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Estimating Case Fatality and Case 
Recovery Rates of COVID-19: is 
this the right thing to do? 
 
Morteza Abdullatif Khafaie1 and 
Fakher Rahim2* 
 
 
1Social Determinants of Health Research 
Center, Ahvaz Jundishapur University of 
Medical Sciences, Ahvaz, Iran;  
2Thalassemia & Hemoglobinopathy Research 
Center, Health Research Institute, Ahvaz 
Jundishapur University of Medical Sciences, 
Ahvaz, Iran;  
 
 
 
*Corresponding Author email: 
bioinfo2003@gmail.com 

 
Vol. 10, No. 1 (2021)   |   ISSN 2166-7403 (online)  
DOI 10.5195/cajgh.2021.489 |   http://cajgh.pitt.edu 



 
 
KHAFAIE & RAHIM 

 
This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 
This journal is published by the University Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 
 

Central Asian Journal of Global Health 
Volume 10, No. 1 (2021) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2021.489 | http://cajgh.pitt.edu 

 
 

Abstract 

Introduction: Case fatality rates (CFRs) and case recovery rates (CRRs) are frequently used to define health consequences related 

to specific disease epidemics, including the COVID-19 pandemic. This study aimed to compare various methods and models for 

calculating CFR and CRR related to COVID-19 based on the global and national data available as of April 2020.  

Methods: This analytical epidemiologic study was conducted based on detailed data from 210 countries and territories worldwide 

in April 2020. We used three different formulas to measure CFR and CRR, considering all possible scenarios. 

Results: We included information for 72 countries with more than 1,000 cases of COVID-19. Overall, using first, second, and third 

estimation models, the CFR were 6.22%, 21.20%, and 8.67%, respectively; similarly, the CRR was estimated as 23.21%, 78.86%, 

32.23%, respectively. We have shown that CFRs vary so much spatially and depend on the estimation method and timing of case 

reports, likely resulting in overestimation. 

Conclusion: Even with the more precise method of CFRs estimation, the value is overestimated. Case fatality and recovery rates 

should not be the only measures used to evaluate disease severity, and the better assessment measures need to be developed as 

indicators of countries’ performance during COVID-19 pandemic. 

Keywords: Coronavirus; COVID-19; Case fatality rates; CFRs; Case recovery rates; CRRs  

 
Estimating Case Fatality and Case 
Recovery Rate of COVID-19: is this 
the right thing to do? 
 
Morteza Abdullatif Khafaie1 and 
Fakher Rahim2*  
 
 
1Social Determinants of Health Research 
Center, Ahvaz Jundishapur University of 
Medical Sciences, Ahvaz, Iran;  
2Thalassemia & Hemoglobinopathy 
Research Center, Health Research 
Institute, Ahvaz Jundishapur University of 
Medical Sciences, Ahvaz, Iran;  
 
 
*Corresponding Author email: 
bioinfo2003@gmail.com 

Research 

In late December 2019, a series of unexplained 
pneumonia cases were reported in Wuhan, China, which 
led government and researchers in China to take quick 
action to control its spread and start a large number of 
etiologic studies.1 On January 30,  2020, WHO declared 
the epidemic of the virus as a public health emergency 
with international concern (PHEIC).2 COVID-19 has 
spread to more than 210 countries and territories around 
the world, and as of December 2020, nearly 1.7 million 
lives have been lost.3 The virus spreads through droplets 
after infected persons cough or sneeze, which may enter 
the body through inhalation or contact with contaminated 
surfaces, and then touching the eyes, nose, and mouth.5 
According to scientists, the average time required for 
symptoms to appear is 5 days, but in some cases and 
situations, it may take much longer, as the virus' 
incubation period lasts up to 14 days.6 

Case fatality rates (CFRs) and case recovery 
rates (CRRs) are frequently used to define health 
consequences related to certain disease epidemics, as 
well as for the COVID-19 outbreak.7 CFR is the 



 
 
CENTRAL ASIAN JOURNAL OF GLOBAL HEALTH 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 
 

This journal is published by the University Library System of the University of Pittsburgh as part  
of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 
Central Asian Journal of Global Health 

Volume 10, No. 1 (2021) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2021.489 | http://cajgh.pitt.edu 

 
 

proportion of deaths due to a specified health condition 
compared to total infected cases.8 Calculations are based 
on the controversial assumption that all of patients were 
tested. COVID-related CFR might be either 
overestimated or underestimated depending on if 
calculations are based on every confirmed case or only 
those cases who have recovered or died. Specialists in 
epidemiology have proposed different scenarios for 
calculating CFR, each with its advantages and 
disadvantages.9-11 CRR is the proportion of recovered or 
discharged individuals with a specified health condition 
compared to total infected cases.12 

The absence of reliable numbers of infected 
cases for the entire population could lead to inaccurate 
calculation of the CFR and CRR due to lack of a valid 
denominator. There has been an urgent need for these 
reported data to be openly available, so estimates of CFR 
and CRR can be estimated as accurately as possible. This 
study aimed to compare various introduced methods and 
models for the calculation of CFR and CRR related to 
COVID-19 over a time based on the recent global and 
national data. 

 

Methods 

Design and setting 

This analytical epidemiologic study was 
conducted using detailed data from 210 countries and 
territories available around the world as of April 17,  
2020. The current survey was approved by the Ahvaz 
Jundishapur University of Medical Sciences Ethical 
Committee. 

Source of data and procedure 

We used a method that our research team 
recently published to retrieve data and estimate CFR and 
CRR.13 In brief, the data about total cases, total deaths, 
and total recovered cases, alongside total screening tests 
used to diagnose COVID-19, were collected from the 
world’s most acceptable and accurate data repositories, 

including WHO14, Worldometer4, the Centers for Disease 
Control and Prevention, and the Morbidity and Mortality 
Weekly Report series (provided from Centers for Disease 
Control and Prevention)15, consistent with the user’s 
guide of data sources for patient registries.16 The data 
analyses were performed between April 17-19, 2020. 
Data were measured and analyzed for each country, and 
CFR and CRR for countries with ≥1,000 cases (n=72) are 
presented in the main tables. Data for the remaining 
countries with <1,000 cases (n=138) are accessible in the 
supplementary tables.  

Measuring the CFR and CRR 

Given the difficulty of estimating CFR and CRR 
accurately during the ongoing COVID-19 pandemic, we 
used three different methods to estimate CFR and CRR, 
considering all possible scenarios (Figure 1).  

Formula I 

According to Battegay et al., we used the 
proportion of total deaths and recovered cases of 
COVID-19 disease to total cases of disease at global and 
national levels to estimate CFRs and CRRs, 
respectively.17 

CFR= (Total deaths attributed to COVID-19/ 
Total cases of COVID-19) * 100 

CRR= (Total recovered individuals attributed to 
COVID-19/Total cases of COVID-19) * 100 

Formula II 

Another method, proposed by Ghani et al., to 
estimate CFRs and CRRs is merely considering the 
summation of the current total deaths plus current total 
recovered as the denominator.18 

CFR= Total deaths attributed to  
COVID-19/(deaths+recovered) 

CRR= Total recovered individuals attributed  
to COVID-19/(deaths+recovered) 

 



 
 
KHAFAIE & RAHIM 

 
This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 
This journal is published by the University Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 
 

Central Asian Journal of Global Health 
Volume 10, No. 1 (2021) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2021.489|http://cajgh.pitt.edu 

 
 

Formula III 

This formula accounts for the lag time between 
an individual’s disease onset and death/recovery.4 T is the 
average time from emerging symptoms until the onset of 
death (or recovery). Since most countries had not adopted 
well-performing detection systems, to avoid 
overestimating the rates, T was considered 7 days, which 
is the difference of the minimum reported time between 
the onset of symptom to outcomes and the maximum 
incubation period.19 

CFR=Deaths at day x/Total cases at day x–T 
CRR=Recovered at day x/Total cases at day x–T 

Statistical analysis 

Data management and calculation were 
conducted in Microsoft Excel, and results (CF and CR 
rates) were tabulated for the three standard methods of 
rate estimation by countries. We reported information for 
the 72 countries in the body of the paper with more than 
1,000 cases of COVID-19 in the main paper, and the 
estimates of the remaining countries (n=138) were 
provided as supplementary tables.  Overall rates for the 
world were also calculated. 

