leading to the dis-
ruption of the social and economic status of the many affected
countries. Among other factors, globalization has enabled and
advanced sharing of information and experiences yet simulta-
neously facilitated the spread of diseases during pandemics
through global trade and travel.
1
Coronavirus disease 2019
(COVID‐19) is one among many pandemics that have occurred
throughout the history of humanity.
1,2
COVID‐19, caused by the
newly discovered severe acute respiratory syndrome coronavirus
2(SARS‐CoV‐2),
3
represents the third coronavirus outbreak of the
21st century after the 2002 SARS‐CoV and the 2012 Middle
East respiratory syndrome (MERS)‐CoV.
4
The World Health
Organization (WHO) declared COVID‐19 as a global pandemic on
March 11, 2020.
5
As of July 18, 2021, 190169 833 confirmed
cases, with 4 086 000 deaths, were identified across all WHO
regions, territories, and areas.
6
The case fatality rate (CFR) of COVID‐19 is one essential epi-
demiologic metric that aids all stakeholders to better understand the
outbreak, its characteristics, and dynamics. It remains one of the
great tools available to express the fatality of the disease. CFR has
been developed and reported in emerging infectious diseases
7,8
such
as SARS (CFR 9.6% on a global scale)
9
and MERS (CFR 34.5%).
10
Therefore, many researchers and scientists have attempted to esti-
mate the COVID‐19 CFR by simply dividing the number of confirmed
deaths by the number of reported cases or by using a simple linear
regression method.
11–19
Estimation of the CFR has many flaws and is subject to many
biases. Examples of these biases include the time lag that exists be-
tween diagnosing a case and reporting it, in addition to the variable
degree of underreporting of cases.
7,11
This is especially true at the
beginning of an epidemic, where several deaths caused by the pa-
thogen may not be reported as a consequence of the infection.
Another challenge in CFR calculation is the actual definition of cases.
COVID‐19 cases can be either defined as laboratory‐confirmed (total
cases) or recovered/died (closed cases).
20
Furthermore, CFRs are
dependent on the phases of the pandemics, which are different in
each country. Likewise, COVID‐19 associated deaths are counted
differently in different countries. Additionally, even though this might
not be considered a bias by itself, CFR is contingent on the policies,
response, and efficiency of local health care systems.
7
To overcome
some of the limitations and biases of the “traditional”way of calcu-
lating CFR, we performed proportion meta‐analyses to estimate the
average CFR for each calendar date. Proportion meta‐analyses cal-
culate the overall proportion of CFR from a set of CFR proportions
already reported and calculated in the literature, for each country and
region. Our team previously applied this method to calculate the
global CFR of COVID‐19 comparing since the outbreak of the first
confirmed case,
21
and we aim to expand this subject by continents
and territories with similar economic status for a broader perspective.
This approach is relatively novel in providing a new insight
that lays the foundation for a proper analysis of CFR. A proportion
4
|
ABOU GHAYDA ET AL.

meta‐analysis was thus carried out for CFR using the data of every
country/territory reporting COVID‐19 cases. On the basis of results,
we first performed a meta‐analysis for global COVID‐19 CFR by
continents and national income level, which may be more accurate
and less subject to distortion and biases. Besides this, this study has a
unique aspect which is confirmed by the International COVID‐19
Research Network including 172 people in 160 countries.
2|METHODS
There were several data sources collecting worldwide data reports
of COVID‐19 (https://www.worldometers.info/coronavirus/,https:
//www.ecdc.europa.eu/en/geographical‐distribution‐2019‐ncov‐
cases,https://coronavirus.jhu.edu/data/mortality,https://covid19.
who.int/). We chose sources updating data of the cumulative cases
and mortality data on a daily basis, allowing us to view past data in a
downloadable file. We extracted the country, calendar date, the
country's cumulative confirmed cases of COVID‐19 of that date,
and the country's cumulative deaths with COVID‐19 of that date.
