Microsoft Word - Khafaie&Rahim.docx New articles in this journal are 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. 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% 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 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 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 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 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 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. References 1. Zhou P, Yang XL, Wang XG, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020;579(7798):270- 273. DOI: 10.1038/s41586-020-2012-7 2. Sun P, Lu X, Xu C, Sun W, Pan B. Understanding of COVID-19 based on current evidence. J Med Virol. 2020. DOI: 10.1002/jmv.25722 3. Dong E, Du H, Gardner L. An interactive web- based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20(5):533-544. DOI: 10.1016/S1473-3099(20)30120-1 4. Abdollahi E, Champredon D, Langley JM, Galvani AP, Moghadas SM. Temporal estimates of case- fatality rate for COVID-19 outbreaks in Canada and the United States. Cmaj. 2020. DOI: 10.1503/cmaj.200711 5. Kampf G, Todt D, Pfaender S, Steinmann E. Persistence of coronaviruses on inanimate surfaces and their inactivation with biocidal agents. J Hosp Infect. 2020;104(3):246-251. DOI: 10.1016/j.jhin.2020.01.022 6. Lauer SA, Grantz KH, Bi Q, et al. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine. 2020;172(9):577-582. DOI: 10.7326/M20-0504 7. Bulut C, Kato Y. Epidemiology of COVID-19. Turk J Med Sci. 2020;50(SI-1):563-570. DOI: 10.3906/sag-2004-172 8. Antunes JL. A dictionary in the dynamics of epidemiology. Rev Bras Epidemiol. 2016;19(1):219-223. DOI:10.1590/1980/5497201600010020 9. Rajgor DD, Lee MH, Archuleta S, Bagdasarian N, Quek SC. The many estimates of the COVID-19 case fatality rate. The Lancet Infectious Diseases. 2020;20(7):776-777. DOI: 10.1016/S1473- 3099(20)30244-9 10. Lipsitch M, Donnelly CA, Fraser C, et al. Potential Biases in Estimating Absolute and Relative Case- Fatality Risks during Outbreaks. PLoS Negl Trop Dis. 2015;9(7):e0003846. DOI: 10.1371/journal.pntd.0003846 11. Atkins KE, Wenzel NS, Ndeffo-Mbah M, Altice FL, Townsend JP, Galvani AP. Under-reporting and case fatality estimates for emerging epidemics. BMJ. 2015;350:h1115. DOI: 10.1136/bmj.h1115 12. National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Care Services; Committee on Health Care Utilization and Adults with Disabilities. Health-Care Utilization as a Proxy in Disability Determination. Washington (DC): National Academies Press (US); March 1, 2018. 13. Khafaie MA, Rahim F. Cross-Country Comparison of Case Fatality Rates of COVID- 19/SARS-COV-2. Osong Public Health Res Perspect. 2020;11(2):74-80. DOI: 10.24171/j.phrp.2020.11.2.03 14. World Health Organization (WHO). Coronavirus disease 2019 (COVID-19) Situation report –43. 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 World Health Organization. March 3, 2020. Accessed December 2020. https://www.who.int/docs/default- source/coronaviruse/situation-reports/20200303- sitrep-43-covid-19.pdf. 15. Centers of Disease Control and Prevention (CDC). United States COVID-19 Cases and Deaths by State. Centers for Disease Control and Prevention. 2020. Accessed March 10, 2020, https://www.cdc.gov/coronavirus/2019- ncov/cases-in-us.html. 16. (US) RMAfHRaQ. Registries for Evaluating Patient Outcomes: A User's Guide [Internet]. Data Sources for Registries. 2014; https://www.ncbi.nlm.nih.gov/books/NBK208611/ 17. 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. DOI: 10.4414/smw.2020.20203 18. Ghani AC, Donnelly CA, Cox DR, et al. Methods for Estimating the Case Fatality Ratio for a Novel, Emerging Infectious Disease. Am J Epidemiol. 2005;162(5):479-486. DOI: 10.1093/aje/kwi230 19. Baud D, Qi X, Nielsen-Saines K, Musso D, Pomar L, Favre G. Real estimates of mortality following COVID-19 infection. The Lancet Infectious Diseases. 2020;20(7):773. DOI: 10.1016/S1473- 3099(20)30195-X 20. COVID TC, Stephanie B, Virginia B, et al. Geographic Differences in COVID-19 Cases, Deaths, and Incidence-United States, February 12- April 7, 2020. MMWR Morb Mort Wkly Rep. 2020;69(15);465-471. DOI: 10.15585/mmwr.mm6915e4 21. Li R, Pei S, Chen B, et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV- 2). Science. 2020;368(6490):489-493. DOI: 10.1126/science.abb3221 22. Maugeri A, Barchitta M, Battiato S, Agodi A. Estimation of Unreported Novel Coronavirus (SARS-CoV-2) Infections from Reported Deaths: A Susceptible-Exposed-Infectious-Recovered- Dead Model. J Clin Med. 2020;9(5):1350. DOI: 10.3390/jcm9051350 23. Kelly H, Cowling BJ. Case Fatality: Rate, Ratio, or Risk? Epidemiology. 2013;24(4):622-623. DOI: 10.1097/EDE.0b013e318296c2b6 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 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% 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 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% 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 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