 

 

Figure 1. Schematic illustration of three different conceivable models for CFR and CRR calculation



 
 
KHAFAIE & RAHIM 

 
This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 
This journal is published by the University Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 
 

Central Asian Journal of Global Health 
Volume 10, No. 1 (2021) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2021.489 | http://cajgh.pitt.edu 

 
 

Results 

The total number of reported cases from the 
beginning of the epidemic until April 17, 2020 was 
1,925,179. The USA had the highest number of COVID-
19 cases detected (n=578,155; 30.5% of global cases), 
followed by Spain and Italy with 170,099 (8.84%) and 
159,516 (8.29%) cases, respectively. Table 1 shows 
global as well as national data on the COVID-19 health-
related consequences. Global CFRs for COVID-19 
estimated by first, second, and third methods were 
6.22%, 21.20%, and 8.67%, respectively. Similarly, 
CRRs were estimated as 23.21%, 78.86%, 32.23%. The 
third method, which is the more precise and widely 
accepted method, shows that Algeria (19.17%), Belgium 
(18.39%), and the UK (18.38%) account for the highest 
CFRs. Data about all countries with confirmed cases less 
than 1,000 were presented in Table S1. 

Considering the first estimation model, the 
highest CRRs were in China, South Korea, and Iran. 
Given the second estimation model, most countries such 
as Germany, China, Iran, Switzerland, Canada, and 
Austria had CRR above 90%. Based on the third 
estimation model, several countries, including China, 
Turkey, Russia, Sweden, and Peru, had CRRs higher than 
90% (Table 1). 

The overall lowest and highest CFR and CRR in 
the European continent were estimated by model 1 and 
model 2, respectively (Table 2). The highest CFR was 
observed in the European continent using models 1 and 
3; model 2 highlighted the North American continent as 
the region with the highest CFR (Table 2). Moreover, the 
highest CRR was observed in Oceania in all three models 
(Table 2).  

The impact of important contributing factors 
affecting CFR and CRR such as the country’s population, 
GDP, number of hospital beds per 1,000 people, number 
of ICU beds per 100,000 people, and number of 
ventilators were assessed in the three different proposed 
models of estimation (Table S2). Comparison among 
countries with high, moderate, and low CFR was 
illustrated in Figure 2. 

Though the analysis showed a statistically non-
significant pattern for all variables of interest, models 1 
and 2 potentially provide more accurate estimates of CFR 
and CRR (Table 3). The WHO reported CFR for COVID-
19 as 2%20; other calculated values are shown based on 
data and available literature in countries and at the global 
level (Table 4). 

 

Country Total 
Recovered 

Total 
deaths 

Total 
cases 

Active 
cases 

Model 1 Model 2 Model 3 
CFR1 CRR1 CFR2 CRR2 CFR3 CRR3 

USA 3,950,354 198,128 6,676,601 2,528,119 2.97% 59% 4.78% 95.22% 0.85% 57.09% 
India 3,702,595 78,614 4,754,356 973,147 1.65% 78% 2.08% 97.92% 0.49% 75.34% 
Brazil 3,553,421 131,274 4,315,858 631,163 3.04% 82% 3.56% 96.44% 0.63% 80.79% 
Russia 873,535 18,484 1,057,362 165,343 1.75% 83% 2.07% 97.93% 2.20% 78.31% 
Peru 559,321 30,593 722,832 132,918 4.23% 77% 5.19% 94.81% 0.85% 61.54% 
Colombia 592,820 22,734 708,964 93,410 3.21% 84% 3.69% 96.31% 2.98% ------ 
Mexico 467,525 70,604 663,973 125,844 10.63% 70% 13.12% 86.88% 0.44% 65.32% 
South Africa 576,423 15,427 648,214 56,364 2.38% 89% 2.61% 97.39% 0.90% 77.98% 
Spain N/A 29,747 576,697 N/A 5.16% ----- ------ ------ 0.14% ------ 
Argentina 409,771 11,263 546,481 125,447 2.06% 75% 2.68% 97.32% 0.85% 60.79% 
Chile 404,919 11,895 432,666 15,852 2.75% 94% 2.85% 97.15% 1.06% 86.39% 
Iran 344,516 23,029 399,940 32,395 5.76% 86% 6.27% 93.73% 0.68% 81.57% 
France 89,059 30,910 373,911 253,942 8.27% 24% 25.76% 74.24% 1.93% 20.91% 
UK N/A 41,623 365,174 N/A 11.40% ----- ------- ------ 1.24% ------- 



 
 
KHAFAIE & RAHIM 

 
This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 
This journal is published by the University Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 
 

Central Asian Journal of Global Health 
Volume 10, No. 1 (2021) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2021.489|http://cajgh.pitt.edu 

 
 

Country 
Total 

Recovered 
Total 
deaths 

Total 
cases 

Active 
cases 

Model 1 Model 2 Model 3 
CFR1 CRR1 CFR2 CRR2 CFR3 CRR3 

Bangladesh 238,271 4,702 336,044 93,071 1.40% 71% 1.94% 98.06% 0.28% 68.78% 
Saudi Arabia 301,836 4,240 325,050 18,974 1.30% 93% 1.39% 98.61% 0.51% 86.03% 
Pakistan 289,429 6,379 301,481 5,673 2.12% 96% 2.16% 97.84% 0.26% 93.21% 
Turkey 257,731 6,999 289,635 24,905 2.42% 89% 2.64% 97.36% 0.32% 88.52% 
Iraq 221,283 7,941 286,778 57,554 2.77% 77% 3.46% 96.54% 0.23% 76.62% 
Italy 213,191 35,603 286,297 37,503 12.44% 74% 14.31% 85.69% 0.38% 71.24% 
Germany 235,300 9,427 260,546 15,819 3.62% 90% 3.85% 96.15% 0.87% 89.92% 
Philippines 187,116 4,292 257,863 66,455 1.66% 73% 2.24% 97.76% 0.06% 71.58% 
Indonesia 152,458 8,650 214,746 53,638 4.03% 71% 5.37% 94.63% 0.09% 67.64% 
Israel 113,496 1,103 152,722 38,123 0.72% 74% 0.96% 99.04% 0.94% 68.37% 
Ukraine 68,346 3,148 151,859 80,365 2.07% 45% 4.40% 95.60% 0.36% 36.87% 
Canada 120,075 9,170 136,141 6,896 6.74% 88% 7.10% 92.90% 0.01% 87.39% 
Bolivia 82,796 7,297 125,982 35,889 5.79% 66% 8.10% 91.90% 0.23% 63.32% 
Qatar 118,475 205 121,523 2,843 0.17% 97% 0.17% 99.83% 0.16% 91.46% 
Ecuador 91,242 10,864 116,451 14,345 9.33% 78% 10.64% 89.36% 0.33% 76.72% 
Kazakhstan 100,615 1,634 106,803 4,554 1.53% 94% 1.60% 98.40% 0.53% 91.90% 
Dominican Republic 76,531 1,953 103,092 24,608 1.89% 74% 2.49% 97.51% 0.62% 71.19% 
Romania 42,811 4,127 102,386 55,448 4.03% 42% 8.79% 91.21% 0.07% 40.11% 
Panama 73,476 2,155 101,041 25,410 2.13% 73% 2.85% 97.15% 0.01% 71.66% 
Egypt 83,261 5,627 100,856 11,968 5.58% 83% 6.33% 93.67% 0.08% 80.48% 
Kuwait 84,404 558 94,211 9,249 0.59% 90% 0.66% 99.34% 0.26% 80.19% 
Belgium 18,709 9,923 92,478 63,846 10.73% 20% 34.66% 65.34% 0.83% 18.99% 
Oman 83,325 762 88,337 4,250 0.86% 94% 0.91% 99.09% 0.25% 93.35% 
Sweden N/A 5,846 86,505 N/A 6.76% ----- 100.00% ----- 0.49% ----- 
China 80,399 4,634 85,184 151 5.44% 94% 5.45% 94.55% 0.19% 92.96% 
Morocco 65,867 1,553 84,435 17,015 1.84% 78% 2.30% 97.70% 0.61% 76.91% 
Guatemala 70,403 2,949 81,658 8,306 3.61% 86% 4.02% 95.98% 0.25% 86.18% 
Netherlands N/A 6,253 81,012 N/A 7.72% ----- 100.00% ----- 0.28% ----- 
UAE 68,983 399 78,849 9,467 0.51% 87% 0.58% 99.42% 0.11% 80.38% 
Japan 66,280 1,423 74,544 6,841 1.91% 89% 2.10% 97.90% 0.38% 87.58% 
Belarus 72,547 744 73,975 684 1.01% 98% 1.02% 98.98% 0.23% 97.45% 
Poland 59,725 2,182 73,650 11,743 2.96% 81% 3.52% 96.48% 0.21% 80.92% 
Honduras 17,760 2,065 67,136 47,311 3.08% 26% 10.42% 89.58% 0.15% 20.56% 
Ethiopia 24,493 996 63,888 38,399 1.56% 38% 3.91% 96.09% 0.40% 36.44% 
Portugal 43,894 1,860 63,310 17,556 2.94% 69% 4.07% 95.93% 0.14% 67.01% 
Venezuela 47,729 477 59,630 11,424 0.80% 80% 0.99% 99.01% 0.57% 77.97% 
Bahrain 53,192 211 59,586 6,183 0.35% 89% 0.40% 99.60% 0.32% 85.07% 
Singapore 56,699 27 57,357 631 0.05% 99% 0.05% 99.95% 0.28% 97.64% 
Nigeria 44,088 1,078 56,177 11,011 1.92% 78% 2.39% 97.61% 0.35% 76.45% 
Costa Rica 20,928 590 55,454 33,936 1.06% 38% 2.74% 97.26% 0.16% 32.11% 
Nepal 37,524 336 53,120 15,260 0.63% 71% 0.89% 99.11% 0.81% 67.71% 
Algeria 33,875 1,605 48,007 12,527 3.34% 71% 4.52% 95.48% 0.21% 68.64% 
Uzbekistan 43,511 386 46,850 2,953 0.82% 93% 0.88% 99.12% 0.05% 90.71% 
Switzerland 38,500 2,020 46,704 6,184 4.33% 82% 4.99% 95.01% 0.11% 76.85% 
Armenia 41,605 911 45,675 3,159 1.99% 91% 2.14% 97.86% 0.05% 89.51% 