Subsequently, proportion meta‐analyses were performed to obtain
the average CFR for each calendar day. We collected global data of
COVID‐19 confirmed cases and deaths from the European Centre
for Disease Prevention and Control website (https://www.ecdc.
europa.eu/en/geographical‐distribution‐2019‐ncov‐cases). These
data revealed each country's information from December 31,
2019 to October 30, 2020. To offset the effect of the vaccine on
the disease, the endpoint was determined before the date on which
the vaccination began. The CFR was defined using the following
mathematical equation:
number of deaths due to COVID 19
number of confirmed cases of COVID 19 × 100(%)
.
Since the number of confirmed cases and deaths are not re-
ported on a daily basis, we encountered missing data. These referred
to the reported numbers from countries that contained “blanks”, and
existed from almost every country, mostly during the early phases of
the pandemic. We decided to fill missing data by processing the data
as the number of cases in the most recent report before the blank
rather than dividing the number of cases equally among the missing
days. We adjusted the COVID‐19 data for each country according to
the calendar date of reported cases.
Using the extracted data, we performed a proportion meta‐
analysis on CFR in every country. On the basis of results, we per-
formed a meta‐analysis for global COVID‐19 CFR. Each analysis
referred to the calendar date of the reported cases.
FIGURE 1 Timeline of variables among countries with COVID‐19 reported as of October 30, 2020: (A) No. of patients, (B) pooled‐estimated
CFR, (C) fixed‐estimated CFR, and (D) random‐estimated CFR. fixed: fixed‐effect model, random: random‐effect model, and pooled: calculated
CFR based on incidence and mortality data. CFR, case fatality rate; COVID‐19, coronavirus 2019; NA, North America; No., number; SA, South
America
ABOU GHAYDA ET AL.
|
5

Analyses were carried out using MedCalc version 19.2.1 soft-
ware (MedCalc Software). Summary effects were calculated with a
95% confidence interval (CI) and between‐study heterogeneity. A
proportion meta‐analysis was carried out to estimate the summary
effects. We used figures to visually represent the summary effects
obtained by the proportion meta‐analysis of the CFR under the fixed‐
and random‐effect model. We provided a summary of the 95% CI in
Tables S1 and S2. The heterogeneity tests with Higgins' I
2
statistic
were used as an estimator of heterogeneity between studies.
22
An I
2
value less than 50% represented low or moderate heterogeneity,
while I
2
above 50% represented high heterogeneity.
22
Microsoft
Excel version 2013 was used to graph the patterns of CFR in all
countries.
3|RESULTS
Figure 1shows the number of confirmed COVID‐19 cases, the
pooled estimate of CFR, the fixed‐model meta‐analysis results, and
the random‐model meta‐analysis results over time. Figure 2shows
the same models and estimates over time according to national in-
come. Figures 3and 4show the fixed‐and random‐model meta‐
analysis results, pooled CFR estimates, and the number of confirmed
cases according to the calendar date, stratified by continent and
national income, respectively. There are organized classifications of
the analysis we performed and the numbers of the corresponding
figures (Figures 1A–Dand 2A–D[variables stratified by date],
Figures 3A–Gand 4A–C[CFR stratified by calendar date], and
Figures S1A–S3A,S2A–C,S3A–C,S4A–C, and S5 [CFRs of countries
in every continent stratified by calendar dates]). Regardless of whe-
ther the CFR was a pooled estimate, fixed‐model, or random‐model,
it was visually observed that the CFR stratified by calendar date
continuously changed over time. Due to a large amount of data, we
present the results according to each main classification.
3.1 |Outbreak characteristics of individual
continents
We compared the worldwide number of confirmed cases and the
number of confirmed cases of each continent over time (Figure 1A)
and did likewise for the pooled estimate, fixed‐model meta‐analysis
estimates, and the random‐model estimates (Figure 1B–D). Until
March 10, 2020, the graph of the worldwide cumulative number of
confirmed cases follows that of Asia, since there were few confirmed
cases from continents other than Asia. As confirmed cases increased
FIGURE 2 Timeline of variables classified grade of income among countries with COVID‐19 reported as of October 30, 2020: (A) No. of
patients, (B) pooled‐estimated CFR, (C) fixed‐estimated CFR, and (D) random‐estimated CFR. Fixed: fixed‐effect model, random: random‐effect
model, and pooled: calculated CFR based on incidence and mortality data. CFR, case fatality rate; COVID‐19, coronavirus 2019; HI, high income;
LI, low income; MI, middle income; No., number
6
|
ABOU GHAYDA ET AL.