 
 
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This work is licensed under a Creative Commons Attribution 4.0 United States License. 
 

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of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 
Central Asian Journal of Global Health 

Volume 10, No. 1 (2021) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2021.489 | http://cajgh.pitt.edu 

 
 

Country 
Total 

Recovered 
Total 
deaths 

Total 
cases 

Active 
cases 

Model 1 Model 2 Model 3 
CFR1 CRR1 CFR2 CRR2 CFR3 CRR3 

Ghana 44,342 286 45,434 806 0.63% 98% 0.64% 99.36% 0.02% 94.92% 
Kyrgyzstan 40,779 1,063 44,828 2,986 2.37% 91% 2.54% 97.46% 0.62% 89.86% 
Moldova 30,437 1,117 42,714 11,160 2.62% 71% 3.54% 96.46% 0.32% 69.91% 
Afghanistan 31,234 1,420 38,641 5,987 3.67% 81% 4.35% 95.65% 0.03% 79.89% 
Azerbaijan 35,607 559 38,172 2,006 1.46% 93% 1.55% 98.45% 0.15% 90.23% 
Kenya 22,771 619 35,969 12,579 1.72% 63% 2.65% 97.35% 0.02% 60.83% 
Czechia 21,205 453 35,401 13,743 1.28% 60% 2.09% 97.91% 0.25% 56.27% 
Austria 26,579 754 32,696 5,363 2.31% 81% 2.76% 97.24% 0.09% 78.70% 
Serbia 31,100 731 32,300 469 2.26% 96% 2.30% 97.70% 0.03% 91.26% 
Ireland 23,364 1,783 30,730 5,583 5.80% 76% 7.09% 92.91% 0.19% 73.41% 
Palestine 19,979 210 29,906 9,717 0.70% 67% 1.04% 98.96% 0.19% 66.07% 
Paraguay 13,679 514 27,324 13,131 1.88% 50% 3.62% 96.38% 0.08% 45.81% 
El Salvador 17,874 782 26,851 8,195 2.91% 67% 4.19% 95.81% 0.19% 65.70% 
World 20,811,464 924,577 28,943,657 7,207,616 3.19% 72% 4.25% 95.75% 0.73% 68.72% 

Table 1. The comparison of case fatality rate (CFR) and case recovery rate (CRR) by model between 72 different 
countries with at least 1,000 total cases. Data retrieved 13 September 2020.  

 

 

 

Continents 
Number  

of  
countries 

Total  
recovered 

Total  
deaths 

Total  
cases 

Active  
cases 

Model 1 Model 2 Model 3 

CFR1 CRR1 CFR2 CRR2 CFR3 CRR3 

Europe 48 2,239,376 212,327 4,053,217 1,601,514 5.24% 55% 8.66% 55% 2.47% 47.72% 

North America 39 4,835,653 289,160 7,950,455 2,825,642 3.64% 61% 5.64% 61% 0.52% 59.77% 

Asia 49 6,843,427 162,543 8,485,682 1,479,712 1.92% 81% 2.32% 81% 0.17% 78.56% 

South America 14 5,771,324 227,166 7,073,893 1,075,403 3.21% 82% 3.79% 82% 0.05% 81.19% 

Africa 57 1,096,779 32,556 1,352,693 223,358 2.41% 81% 2.88% 81% 0.08% 80.68% 

Oceania 7 25,940 843 29,967 3,184 2.81% 87% 3.15% 87% 0.27% 69.35% 

World 210 20,813,150 924,610 28,946,628 7,208,868 3.19% 72% 4.25% 72% 0.41% 70.36% 

Table 2. Continental comparison of CFRs and CRRs using three various proposed estimation methods

 

 

 

 

 

 

 

 

 



 
 
KHAFAIE & RAHIM 

 
This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 
This journal is published by the University Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 
 

Central Asian Journal of Global Health 
Volume 10, No. 1 (2021) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2021.489 | http://cajgh.pitt.edu 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

  
 

Figure 2. Comparison between countries with low, moderate, and high CFR 

 

Variables 
Model 1 Model 2 Model 3 

rs P rs P rs P 
Population With CFR 0.088 0.597 -0.078 0.637 0.124 0.457 
Population With CRR 0.098 0.556 0.078 0.637 -0.082 0.622 
GDP With CFR 0.152 0.361 -0.029 0.859 0.266 0.106 
GDP With CRR 0.121 0.467 0.029 0.859 0.005 0.974 
NHB With CFR -0.167 0.315 -0.149 0.637 0.192 0.247 
NHB With CRR 0.124 0.457 0.149 0.371 0.121 0.468 
NIB With CFR 0.112 0.501 0.014 0.933 0.029 0.073 
NIB With CRR 0.122 0.462 -0.014 0.933 0.217 0.188 
Number of Ventilators With CFR -0.221 0.181 -0.009 0.953 0.041 0.803 
Number of Ventilators With CRR -0.109 0.511 0.009 0.953 -0.088 0.0595 

Note: NHB: Number of Hospital Beds per 1000 people; NIB: Number of ICU Beds per 100,000 people; CFR: Case Fatality Rate; CRR: Case 

Recovery Rate; rs: Pearson Correlation Coefficient; P: P-value  

Table 3. The estimated CFRs and CRRs against the county’s population, GDP, number of hospital beds per 1,000 
people, number of ICU beds per 100,000 people, and number of ventilators between the three different proposed 
models of estimation.



 
 
KHAFAIE & RAHIM 

 
This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 
This journal is published by the University Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 
 

Central Asian Journal of Global Health 
Volume 10, No. 1 (2021) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2021.489 | http://cajgh.pitt.edu 

 
 

Study ID (reference) Country Population Method CFR Estimation  
level 

Change et al, 2020 (19) China >30 Chinese locations and 
other countries/regions 

Model 1 
(Computational using 

Bayes Theorem) 
3.7% Local 

Yang et al., 2020 (20) China 
205 patients with cancer and 
laboratory-confirmed SARS-

CoV-2 infection 
Model 1 

Hematological 
malignancies: 41% 
Solid tumors: 3.28 

Local 

Turk et al., 2020 (21) USA 
474 people with intellectual 

and developmental disabilities 
(IDD) 

Model 1 (CFR within 
30 days) 5.1% Local 

Capalbo et al., 2020 (22) Italy 
182 patients with laboratory-

confirmed SARS-CoV-2 
infection 

Model 2 12.1% Local 

Dongarwar and Salihu, 2020 (23) USA 
A total of 213 countries had 

been affected by the disease as 
of May 6, 2020 

Model 1 Asia: 3.5 
Australia: 1.4% Global 

Peng et al., 2020 (24) China 
82,836 patients with COVID-

19 were confirmed in 
mainland China 

Model 1 5.6% Local 

Abdollahi et al., 2020 (3) 
Canada 

and 
USA 

Using data for COVID-19 
confirmed cases 

Model 1 (CFR within 
30 days) 

Canada: 4.9% 
USA: 5.4% Local 

Undela and Gudi, 2020 (25) India 2,761,121 confirmed cases Model 1 7.0% Global 

Mi et al, 2020 (26) China 82,735 confirmed cases Model 1 5.7% Local 

Khafaie and Rahim, 2020 (12) Iran 33,570 confirmed cases Model 1 (CFR within 
30 days) 3.61 Global 

Table 4. Reported values and methods to calculate CFR from the literature on COVID-19 

 

Discussion 

We have presented a global consequence of 
COVID-19 in terms of CFRs and CRRs using three 
different estimation methods. By April 18, 2020, 
deceased cases reached 119,699, according to data from 
Worldometer.20 

We have shown that the CFR varies greatly 
geographically and even depends on the method of 
estimation implemented and case reports' timing. As a 

clear example of this, a CFR of 0.31 was estimated in 
Singapore and 98.82 in the UK. Even with the more 
precise CFR estimation method,4 we hypothesize that the 
value is still overestimated. Other factors that could 
contribute to varying estimations are the pandemic stage, 
number and types of tests performed, strategies of 
diagnostics, capability of the healthcare system, and the 
reporting system. For example, the USA had a significant 
increase in testing capacity, but the preliminary estimates 
of CFRs did not change dramatically (CFR=3.07 on 



 
 
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March 12, 2020 vs. 4.03 on April 18, 2020).13 As of April 
2020, most countries were testing people with severe 
symptoms, mainly those needing hospitalization. The 
important point is that it is still unclear how many cases 
of COVID-19 were asymptomatic, or whether similar 
standards for testing are being performed between 
countries. Cross-country comparisons cannot be reliable 
indicators, unless countries are comparable or important 
factors are adjusted for. 

However, if all these possible limitations are 
carefully acknowledged, CFR may help better appreciate 
the severity of COVID-19 and required mitigation steps. 
Given the impossibility of accurately estimating CFR and 
CRR while the COVID-19 pandemic has not yet ended, 
using different methods to estimate CFR and CRR, 
considering all possible scenarios, could help us to better 
estimate disease severity across different countries. Some 
researchers prefer to use the proportion of total deaths 
and recovered cases of COVID-19 disease to total disease 
cases at global and national levels to estimate CFRs and 
CRRs. After the end of the pandemic, observing CFR and 
CRR using this method can be done, but while the 
pandemic is still ongoing, this method is naïve and could 
be misleading.  

The immune response to COVID-19 is not fully 
understood yet. Studies suggested the possible likelihood 
of relapse in recovered patients and existing models do 
not account for that. However, method III highly depends 
on the selected time period from where total cases are 
considered as the denominator.18 The estimation of CFR 
using method III (6.22%) is similar to the method I 
(8.67%). However, because all the cases have not been 
resolved, method III can still be assumed to be the more 
precise.18 Otherwise, we suggest merely extracting the 
active cases from the denominator while using method I. 
Undiagnosed cases are important for the disease spread, 
so detecting asymptomatic/undiagnosed cases is critical 
for the COVID-19 pandemic control. To this end, new 
methods based on mathematical models have been 
recently proposed to accurately calculate the health-

related consequences of the COVID-19.21 One of these 
models is the Susceptible–Exposed–Infectious–
Recovered–Dead (SEIRD) Model, which could be 
applied to better estimate the COVID-19 transmission 
rate and case fatality risk worldwide.22  

CFR is used as a measure of disease severity and 
ideally, should be estimated by direct follow-up of cases 
and ascertainment of their outcome.23 We have 
alternatively estimated the risk in a population within a 
specified period by dividing the number of deaths 
associated with the disease by the number of cases of that 
disease using different methods. In this current report, we 
have presented risk instead of “rate” because the 
numerator cases were not a subset of the denominator's 
population. All three methods of CFR estimation have 
their limitations. Common limitations of the methods are 
the undiagnosed cases and delays in reporting data. 
Another limitation of this research is removing countries 
with a relatively small number of COVID-19 confirmed 
cases in the main analyses, since CFR is a flawed metric 
of mortality risk when the sample size is small or very 
limited. 

CFR is commonly used to measure disease 
severity and is often used to predict the course or 
outcome of a disease. It can also be used to evaluate the 
effectiveness of new therapies by reducing measures and 
improving methods. In the COVID-19 outbreak, 
widespread changes in CFR estimates can be misleading, 
which may lead to underestimating the potential threat of 
COVID-19 in symptomatic patients. It is difficult to 
compare estimates across the countries, as different 
countries use different definitions and various testing 
strategies that may or may not include some cases. 
Changes in CFR may also be impacted by testing delays, 
dealing with delays, and differences in the quality of care 
or interventions at diverse stages of the disease. 

Moreover, gender, ethnicity, and underlying 
diseases may vary by country. Cross-sectional 
comparisons of CFR values may be biased because the 
disease duration may potentially vary from country to 



 
 
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country during the epidemic. To avoid this bias, time-
adjusted estimates between the onset of symptoms and 
death should be recommended to compare CFRs across 
countries.13 Therefore, the estimation of CFR in response 
to COVID-19 pandemic disease is a high priority, but its 
interpretation must be done using evidence-based 
strategies. Though each model has its disadvantages and 
pitfalls, we recommend estimating CFR using corrected 
model I by dividing the number of deaths on a given day 
by the number of patients with confirmed COVID-19 
infection 14 days before, based on the assumed 
maximum incubation period of up to 14 days.  

The WHO announced that the fatality rate of the 
COVID-19 is 10 times higher than that of influenza, 
making this research timely and relevant.14 Due to high 
mortality cases around the world, accurate calculations 
and clear estimates of CFR for COVID-19 can inform 
public health interventions and policies to improve health 
locally and globally. CFR and CRR are not the only 
measures of severity of the disease, and better estimators 
could be explored in future research.  

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Table S1. The comparison of case fatality rate (CFR) and case recovery rate (CRR) between different countries (n = 

210 countries and territories around the world and 2 international conveyances). Data retrieved on September 13, 

2020. 
Country Total Recovered Total deaths Total cases Active cases CFR1 CRR1 CFR2 CRR2 CFR3 CRR3 
USA 3,950,354 198,128 6,676,601 2,528,119 2.97% 59% 4.78% 95.22% 0.85% 57.09% 