in all continents, especially in Europe and North America, the
worldwide number of confirmed cases rapidly increased (Figure 1A).
The random‐model and the fixed‐model estimates coincided until a
certain period (March 10). After this period, the fixed‐model followed
the pooled estimates while the random‐model estimates were smaller
in comparison (Figure 1B–D).
3.2 |Comparison of COVID‐19 incidence based on
income
All enrolled countries are classified into three categories according to
income based on The World Bank stratification: high (HI), middle (MI),
and low income (LI).
23
Cases of confirmed patients increased rapidly
FIGURE 3 Timeline of CFR according to calendar date (reported as of October 30, 2020) in: (A) the whole world, (B) Asia, (C) Europe,
(D) North America, (E) South America, (F) Africa, and (G) Oceania. Fixed: fixed‐effect model, random: random‐effect model, and pooled:
calculated CFR based on incidence and mortality data. CFR, case fatality rate; No., number
ABOU GHAYDA ET AL.
|
7

after March 20 in HI countries, and the increase started from April 1 in
MI countries. There were no differences between all three income
categories until May, however, confirmed cases rapidly increased in MI
after then. Cases in LI countries were notably different than MI and HI
countries (Figure 2A). In the case of pooled estimates, cases increased
gradually since COVID‐19 emerged in MI countries but confirmed
cases in HI countries began to be identified from February 12 and
increased sharply to 8.1% until April 26. In contrast, this was first
confirmed on March 18 and rose to 3.4% in LI countries (Figure 2B).
The rest of the fixed‐and random‐model in the three categories
showed similar patterns to each other since early March (Figure 2C,D).
3.3 |CFR according to the calendar date
We also conducted a meta‐analysis of the CFR of each continent and
presented the fixed‐and random‐model meta‐analysis estimates,
pooled CFR estimates, and the number of confirmed cases according
to date (Figures 3A–Gand 4A–C).
3.3.1 |Globally
Globally, until February 19th, all three types of CFR remained ap-
proximately at 2.7% following a similar pattern. However, after
February 19, the fixed‐model results and the pooled estimate of CFRs
showed a rapid increase up to 6.6% and 7.3%, respectively. This was
continued until May, which was followed by a decreasing trend since.
In contrast, the random‐model results of CFR did not show significant
changes, moving between 3% and 4%, until May and slowly
decreased since then (Figure 3A).
In other perspectives, till March 10th, the graph for global CFR
follows the graph for the CFR for Asia as initially most of the cases
were reported from the Asian continent. However, after March 10th,
the global number of cases increased sharply.
3.3.2 |Per continent
In Asia, until February 19th, the CFR pattern was very similar to the
global CFR pattern: All three methods of CFR calculation reported
values of approximately 2.0% and 3.0% in a similar pattern. Since
then, in the case of pooled estimates and the fixed‐model results,
the values increased to cross the 4.0% mark before gradually
decreasing again. Till October 30th, the fixed model showed a CFR
of 1.6% while the random model showed 1.5%. In the case of the
random‐estimated model, it remained under 2.0% since March 6th
(Figure 3B).
In Europe, the fixed‐and random‐model results of CFR before
February 15th represent a statistical bias as there were zero
FIGURE 4 Timeline of CFR according to calendar date (reported as of October 30, 2020) in: (A) high‐income countries, (B) middle‐income
countries, and (C) low‐income countries. Fixed: fixed‐effect model, random: random‐effect model, and pooled: calculated CFR based on
incidence and mortality data. CFR, case fatality rate; No., number
8
|
ABOU GHAYDA ET AL.

confirmed cases at the time. The fixed‐model results of the CFR
showed a similar trend with the pooled estimates since March 2nd.
The results from the two methods of CFR calculation increased until
reaching between 9% and 10%, followed by a gradual decrease since
the beginning of May. After February 15th, the calculated CFR from
the random‐model approach showed a slower increase until late May,
reaching close to 5%, and then gradually decreasing to 1.9% in
October 30th (Figure 3C).