India 3,702,595 78,614 4,754,356 973,147 1.65% 78% 2.08% 97.92% 0.49% 75.34% 

Brazil 3,553,421 131,274 4,315,858 631,163 3.04% 82% 3.56% 96.44% 0.63% 80.79% 

Russia 873,535 18,484 1,057,362 165,343 1.75% 83% 2.07% 97.93% 2.20% 78.31% 

Peru 559,321 30,593 722,832 132,918 4.23% 77% 5.19% 94.81% 0.85% 61.54% 

Colombia 592,820 22,734 708,964 93,410 3.21% 84% 3.69% 96.31% 2.98% ---- 

Mexico 467,525 70,604 663,973 125,844 10.63% 70% 13.12% 86.88% 0.44% 65.32% 

South Africa 576,423 15,427 648,214 56,364 2.38% 89% 2.61% 97.39% 0.90% 77.98% 

Spain N/A 29,747 576,697 N/A 5.16% ---- 100.00% ---- 0.14% ---- 

Argentina 409,771 11,263 546,481 125,447 2.06% 75% 2.68% 97.32% 0.85% 60.79% 

Chile 404,919 11,895 432,666 15,852 2.75% 94% 2.85% 97.15% 1.06% 86.39% 

Iran 344,516 23,029 399,940 32,395 5.76% 86% 6.27% 93.73% 0.68% 81.57% 

France 89,059 30,910 373,911 253,942 8.27% 24% 25.76% 74.24% 1.93% 20.91% 

UK N/A 41,623 365,174 N/A 11.40% ---- 100.00% ---- 1.24% ---- 

Bangladesh 238,271 4,702 336,044 93,071 1.40% 71% 1.94% 98.06% 0.28% 68.78% 

Saudi Arabia 301,836 4,240 325,050 18,974 1.30% 93% 1.39% 98.61% 0.51% 86.03% 

Pakistan 289,429 6,379 301,481 5,673 2.12% 96% 2.16% 97.84% 0.26% 93.21% 

Turkey 257,731 6,999 289,635 24,905 2.42% 89% 2.64% 97.36% 0.32% 88.52% 

Iraq 221,283 7,941 286,778 57,554 2.77% 77% 3.46% 96.54% 0.23% 76.62% 

Italy 213,191 35,603 286,297 37,503 12.44% 74% 14.31% 85.69% 0.38% 71.24% 

Germany 235,300 9,427 260,546 15,819 3.62% 90% 3.85% 96.15% 0.87% 89.92% 

Philippines 187,116 4,292 257,863 66,455 1.66% 73% 2.24% 97.76% 0.06% 71.58% 

Indonesia 152,458 8,650 214,746 53,638 4.03% 71% 5.37% 94.63% 0.09% 67.64% 

Israel 113,496 1,103 152,722 38,123 0.72% 74% 0.96% 99.04% 0.94% 68.37% 

Ukraine 68,346 3,148 151,859 80,365 2.07% 45% 4.40% 95.60% 0.36% 36.87% 

Canada 120,075 9,170 136,141 6,896 6.74% 88% 7.10% 92.90% 0.01% 87.39% 

Bolivia 82,796 7,297 125,982 35,889 5.79% 66% 8.10% 91.90% 0.23% 63.32% 

Qatar 118,475 205 121,523 2,843 0.17% 97% 0.17% 99.83% 0.16% 91.46% 

Ecuador 91,242 10,864 116,451 14,345 9.33% 78% 10.64% 89.36% 0.33% 76.72% 

Kazakhstan 100,615 1,634 106,803 4,554 1.53% 94% 1.60% 98.40% 0.53% 91.90% 

Dominican Republic 76,531 1,953 103,092 24,608 1.89% 74% 2.49% 97.51% 0.62% 71.19% 

Romania 42,811 4,127 102,386 55,448 4.03% 42% 8.79% 91.21% 0.07% 40.11% 

Panama 73,476 2,155 101,041 25,410 2.13% 73% 2.85% 97.15% 0.01% 71.66% 

Egypt 83,261 5,627 100,856 11,968 5.58% 83% 6.33% 93.67% 0.08% 80.48% 

Kuwait 84,404 558 94,211 9,249 0.59% 90% 0.66% 99.34% 0.26% 80.19% 

Belgium 18,709 9,923 92,478 63,846 10.73% 20% 34.66% 65.34% 0.83% 18.99% 

Oman 83,325 762 88,337 4,250 0.86% 94% 0.91% 99.09% 0.25% 93.35% 

Sweden N/A 5,846 86,505 N/A 6.76% ---- 100.00% ---- 0.49% ---- 

China 80,399 4,634 85,184 151 5.44% 94% 5.45% 94.55% 0.19% 92.96% 

Morocco 65,867 1,553 84,435 17,015 1.84% 78% 2.30% 97.70% 0.61% 76.91% 

Guatemala 70,403 2,949 81,658 8,306 3.61% 86% 4.02% 95.98% 0.25% 86.18% 



 
 
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Country Total Recovered Total deaths Total cases Active cases CFR1 CRR1 CFR2 CRR2 CFR3 CRR3 

Netherlands N/A 6,253 81,012 N/A 7.72% ---- 100.00% ---- 0.28% ---- 

UAE 68,983 399 78,849 9,467 0.51% 87% 0.58% 99.42% 0.11% 80.38% 

Japan 66,280 1,423 74,544 6,841 1.91% 89% 2.10% 97.90% 0.38% 87.58% 

Belarus 72,547 744 73,975 684 1.01% 98% 1.02% 98.98% 0.23% 97.45% 

Poland 59,725 2,182 73,650 11,743 2.96% 81% 3.52% 96.48% 0.21% 80.92% 

Honduras 17,760 2,065 67,136 47,311 3.08% 26% 10.42% 89.58% 0.15% 20.56% 

Ethiopia 24,493 996 63,888 38,399 1.56% 38% 3.91% 96.09% 0.40% 36.44% 

Portugal 43,894 1,860 63,310 17,556 2.94% 69% 4.07% 95.93% 0.14% 67.01% 

Venezuela 47,729 477 59,630 11,424 0.80% 80% 0.99% 99.01% 0.57% 77.97% 

Bahrain 53,192 211 59,586 6,183 0.35% 89% 0.40% 99.60% 0.32% 85.07% 

Singapore 56,699 27 57,357 631 0.05% 99% 0.05% 99.95% 0.28% 97.64% 

Nigeria 44,088 1,078 56,177 11,011 1.92% 78% 2.39% 97.61% 0.35% 76.45% 

Costa Rica 20,928 590 55,454 33,936 1.06% 38% 2.74% 97.26% 0.16% 32.11% 

Nepal 37,524 336 53,120 15,260 0.63% 71% 0.89% 99.11% 0.81% 67.71% 

Algeria 33,875 1,605 48,007 12,527 3.34% 71% 4.52% 95.48% 0.21% 68.64% 

Uzbekistan 43,511 386 46,850 2,953 0.82% 93% 0.88% 99.12% 0.05% 90.71% 

Switzerland 38,500 2,020 46,704 6,184 4.33% 82% 4.99% 95.01% 0.11% 76.85% 

Armenia 41,605 911 45,675 3,159 1.99% 91% 2.14% 97.86% 0.05% 89.51% 

Ghana 44,342 286 45,434 806 0.63% 98% 0.64% 99.36% 0.02% 94.92% 

Kyrgyzstan 40,779 1,063 44,828 2,986 2.37% 91% 2.54% 97.46% 0.62% 89.86% 

Moldova 30,437 1,117 42,714 11,160 2.62% 71% 3.54% 96.46% 0.32% 69.91% 

Afghanistan 31,234 1,420 38,641 5,987 3.67% 81% 4.35% 95.65% 0.03% 79.89% 

Azerbaijan 35,607 559 38,172 2,006 1.46% 93% 1.55% 98.45% 0.15% 90.23% 

Kenya 22,771 619 35,969 12,579 1.72% 63% 2.65% 97.35% 0.02% 60.83% 

Czechia 21,205 453 35,401 13,743 1.28% 60% 2.09% 97.91% 0.25% 56.27% 

Austria 26,579 754 32,696 5,363 2.31% 81% 2.76% 97.24% 0.09% 78.70% 

Serbia 31,100 731 32,300 469 2.26% 96% 2.30% 97.70% 0.03% 91.26% 

Ireland 23,364 1,783 30,730 5,583 5.80% 76% 7.09% 92.91% 0.19% 73.41% 

Palestine 19,979 210 29,906 9,717 0.70% 67% 1.04% 98.96% 0.19% 66.07% 

Paraguay 13,679 514 27,324 13,131 1.88% 50% 3.62% 96.38% 0.08% 45.81% 

El Salvador 17,874 782 26,851 8,195 2.91% 67% 4.19% 95.81% 0.19% 65.70% 

Australia 23,340 810 26,651 2,501 3.04% 88% 3.35% 96.65% 0.23% 85.10% 

Lebanon 7,936 239 23,669 15,494 1.01% 34% 2.92% 97.08% 0.05% 32.87% 

Bosnia and Herzegovina 15,922 690 23,138 6,526 2.98% 69% 4.15% 95.85% 0.08% 63.57% 

Libya 12,100 354 22,348 9,894 1.58% 54% 2.84% 97.16% 0.18% 52.02% 

S. Korea 18,226 358 22,176 3,592 1.61% 82% 1.93% 98.07% 0.37% 81.19% 

Cameroon 18,837 415 20,009 757 2.07% 94% 2.16% 97.84% 0.32% 91.38% 

Denmark 16,247 630 19,557 2,680 3.22% 83% 3.73% 96.27% 0.29% 80.39% 

Ivory Coast 17,960 119 18,916 837 0.63% 95% 0.66% 99.34% 0.10% 92.82% 

Bulgaria 12,758 717 17,891 4,416 4.01% 71% 5.32% 94.68% 0.32% 70.16% 

Madagascar 14,349 210 15,737 1,178 1.33% 91% 1.44% 98.56% 0.25% 89.56% 

North Macedonia 13,128 646 15,694 1,920 4.12% 84% 4.69% 95.31% 0.09% 80.47% 

Senegal 10,373 295 14,237 3,569 2.07% 73% 2.77% 97.23% 0.05% 71.13% 

Sudan 6,731 834 13,470 5,905 6.19% 50% 11.02% 88.98% 0.03% 44.13% 

Zambia 12,007 312 13,466 1,147 2.32% 89% 2.53% 97.47% 0.01% 85.62% 

Croatia 10,721 218 13,368 2,429 1.63% 80% 1.99% 98.01% 0.40% 79.47% 



 
 
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Country Total Recovered Total deaths Total cases Active cases CFR1 CRR1 CFR2 CRR2 CFR3 CRR3 

Greece 3,804 302 13,036 8,930 2.32% 29% 7.36% 92.64% 0.30% 27.04% 

Norway 10,371 265 12,079 1,443 2.19% 86% 2.49% 97.51% 0.12% 84.63% 

Hungary 4,058 633 11,825 7,134 5.35% 34% 13.49% 86.51% 0.11% 32.06% 

Albania 6,494 330 11,185 4,361 2.95% 58% 4.84% 95.16% 0.36% 54.62% 

DRC 9,719 262 10,385 404 2.52% 94% 2.62% 97.38% 0.27% 89.52% 

Guinea 9,251 63 10,020 706 0.63% 92% 0.68% 99.32% 0.09% 89.49% 

Malaysia 9,189 128 9,868 551 1.30% 93% 1.37% 98.63% 0.13% 86.58% 

Namibia 5,811 98 9,604 3,695 1.02% 61% 1.66% 98.34% 0.25% 59.00% 

French Guiana 9,132 63 9,521 326 0.66% 96% 0.69% 99.31% 0.30% 91.87% 

Maldives 7,055 31 9,052 1,966 0.34% 78% 0.44% 99.56% 0.07% 74.77% 

Tajikistan 7,782 72 9,014 1,160 0.80% 86% 0.92% 99.08% 0.09% 81.95% 

Gabon 7,706 53 8,643 884 0.61% 89% 0.68% 99.32% 0.71% 88.24% 

Finland 7,500 337 8,557 720 3.94% 88% 4.30% 95.70% 0.49% 82.17% 

Haiti 6,120 219 8,478 2,139 2.58% 72% 3.45% 96.55% 0.18% 67.63% 

Zimbabwe 5,675 224 7,508 1,609 2.98% 76% 3.80% 96.20% 0.09% 73.91% 

Mauritania 6,804 161 7,274 309 2.21% 94% 2.31% 97.69% 0.56% 92.66% 

Luxembourg 6,397 124 7,194 673 1.72% 89% 1.90% 98.10% 0.21% 88.24% 

Tunisia 1,991 107 6,635 4,537 1.61% 30% 5.10% 94.90% 0.53% 24.70% 

Montenegro 4,491 118 6,530 1,921 1.81% 69% 2.56% 97.44% 0.09% 66.39% 

Malawi 3,724 177 5,678 1,777 3.12% 66% 4.54% 95.46% 0.46% 65.41% 

Slovakia 3,114 38 5,453 2,301 0.70% 57% 1.21% 98.79% 0.51% 56.19% 

Djibouti 5,327 61 5,394 6 1.13% 99% 1.13% 98.87% 0.07% 93.46% 

Eswatini 4,188 98 5,050 764 1.94% 83% 2.29% 97.71% 0.14% 76.16% 

Mozambique 2,905 35 5,040 2,100 0.69% 58% 1.19% 98.81% 0.12% 51.88% 

Equatorial Guinea 4,490 83 4,996 423 1.66% 90% 1.82% 98.18% 0.00% 83.87% 

Hong Kong 4,613 100 4,939 226 2.02% 93% 2.12% 97.88% 0.47% 91.11% 

Congo 3,887 88 4,928 953 1.79% 79% 2.21% 97.79% 0.08% 75.95% 

Nicaragua 2,913 144 4,818 1,761 2.99% 60% 4.71% 95.29% 0.15% 59.86% 

CAR 1,825 62 4,749 2,862 1.31% 38% 3.29% 96.71% 0.29% 36.03% 

Cabo Verde 4,104 44 4,711 563 0.93% 87% 1.06% 98.94% 0.04% 85.35% 

Uganda 1,998 52 4,703 2,653 1.11% 42% 2.54% 97.46% 0.21% 36.06% 

Cuba 3,878 108 4,653 667 2.32% 83% 2.71% 97.29% 0.21% 80.29% 

Suriname 3,788 93 4,579 698 2.03% 83% 2.40% 97.60% 0.17% 80.78% 

Rwanda 2,544 22 4,565 1,999 0.48% 56% 0.86% 99.14% 0.15% 51.59% 

Jamaica 1,072 40 3,623 2,511 1.10% 30% 3.60% 96.40% 0.55% 22.74% 

Slovenia 2,699 135 3,603 769 3.75% 75% 4.76% 95.24% 0.28% 73.58% 

Syria 827 152 3,506 2,527 4.34% 24% 15.53% 84.47% 0.63% 22.99% 

Thailand 3,312 58 3,473 103 1.67% 95% 1.72% 98.28% 0.00% 88.89% 

Gambia 1,617 102 3,376 1,657 3.02% 48% 5.93% 94.07% 0.03% 47.63% 

Somalia 2,791 98 3,376 487 2.90% 83% 3.39% 96.61% 0.27% 79.86% 

Mayotte 2,964 40 3,374 370 1.19% 88% 1.33% 98.67% 0.00% 87.34% 

Angola 1,289 132 3,335 1,914 3.96% 39% 9.29% 90.71% 0.09% 37.36% 

Lithuania 2,070 86 3,296 1,140 2.61% 63% 3.99% 96.01% 0.24% 62.23% 

Sri Lanka 2,983 12 3,195 200 0.38% 93% 0.40% 99.60% 0.00% 90.45% 

Guadeloupe 837 24 3,080 2,219 0.78% 27% 2.79% 97.21% 0.00% 21.40% 

Jordan 2,156 22 3,062 884 0.72% 70% 1.01% 98.99% 0.46% 67.90% 



 
 
KHAFAIE & RAHIM 

 
This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 
This journal is published by the University Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 
 

Central Asian Journal of Global Health 
Volume 10, No. 1 (2021) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2021.489|http://cajgh.pitt.edu 

 
 

Country Total Recovered Total deaths Total cases Active cases CFR1 CRR1 CFR2 CRR2 CFR3 CRR3 