CFR patterns from North America were very similar to those
from Europe. The first confirmed case of COVID‐19 began on March
1, and all three calculated CFRs showed a sharp rise to 5.8% within
the first 3 days only, and then decreased to 1.0% again until March
20. After March 20, all three estimates of CFRs gradually rose again,
showcasing a plateau pattern: pooled estimates (about 6.0%), fixed‐
model estimates (approximately 6.0%), and the random‐model esti-
mates (about 5.0%). This result from North America is similar to other
continents, as the three CFR estimates show a noticeably decreasing
trend (Figure 3D).
The first confirmed case in South America was reported on
February 21, relatively late compared to other continents. Until
March 18, the three CFR estimates showed varying patterns, but
thereafter, gradually increased to about 5.0% (in pooled estimates
and the fixed‐model estimate) and 4.0% (in the random‐model esti-
mate). After May 14, all three CFR estimates gradually decreased and
a plateau pattern since September (about 3.0%) (Figure 3E).
Africa also showed a similar pattern of CFRs with South America,
with only a 1‐or 2‐day delay compared to South America. The first
confirmed case in Africa was reported on February 15, which is
likewise relatively late compared to the other continents. The three
CFRs increased in a similar pattern from March 20 to mid‐April,
the maximum ranging between about 4.0% and 6.0%. Afterward, the
three CFR estimates gradually decreased, and recently, the gap be-
tween them has narrowed, converging to similar values of about 2.2%
(Figure 3F).
The COVID‐19 pandemic was confirmed to have reached
Oceania on January 25, 2020 with the first confirmed case reported
in Australia. All three CFR estimates showed a similar pattern since
the end of March. Although there is a slightly decreasing pattern in
May, all three CFRs were below 2.0%. Both pooled and fixed calcu-
lated CFRs showed a rapid rise to 2.8% in early October, and then
decreased to about 2.5% again. (Figure 3G).
3.3.3 |Per income
In the HI countries, pooled estimates and the fixed‐model showed
gradually increasing trends after the three CFRs matched to 1.3% on
February 27th, and pooled estimates and the random‐model reached
about 8.0% and 4.0% in May, respectively. All three CFR estimates
had decreased since mid‐May, although the number of confirmed
cases increased rapidly since mid‐March (Figure 4A).
In MI countries, the three CFR estimates showed a similar pat-
tern since the first COVID‐19 case appeared. Starting from February
19th, the random‐model severely fluctuated. Since then, the pooled
estimates and estimates based on the fixed‐model gradually in-
creased to 1.3%, reaching up to 4.5% by March 25. From February
20, the pooled estimates and estimates based on the fixed model
rapidly increased from 2.8% to 3.4% until February 25 and then
gradually increased to 4.5% on March 25th. Similar to HI countries,
although the number of confirmed cases increased rapidly from the
end of March, all three CFR estimates decreased (Figure 4B).
Pooled estimates in the LI category were first identified relatively
late on March 18th. As of March 31st, the three CFR estimates re-
mained similar, between 1.5% and 3.0% of each other (Figure 4C).
4|DISCUSSION
In this study, we applied methods using meta‐analyses to calculate
CFR. Given that the CFR constantly fluctuates with time, location,
and population, for the first time, we, thus, calculated the fixed‐and
the random‐model results of the meta‐analysis, the pooled estimate,
and the number of total cases included in each analysis. We obtained
the time trend of CFR by calculating pooled estimates, fixed‐and
random‐effect estimates from meta‐analyses by calendar date. In this
context, it is important to view CFR as a function of time, rather than
presenting CFR as a single, absolute, and static value.
As for the patterns of CFR, there were differences among con-
tinents. In terms of the pooled estimated and fixed‐effect model,
Europe showed the highest CFR until mid‐October, followed by
North America and South America. Asia, where CFR was high when
COVID‐19 was emerging, has experienced a continuous decrease
since March 2020. Europe's high CFR also affected global CFR, which
showed a CFR value between that of Europe and North America.
Different continents have different periods of CFR increase, and
when one continent increases, the other continents show a pattern of
decrease or plateau. But overall, the difference between continents
in CFR is also related to the number of confirmed cases: Europe and
North America showed the fastest increase of confirmed cases, and
the CFR increased rapidly accordingly (Figures 1A and Figure 3). This
may be because the greater availability of testing of critically ill pa-
tients allowed for more deaths to be attributed to COVID 19.
Another reason why CFR may increase with the rapidly increasing
number of confirmed cases may be due to strain on healthcare sys-
tems to deliver high‐quality care when capacity is exceeded. In terms
of country income level, CFRs in HI countries, such as Europe and
North America, tended to increase explosively, compared to LI
countries. Of note, LI countries may have lower reporting and testing
capacities due to financial hardships, which may have led to the
underreporting of mortality cases from COVID19. Another aspect to
be considered is the fact that LI countries may have lower global
travelers compared to HI countries, and, therefore, lower global
transmission rates.
On the basis of pooled estimate, HI countries had CFRs twice as
high in comparison to MI countries (about 8.0% vs. 4.0%, respec-
tively) (Figure 4). This may relate to the fact that in HI countries there
ABOU GHAYDA ET AL.
|
9

is a higher percentage of older people above 70 years of age, who
have higher mortality and/or a higher percentage of people affected
by obesity, which also increases mortality.
24
Additionally, a rapid in-
crease in case confirmation could lead to higher mortality in some of
these countries. As we have observed from our results, we have
proven that significant differences exist between continents. One of
the factors contributing to these variations includes the population
size of countries. Countries with a relatively large population such as
the U.S. affect the overall pooled estimated and fixed‐model CFR as
they have more weight. Therefore, these CFR have a more accurate
relative representation because of this weight‐adjustment factor.
Our results show different outcomes from the CFR patterns
mentioned in other previously published papers.
11–19
First, when the
number of confirmed cases increases, CFR is not fixed and rather
increases, resulting in a sharp increase of confirmed cases. In addi-
tion, CFR seems to be relatively high in countries with HI, such as
Europe and North America. Although CFR is currently falling in these
continents, it is still relatively high compared to other territories as
the global CFR has not fallen significantly. From these results,
the CFR of COVID‐19, or any highly infectious disease, may have the
potential to be presented differently due to the epidemiologic phase
of the spread, or the characteristic of the continent we aim to
present.
It has been well‐established that CFR estimations are affected by
a multitude of biases and confounders. Consequently, the methods
used to assess CFR should be used conservatively and utilized with
caution. CFR estimates may be skewed in either direction: they have
the potential to be over‐or under‐estimated. Over‐estimation of CFR
can be a result of multiple factors. These include the inaccuracy of
the total confirmed cases, representing that denominator of the CFR
mathematical equation. This imprecision in accounting for all
laboratory‐confirmed COVID‐19 cases stems from the fact that these
figures depend on the testing abilities and strategies of the affected
countries.
During the early phases of the pandemic, testing for COVID‐19
was impacted by financial and technical challenges. Therefore, severe
cases of the disease were given priority for testing over mild and
asymptomatic cases.
25,26
This led to an overall over‐representation of
more acute cases of the disease rather than the total burden of the
pandemic. Another salient point is that at this stage of the pandemic,
precisely reporting the mortality of cases that are directly related and
secondary to COVID‐19 infection is not achievable. Actually, many
deaths that are associated with COVID‐19 might actually be sec-
ondary to fatal comorbid conditions. Therefore, over‐emphasizing the
triggering condition will potentially lead to elevated CFR estimates.
The variability and inconsistency of the medical systems' capabilities
and response to the pandemic across different geographical locations
further distort the reporting of COVID‐19 cases and deaths.
Accurate CFR calculation is contingent on a truthful estimation
of the incidence of COVID‐19 cases. Incidence of COVID‐19 cases
are inconstant and are subject to the different diagnostic criteria and
testing abilities of countries. As the disease progressed and expanded
geographically, estimating confirmed cases has seen a great variation.
This is secondary, in part, to a better understanding of the pandemic
spread and its clinical outcomes. The country‐specific screening
strategies and criteria changed in real‐time to adapt to the national
governmental and WHO recommendations and directives.