Aruba 1,542 18 2,994 1,434 0.60% 52% 1.15% 98.85% 0.40% 48.76% 

Trinidad and Tobago 766 51 2,993 2,176 1.70% 26% 6.24% 93.76% 0.17% 25.06% 

Bahamas 1,319 67 2,928 1,542 2.29% 45% 4.83% 95.17% 0.00% 40.57% 

Mali 2,276 128 2,916 512 4.39% 78% 5.32% 94.68% 0.03% 73.80% 

Myanmar 676 16 2,796 2,104 0.57% 24% 2.31% 97.69% 0.57% 23.28% 

Réunion 1,313 14 2,723 1,396 0.51% 48% 1.06% 98.94% 0.00% 45.46% 

Estonia 2,252 64 2,655 339 2.41% 85% 2.76% 97.24% 0.11% 82.94% 

South Sudan 1,290 49 2,578 1,239 1.90% 50% 3.66% 96.34% 0.00% 45.42% 

Guinea-Bissau 1,127 39 2,275 1,109 1.71% 50% 3.34% 96.66% 0.35% 46.95% 

Malta 1,850 15 2,274 409 0.66% 81% 0.80% 99.20% 0.04% 77.53% 

Botswana 546 10 2,252 1,696 0.44% 24% 1.80% 98.20% 0.27% 22.29% 

Benin 1,793 40 2,242 409 1.78% 80% 2.18% 97.82% 0.04% 79.93% 

Iceland 2,085 10 2,162 67 0.46% 96% 0.48% 99.52% 0.09% 93.06% 

Sierra Leone 1,634 72 2,096 390 3.44% 78% 4.22% 95.78% 0.29% 75.00% 

Georgia 1,363 19 2,075 693 0.92% 66% 1.37% 98.63% 0.19% 63.66% 

Yemen 1,211 582 2,009 216 28.97% 60% 32.46% 67.54% 0.20% 59.78% 

Guyana 1,191 54 1,812 567 2.98% 66% 4.34% 95.66% 0.17% 63.41% 

New Zealand 1,676 24 1,797 97 1.34% 93% 1.41% 98.59% 0.06% 90.21% 

Uruguay 1,502 45 1,780 233 2.53% 84% 2.91% 97.09% 0.62% 83.15% 

Togo 1,189 37 1,555 329 2.38% 76% 3.02% 96.98% 0.39% 73.95% 

Cyprus 1,281 22 1,523 220 1.44% 84% 1.69% 98.31% 0.00% 81.02% 

Burkina Faso 1,127 56 1,514 331 3.70% 74% 4.73% 95.27% 0.40% 73.91% 

Latvia 1,248 35 1,464 181 2.39% 85% 2.73% 97.27% 0.00% 84.43% 

Belize 458 19 1,458 981 1.30% 31% 3.98% 96.02% 0.89% 29.15% 

Andorra 943 53 1,344 348 3.94% 70% 5.32% 94.68% 0.60% 69.05% 

Liberia 1,210 82 1,316 24 6.23% 92% 6.35% 93.65% 0.08% 90.58% 

Lesotho 528 33 1,245 684 2.65% 42% 5.88% 94.12% 0.08% 41.77% 

Niger 1,100 69 1,178 9 5.86% 93% 5.90% 94.10% 0.08% 92.53% 

Chad 938 80 1,083 65 7.39% 87% 7.86% 92.14% 0.09% 83.56% 

Vietnam 910 35 1,060 115 3.30% 86% 3.70% 96.30% 0.19% 84.15% 

French Polynesia 642 2 953 309 0.21% 67% 0.31% 99.69% 0.00% 62.85% 

Martinique 98 18 939 823 1.92% 10% 15.52% 84.48% 0.00% 8.73% 

Sao Tome and Principe 866 15 906 25 1.66% 96% 1.70% 98.30% 0.00% 93.93% 

San Marino 662 42 722 18 5.82% 92% 5.97% 94.03% 0.00% 87.26% 

Diamond Princess 651 13 712 48 1.83% 91% 1.96% 98.04% 0.42% 88.76% 

Turks and Caicos 270 5 641 366 0.78% 42% 1.82% 98.18% 0.00% 40.09% 

Channel Islands 575 48 633 10 7.58% 91% 7.70% 92.30% 0.47% 87.05% 

Sint Maarten 430 19 533 84 3.56% 81% 4.23% 95.77% 0.00% 78.80% 

Tanzania 183 21 509 305 4.13% 36% 10.29% 89.71% 0.59% 35.17% 

Papua New Guinea 232 5 508 271 0.98% 46% 2.11% 97.89% 0.79% 44.69% 

Taiwan 475 7 498 16 1.41% 95% 1.45% 98.55% 0.40% 94.18% 

Burundi 374 1 471 96 0.21% 79% 0.27% 99.73% 0.64% 77.07% 

Comoros 415 7 456 34 1.54% 91% 1.66% 98.34% 0.00% 90.57% 

Faeroe Islands 410  418 8 0.00% 98% 0.00% 100.00% 0.24% 98.09% 

Mauritius 335 10 361 16 2.77% 93% 2.90% 97.10% 0.00% 90.03% 

Eritrea 304  361 57 0.00% 84% 0.00% 100.00% 0.00% 82.27% 



 
 
CENTRAL ASIAN JOURNAL OF GLOBAL HEALTH 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 
 

This journal is published by the University Library System of the University of Pittsburgh as part  
of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 
Central Asian Journal of Global Health 

Volume 10, No. 1 (2021) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2021.489 | http://cajgh.pitt.edu 

 
 

Country Total Recovered Total deaths Total cases Active cases CFR1 CRR1 CFR2 CRR2 CFR3 CRR3 

Isle of Man 312 24 337 1 7.12% 93% 7.14% 92.86% 0.59% 90.80% 

Gibraltar 294  327 33 0.00% 90% 0.00% 100.00% 0.00% 86.24% 

Mongolia 298  311 13 0.00% 96% 0.00% 100.00% 0.00% 93.57% 

Cambodia 274  275 1 0.00% 100% 0.00% 100.00% 0.00% 93.45% 

Saint Martin 107 6 256 143 2.34% 42% 5.31% 94.69% 0.39% 37.50% 

Bhutan 159  244 85 0.00% 65% 0.00% 100.00% 0.00% 59.84% 

Cayman Islands 204 1 208 3 0.48% 98% 0.49% 99.51% 0.00% 94.23% 

Barbados 158 7 180 15 3.89% 88% 4.24% 95.76% 0.00% 86.67% 

Bermuda 161 9 177 7 5.08% 91% 5.29% 94.71% 0.00% 82.49% 

Monaco 123 1 169 45 0.59% 73% 0.81% 99.19% 0.00% 68.05% 

Brunei 139 3 145 3 2.07% 96% 2.11% 97.89% 2.07% 91.03% 

Curaçao 56 1 145 88 0.69% 39% 1.75% 98.25% 0.00% 31.03% 

Seychelles 136  139 3 0.00% 98% 0.00% 100.00% 0.72% 94.24% 

Liechtenstein 105 1 111 5 0.90% 95% 0.94% 99.06% 0.90% 90.99% 

Antigua and Barbuda 91 3 95 1 3.16% 96% 3.19% 96.81% 1.05% 93.68% 

British Virgin Islands 37 1 66 28 1.52% 56% 2.63% 97.37% 0.00% 39.39% 

St. Vincent Grenadines 61  64 3 0.00% 95% 0.00% 100.00% 0.00% 85.94% 

Macao 46  46 0 0.00% 100% 0.00% 100.00% 2.17% 82.61% 

Fiji 24 2 32 6 6.25% 75% 7.69% 92.31% 3.13% 53.13% 

Saint Lucia 26  27 1 0.00% 96% 0.00% 100.00% 7.41% 96.30% 

Timor-Leste 25  27 2 0.00% 93% 0.00% 100.00% 0.00% 85.19% 

New Caledonia 26  26 0 0.00% 100% 0.00% 100.00% 0.00% 100.00% 

Caribbean Netherlands 7  25 18 0.00% 28% 0.00% 100.00% 4.00% 4.00% 

Dominica 18  24 6 0.00% 75% 0.00% 100.00% 0.00% 58.33% 

Grenada 24  24 0 0.00% 100% 0.00% 100.00% 4.17% 87.50% 

Laos 21  23 2 0.00% 91% 0.00% 100.00% 0.00% 65.22% 

St. Barth 13  21 8 0.00% 62% 0.00% 100.00% 0.00% 61.90% 

Saint Kitts and Nevis 17  17 0 0.00% 100% 0.00% 100.00% 0.00% 70.59% 

Greenland 14  14 0 0.00% 100% 0.00% 100.00% 0.00% 100.00% 

Montserrat 11 1 13 1 7.69% 85% 8.33% 91.67% 0.00% 84.62% 

Falkland Islands 13  13 0 0.00% 100% 0.00% 100.00% 0.00% 76.92% 

Vatican City 12  12 0 0.00% 100% 0.00% 100.00% 0.00% 100.00% 

Saint Pierre Miquelon 5  11 6 0.00% 45% 0.00% 100.00% 0.00% 36.36% 

Western Sahara 8 1 10 1 10.00% 80% 11.11% 88.89% 0.00% 80.00% 

MS Zaandam  2 9 7 22.22% 0% 100.00% 0.00% 0.00% 0.00% 

Anguilla 3  3 0 0.00% 100% 0.00% 100.00% 0.00% 100.00% 

Total 20,811,464 924,577 28,943,657 7,207,616 3.19% 72% 4.25% 95.75% 0.73% 68.72% 