Extensive testing is one of the many factors that helped explain
the discrepancy in fatality ratio between two neighboring countries,
Germany and Italy. It has been hypothesized that extensive testing
protocol strategies adopted by Germany were able to detect
asymptomatic cases that would have been undiagnosed otherwise.
Subsequently, this had greatly impacted Germany's CFR.
27,28
On the
other hand, countries such as India and Egypt which did not adopt
large‐scale testing had an initial misleading CFR.
27
Therefore, the
response and preparedness of healthcare systems and their testing
strategies of COVID‐19 are of utmost importance. Mild or asymp-
tomatic cases might be underrepresented at the expense of an
overall presentation of hospitalized, severe, and acute hospitalized
COVID‐19 cases. This will result in artificially inflated CFR estima-
tions. Therefore, the readiness and vigilance of healthcare systems
are key in understanding and responding to the pandemic.
The direct temporal relationship between infected patients and
those who died because of the disease represents another barrier for
precise CFR estimations. A proposed modification offered a time
delay‐adjusted CFR to correct the delay between confirmation of
cases and death of patients.
11,18
This mathematical amendment
provided an average of two weeks adjustment to calculate the con-
firmed infected cases concurrently with those who passed away from
the disease. This fine‐tuned temporal adjustment methodology has
been used by researchers at Oxford University to estimate the global
COVID‐19 CFR according to the date since the start of the pan-
demic.
29
However, this approach is not without flaws. It has been
reported that even adjusting the calculations temporally does not
guarantee the preciseness of the dates of the actual infected
patients.
17
Other challenges to accurate CFRs estimation include la-
boratory positivity despite clinical recovery and time delays between
testing and reporting of the results.
13,17
In this manuscript, we re-
sorted to using the conventional methods of calculating CFR
estimates.
In the current study, we observed diverse CFRs estimations re-
sulting from our meta‐analysis of the COVID‐19 pandemic when
analyzed based on continents and levels of income. One possible
explanation is that a statistical bias has occurred because our model
included countries and groups without normalizing their numbers.
Therefore, a more standardized and homogeneous analysis of the
data is warranted in future studies. One mitigation action would be to
include results of confirmed cases only after a certain understanding
of the threshold level of these cases is achieved within the country.
Hence, we propose that the fixed‐effect model may be more accurate
and reliable than the random effect model.
Remarkably, we identified that following a concurrence in the
initial estimation of the random and fixed model, these two estimates
diverge at a certain point in time, which is approximately Day 15 from
the first identified case of every country. On Day 15 and thereafter,
we observed that the fixed and random model estimates split. On the
10
|
ABOU GHAYDA ET AL.

other hand, the fixed model continues in an analogous and close
direction of the pooled model estimation. Even though these findings
are exciting, we caution against the extrapolation of this model to
predict future CFR estimates because the pandemic is still active and
unfolding. Continents that included countries, such as China, Italy, or
Spain, have resulted in more weight at the end of the CFR trend
compared to other countries where the pandemic was still at earlier
stages.
This study is of primordial importance as it once again highlights
the healthcare discrepancies and inequalities among counties driven
by different levels of income. One essential element determining the
speed of responsiveness and preparedness competencies is public
health infrastructures directly related to the level of income and level
of country development. Government interventions to mitigate the
COVID‐19 CFR are proportional to the income level of a country.
Therefore, our study has once more shown that continents with a
high concentration of LI countries were hit the hardest, as shown by a
higher CFR estimation. Additionally, this study has not only exposed
differences between HI and LI countries with regard to CFR esti-
mation, geographical differences were apparent among nations of
similar income and development, uncovering gaps and needs in their
respective healthcare system. For instance, differences in CFR
estimation in Asia compared to North America could be partially
explained by the number of beds per 1000 inhabitants. South Korea
possesses on average 12.3 beds/1000 inhabitants, compared to 2.8
beds/1000 in the US.
30
Socioeconomic disparities in health are well known and estab-
lished. Pandemics such as COVID‐19 has only exacerbated its man-
ifestations. Variation in CFRs when estimated according to continents
and income levels is one of the indicators of these inequalities. The
variation shown in this manuscript has provided further evidence
supporting efforts to mitigate health inequities. We have demon-
strated that when looking at the patterns of CFR, there are differ-
ences among continents. Overall, the difference between continents
in CFR is also related to the number of confirmed cases. Additionally,
we showed that notable CFR differences exist between continents.
This stems from the fact that large population size affects the overall
pooled estimated CFR and fixed‐model CFR. Therefore, these CFRs
have a more accurate relative representation because of this weight‐
adjustment factor. As such, we caution that this indicator alone
should not be used in isolation for COVID‐19 decision making. There
is a need to examine CFR in parallel with other indicators such as
synthetic CFRs and age‐standardized mortality rates. As the pan-
demic is still in progress, it is uncertain whether the CFR time‐trend
could be explained by the proposed epidemic stages of COVID‐19.
Future studies and discussions, especially toward the end of the
pandemic, are needed to satisfy the unmet need for a consensus on
the definition of each phase.
ACKNOWLEDGMENTS
The case fatality rate (CFR) meta‐analyses of COVID‐19 according to
continents and income on this study are novel and have not
been published before, but a part of this study on the global CFR
meta‐analysis by calendar date was somewhat overlapped with the
authors' previous work which was published in August 2020 in the
International Journal of Infectious Diseases.
CONFLICT OF INTERESTS
The authors declare no conflict of interest.
AUTHOR CONTRIBUTIONS
All authors made substantial contributions to all of the following:
conception and design of the study, data acquisition, or analysis and
interpretation of data; drafting or critical revision of the article for
intellectual content; and final approval of the version to be submitted.
DATA AVAILABILITY STATEMENT
The supporting data are available within the article and
Supplementary Files.
ORCID
Umesh Jayarajah http://orcid.org/0000-0002-0398-5197
Mohammed Mamun http://orcid.org/0000-0002-1728-8966
Atte Oksanen http://orcid.org/0000-0003-4143-5580
Jae Il Shin http://orcid.org/0000-0003-2326-1820
REFERENCES
1. Akin L, Gozel MG. Understanding dynamics of pandemics. Turk
J Med Sci. 2020;50(SI‐1):515‐519.
2. Potter CW. A history of influenza. J Appl Microbiol. 2001;91(4):
572‐579.
3. Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus
outbreak of global health concern. Lancet. 2020;395(10223):
470‐473.
4. Guarner J. Three emerging coronaviruses in two decades. Am J Clin
Pathol. 2020;153(4):420‐421.
5. WHO. WHO Timeline—COVID‐19. Publishing who web. 2020.
Accessed April 13, 2020. https://www.who.int/news‐room/detail/
08‐04‐2020‐who‐timeline‐‐‐covid‐19
6. (COVID‐19) WCd. Weekly epidemiological update. 2021. Accessed
March 20, 2021. https://www.who.int/publications/m/item/weekly‐
epidemiological‐update‐‐‐16‐march‐2021
7. Battegay M, Kuehl R, Tschudin‐Sutter S, Hirsch HH, Widmer AF,
Neher RA. 2019‐novel coronavirus (2019‐nCoV): estimating the
case fatality rate—a word of caution. Swiss Med Wkly. 2020;150:
w20203.
8. Reich NG, Lessler J, Cummings DA, Brookmeyer R. Estimating
absolute and relative case fatality ratios from infectious disease
surveillance data. Biometrics. 2012;68(2):598‐606.
9. Donnelly CA, Ghani AC, Leung GM, et al. Epidemiological determi-
nants of spread of causal agent of severe acute respiratory syn-
drome in Hong Kong. Lancet. 2003;361(9371):1761‐1766.
10. Majumder MS, Rivers C, Lofgren E, Fisman D. Estimation of MERS‐
coronavirus reproductive number and case fatality rate for the spring
2014 Saudi Arabia outbreak: insights from publicly available data.
PLoS Curr. 2014;6: doi:10.1371/currents.outbreaks.98d2f8f3382d84f
390736cd5f5fe133c
11. Baud D, Qi X, Nielsen‐Saines K, Musso D, Pomar L, Favre G. Real
estimates of mortality following COVID‐19 infection. Lancet Infect
Dis. 2020;20(7):773.