           

 



 
 
KHAFAIE & RAHIM 

 
This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 
This journal is published by the University Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 
 

Central Asian Journal of Global Health 
Volume 10, No. 1 (2021) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2021.489 | http://cajgh.pitt.edu 

 
 

Table S2. The estimated CFRs and CRRs for each included country (n=38) against the county’s population, GDP, number of hospital beds per 1,000 people, 

number of ICU beds per 100,000 people, and number of ventilators between the three different proposed models of estimation. 

Country 
Total 

Recovered 

Total 

deaths 

Total 

cases 

Active 

cases 

Population 

(Million) 

GDP 

(Trillion) 

Number of 

hospital beds 

per 1,000 

people 

Number of 

ICU Beds 

per 100,000 

people 

Number of 

Ventilators 

Model 1 Model 2 Model 3 

CFR1 CRR1 CFR2 CRR2 CFR3 CRR3 

USA 36,948 23,644 587,155 526,563 327.2 19.39 2.77 34.7 177,000 4.03 6.29 39.02 60.98 6.96 12.18 

Spain 64,727 17,756 170,099 87,616 46.66 1.311 2.97 9.7 NR 10.44 38.05 21.53 78.47 12.37 44.83 

Italy 35,435 20,465 159,516 103,616 60.48 1.935 3.18 12.5 3,000 12.83 22.21 36.61 63.39 14.85 28.73 

France 27,718 14,967 136,779 94,094 66.99 2.583 5.98 11.6 30,000 10.94 20.26 35.06 64.94 14.57 27.14 

Germany 64,300 3,194 130,072 62,578 82.79 3.677 8.00 29.2 25,000 2.46 49.43 4.73 95.27 3.55 66.79 

UK 135 11,329 88,621 76,948 66.44 2.622 2.54 6.6 8,175 12.78 0.15 98.82 1.18 18.35 ----- 

China 77,738 3,341 82,249 1,170 1,386 12.24 4.34 3.6 NR 4.06 94.52 4.12 95.88 5.64 93.80 

Iran 45,983 4,585 73,303 22,735 81.16 0.4395 1.5 4.8 NR 6.25 62.73 9.07 90.93 2.64 14.58 

Turkey 3,957 1,296 61,049 55,796 80.81 0.8511 2.81 47.1 17,000 2.12 6.48 24.67 75.33 8.83 98.30 

Belgium 6,707 3,903 30,589 19,979 11.4 0.4927 5.76 15.9 NR 12.76 21.93 36.79 63.21 18.39 28.16 

Netherlands 250 2,823 26,551 23,478 17.18 0.8262 3.32 6.4 NR 10.63 0.94 91.86 8.14 9.27 54.82 

Switzerland 13,700 1,138 25,688 10,850 8.57 0.6789 4.53 11.0 NR 4.43 53.33 7.67 92.33 1.23 12.03 

Canada 7,756 780 25,680 17,144 37.59 1.653 2.52 13.5 NR 3.04 30.20 9.14 90.86 6.03 45.96 

Brazil 173 1,355 23,723 22,195 209.3 2.056 2.3 NR NR 5.71 0.73 88.68 11.32 16.13 1.12 

Russia 1,470 148 18,328 16,710 144.5 1.578 8.05 8.3 40,000 0.81 8.02 9.15 90.85 8.25 103.11 

Portugal 277 535 16,934 16,122 10.29 0.2176 3.39 4.2 1,400 3.16 1.64 65.89 34.11 4.36 3.87 

Austria 7,343 368 14,041 6,330 24.6 1.323 3.84 9.1 1,314 2.62 52.30 4.77 95.23 3.74 17.66 

Israel 1,855 116 11,586 9,615 8.712 0.3509 3.02 NR NR 1.00 16.01 5.89 94.11 5.12 0.69 

Sweden 381 919 10,948 9,648 10.12 0.538 2.22 5.8 NR 8.39 3.48 70.69 29.31 4.20 96.93 



 
 
CENTRAL ASIAN JOURNAL OF GLOBAL HEALTH 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 
 

This journal is published by the University Library System of the University of Pittsburgh as part  
of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 
Central Asian Journal of Global Health 

Volume 10, No. 1 (2021) | ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2021.489 | http://cajgh.pitt.edu 

 
 

Ireland 25 365 10,647 10,257 4.83 0.3337 2.96 6.5 NR 3.43 0.23 93.59 6.41 3.32 63.76 

S. Korea 7,534 222 10,564 2,808 51.4 1.531 12.27 10.6 9,795 2.10 71.32 2.86 97.14 15.65 5.70 

India 1,181 358 10,453 8,914 1,339 2.597 0.53 5.2 40,000 3.42 11.30 23.26 76.74 1.78 37.54 

Peru 2,642 216 9,784 6,926 32.17 0.2114 1.6 NR NR 2.21 27.00 7.56 92.44 3.11 96.101 

Japan 799 143 7,645 6,703 126.8 4.872 13.05 7.3 32,586 1.87 10.45 15.18 84.82 2.97 14.32 

Ecuador 597 355 7,529 6,577 16.62 0.1031 1.50 NR NR 4.72 7.93 37.29 62.71 1.71 54.75 

Chile 2,367 82 7,525 5,076 18.05 0.2771 2.2 2.11 NR 1.09 31.46 3.35 96.65 6.32 13.97 

Poland 487 245 6,934 6,202 37.98 0.5245 6.62 6.9 10,100 3.53 7.02 33.47 66.53 5.20 14.70 

Romania 914 331 6,633 5,388 19.53 0.2118 6.3 21.4 NR 4.99 13.78 26.59 73.41 6.45 26.51 

Norway 32 134 6,605 6,439 5.368 0.3988 3.6 8 800 2.03 0.48 80.72 19.28 1.46 21.02 

Australia 3,494 61 6,394 2,839 24.6 1.323 3.84 9.1 1,314 0.95 54.64 1.72 98.28 2.27 29.08 

Denmark 2,235 285 6,318 3,798 5.603 0.3249 2.61 6.7 NR 4.51 35.38 11.31 88.69 10.53 42.55 

Czech Republic 519 143 6,059 5,397 10.65 0.2157 6.63 11.6 3,529 2.36 8.57 21.60 78.40 5.78 64.21 

Pakistan 1,097 96 5,707 4,514 197 0.305 0.6 NR 34,000 1.68 19.22 8.05 91.95 3.14 0.61 

Mexico 1,964 332 5,014 2,718 129.2 1.15 1.38 1.2 2,050 6.62 39.17 14.46 85.54 3.87 26.20 

Saudi Arabia 805 65 4,934 4,064 32.94 0.6838 2.7 NR NR 1.32 16.32 7.47 92.53 1.50 89.40 

Philippines 242 315 4,932 4,375 104.9 0.3136 1.0 NR NR 6.39 4.91 56.55 43.45 0.80 25.56 

Malaysia 2,276 77 4,817 2,464 31.62 0.3145 1.9 NR NR 1.60 47.25 3.27 96.73 11.99 14.14 

Indonesia 380 399 4,557 3,778 264 1.016 1.2 NR NR 8.76 8.34 51.22 48.78 9.36 12.17 

World 445,023 119,699 1,925,179 1,360,457 ----- ----- ----- ----- ----- 6.22 23.12 21.20 78.80 8.67 32.23 

 