12. Kim DD, Goel A. Estimating case fatality rates of COVID‐19. Lancet
Infect Dis. 2020;20(7):773‐774.
ABOU GHAYDA ET AL.
|
11

13. Lipsitch M. Estimating case fatality rates of COVID‐19. Lancet Infect
Dis. 2020;20(7):775.
14. Onder G, Rezza G, Brusaferro S. Case‐fatality rate and character-
istics of patients dying in relation to COVID‐19 in Italy. JAMA. 2020;
323(18):1775‐1776.
15. Öztoprak FJA. Case fatality rate estimation of COVID‐19 for
European countries: Turkey's current scenario amidst a global pan-
demic; comparison of outbreaks with European countries. Eurasian
J Med Oncol. 2020;4(2):149‐159.
16. Rajgor DD, Lee MH, Archuleta S, Bagdasarian N, Quek SC. The many
estimates of the COVID‐19 case fatality rate. Lancet Infect Dis. 2020;
20(7):776‐777.
17. Spychalski P, Blazynska‐Spychalska A, Kobiela J. Estimating case
fatality rates of COVID‐19. Lancet Infect Dis. 2020;20(7):
774‐775.
18. Wilson N, Kvalsvig A, Barnard LT, Baker MG. Case‐fatality risk es-
timates for COVID‐19 calculated by using a lag time for fatality.
Emerg Infect Dis. 2020;26(6):1339‐1441.
19. Yang S, Cao P, Du P, et al. Early estimation of the case fatality rate of
COVID‐19 in mainland China: a data‐driven analysis. Ann Transl Med.
2020;8(4):128.
20. Pueyo T. Coronavirus: why you must act now. 2020. Accessed
March 18, 2020. https://medium.com/@tomaspueyo/coronavirus‐
act‐today‐or‐people‐will‐die‐f4d3d9cd99ca
21. Ghayda RA, Lee KH, Han YJ, et al. Estimation of global case
fatality rate of coronavirus disease 2019 (COVID‐19) using meta‐
analyses: Comparison between calendar date and days since the
outbreak of the first confirmed case. Int J Infect Dis. 2020;100:
302‐308.
22. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring incon-
sistency in meta‐analyses. BMJ. 2003;327(7414):557‐560.
23. Bank TW. The world by income and region. 2020. Accessed July 19,
2020. https://datatopics.worldbank.org/world‐development‐indicators/
images/figures‐png/world‐by‐income‐sdg‐atlas‐2018.pdf
24. Dietz W, Santos‐Burgoa C. Obesity and its implications for
COVID‐19 mortality. Obesity. 2020;28(6):1005.
25. Rinaldi G, Paradisi M. An empirical estimate of the infection fatality
rate of COVID‐19 from the first Italian outbreak. medRxiv. 2020.
doi:10.1101/2020.04.18.20070912
26. Tang YW, Schmitz JE, Persing DH, Stratton CW. Laboratory diag-
nosis of COVID‐19: current issues and challenges. J Clin Microbiol.
2020;58(6):e00512.
27. Abu Hammad O, Alnazzawi A, Borzangy SS, et al. Factors influencing
global variations in COVID‐19 cases and fatalities; A review.
Healthcare. 2020;8:216.
28. Paital B, Das K, Parida SK. Inter nation social lockdown versus
medical care against COVID‐19, a mild environmental insight with
special reference to India. Sci Total Environ. 2020;728:138914.
29. Medicine TCfE‐B. Global Covid‐19 case fatality rates. May 15, 2020.
Accessed May 17, 2020. https://www.cebm.net/covid‐19/global‐
covid‐19‐case‐fatality‐rates/
30. Hospital beds. 2018. Accessed: June 5, 2021. https://www.oecd‐
ilibrary.org/content/data/0191328e‐en
SUPPORTING INFORMATION
Additional supporting information may be found in the online version
of the article at the publisher’s website.
How to cite this article: Abou Ghayda R, Lee KH, Han YJ,
et al. Global case fatality rate of coronavirus disease 2019 by
continents and national income: a meta‐analysis. J Med Virol.
2022;1‐12. doi:10.1002/jmv.27610
12
|
ABOU GHAYDA ET AL.
View publication statsView publication stats