| Literature DB >> 35071820 |
Sina Salajegheh Tazerji1,2, Fatemeh Shahabinejad3, Mahya Tokasi4, Mohammad Ali Rad5, Muhammad Sajjad Khan6, Muhammad Safdar6,7, Krzysztof J Filipiak8, Lukasz Szarpak9, Tomasz Dzieciatkowski10, Jan Jurgiel11, Phelipe Magalhães Duarte12, Md Tanvir Rahman13, Md Abdus Sobur13, Md Saiful Islam13, Adnan Ahmed14, Mohamed N F Shaheen15, Awad A Shehata16,17, Rasha Gharieb18, Mohamed Fawzy19, Yashpal Singh Malik20, Nagaraj Jaganathasamy21, Vinodhkumar Obli Rajendran22, Kannan Subbaram23, P Shaik Syed Ali23, Sheeza Ali23, Saif Ur Rehman24, Mehmet Ozaslan7, Gulfaraz Khan25, Muhammad Saeed6, Umair Younas6, Safdar Imran26, Yasmeen Junejo6, Parmida Arabkarami2, Unarose Hogan27, Alfonso J Rodriguez-Morales28,29,30.
Abstract
This review was focused on global data analysis and risk factors associated with morbidity and mortality of coronavirus disease 2019 from different countries, including Bangladesh, Brazil, China, Central Eastern Europe, Egypt, India, Iran, Pakistan, and South Asia, Africa, Turkey and UAE. Male showed higher confirmed and death cases compared to females in most of the countries. In addition, the case fatality ratio (CFR) for males was higher than for females. This gender variation in COVID-19 cases may be due to males' cultural activities, but similar variations in the number of COVID-19 affected males and females globally. Variations in the immune system can illustrate this divergent risk comparatively higher in males than females. The female immune system may have an edge to detect pathogens slightly earlier. In addition, women show comparatively higher innate and adaptive immune responses than men, which might be explained by the high density of immune-related genes in the X chromosome. Furthermore, SARS-CoV-2 viruses use angiotensin-converting enzyme 2 (ACE2) to enter the host cell, and men contain higher ACE2 than females. Therefore, males may be more vulnerable to COVID-19 than females. In addition, smoking habit also makes men susceptible to COVID-19. Considering the age-wise distribution, children and older adults were less infected than other age groups and the death rate. On the contrary, more death in the older group may be associated with less immune system function. In addition, most of these group have comorbidities like diabetes, high pressure, low lungs and kidney function, and other chronic diseases. Due to the substantial economic losses and the numerous infected people and deaths, research examining the features of the COVID-19 epidemic is essential to gain insight into mitigating its impact in the future and preparedness for any future epidemics.Entities:
Keywords: COVID-19; COVID-19, Coronavirus disease 2019; Epidemiology; Mortality; One-health; Pandemic; SARS-CoV-2; SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus-2
Year: 2022 PMID: 35071820 PMCID: PMC8761036 DOI: 10.1016/j.genrep.2022.101505
Source DB: PubMed Journal: Gene Rep ISSN: 2452-0144
Province-wise distribution of COVID-19 cases, death, and case fatality rate till October 8, 2021, in China (Mainland).
| Division | Confirmed Cases | Deaths | Recovered | Case fatality ratio (%) |
|---|---|---|---|---|
| Hubei | 68,148 | 4512 | 63,627 | 6.62 |
| Guangdong | 1963 | 08 | 1922 | 0.4 |
| Zhejiang | 1291 | 01 | 1279 | 0.07 |
| Henan | 1288 | 22 | 1259 | 1.7 |
| Shanghai | 1277 | 07 | 1174 | 0.5 |
| Hunan | 1020 | 04 | 1015 | 0.4 |
| Anhui | 992 | 06 | 985 | 0.6 |
| Xinjiang | 980 | 03 | 952 | 0.3 |
| Heilongjiang | 949 | 13 | 936 | 1.4 |
| Beijing | 947 | 09 | 932 | 1 |
| Jiangxi | 935 | 01 | 934 | 0.1 |
| Shandong | 848 | 07 | 841 | 0.8 |
| Sichuan | 783 | 03 | 735 | 04 |
| Jiangsu | 676 | 0 | 666 | 0 |
| Chongqing | 589 | 06 | 582 | 1 |
| Shaanxi | 484 | 03 | 438 | 0.6 |
| Fujian | 461 | 01 | 423 | 0.2 |
| Hebei | 373 | 06 | 362 | 1.6 |
| Inner Mongolia | 307 | 01 | 282 | 0.3 |
| Tianjin | 289 | 03 | 266 | 1 |
| Liaoning | 288 | 02 | 280 | 0.6 |
| Shanxi | 218 | 0 | 213 | 0 |
| Yunnan | 217 | 02 | 209 | 0.9 |
| Gansu | 181 | 02 | 177 | 1 |
| Hainan | 171 | 06 | 165 | 3.5 |
| Jilin | 157 | 02 | 155 | 1.2 |
| Guizhou | 147 | 02 | 145 | 1.3 |
| Ningxia | 75 | 0 | 75 | 0 |
| Qinghai | 18 | 0 | 18 | 0 |
| Overall | 96,410 | 4636 | 90,677 | 4.8 |
Source: https://cn.bing.com/search?q=Coronavirus+statistics+in+China+(mainland).
Division-wise distribution of COVID-19 cases, death, and case fatality rate (World Health Organization, 2021. COVID-19 Bangladesh situation reports.
| Division | Cases | Deaths | Case fatality ratio (%) |
|---|---|---|---|
| Dhaka | 916,973 | 12,017 | 1.31 |
| Chottogram | 239,862 | 5589 | 2.33 |
| Rajshahi | 98,044 | 2028 | 2.07 |
| Khulna | 112,080 | 3564 | 3.18 |
| Sylhet | 54,140 | 1253 | 2.31 |
| Rangpur | 55,005 | 1354 | 2.46 |
| Barishal | 45,038 | 934 | 2.07 |
| Mymensingh | 36,822 | 834 | 2.26 |
| Overall | 1,557,964 | 27,573 | 1.77 |
Source: (WHO Bangladesh COVID-19 Morbidity and Mortality Weekly Update (MMWU), 2021).
Fig. 1Cases of COVID-19 associated with different medical conditions. Data are included from March 1, 2020, to August 31, 2021 (CDC Laboratory Confirmed COVID-19 Associated Hospitalizations, 2021).
Fig. 2Details of Indian state-wise percentage of morality due to COVID-19 as of 06.10.2021 (Source: https://www.mygov.in/covid-19).
Province-wise distribution of COVID-19 cases, death, and case fatality rate till October 6 2021.
| Division | Cases | Deaths | Case fatality ratio (%) |
|---|---|---|---|
| Islamabad | 105,839 | 930 | 0.87 |
| Punjab | 434,139 | 12,724 | 2.93 |
| Sindh | 461,258 | 7451 | 1.61 |
| KPK | 175,012 | 5608 | 3.20 |
| Balochistan | 33,004 | 349 | 1 |
| GB | 10,338 | 186 | 1.80 |
| AJK | 34,278 | 738 | 2.15 |
| Overall | 1253,868 | 27,986 | 2.23 |
(COVID-19 Bangladesh Situation Reports, 2021).
Distribution of COVID-19 cases, death, and case fatality rate in Central-Eastern Europe till October 6, 2021.
| Country | Cases | Deaths | Case fatality ratio (%) |
|---|---|---|---|
| Bosnia and Herzegovina | 238,458 | 10,802 | 4.53 |
| Bulgaria | 511,666 | 21,320 | 4.17 |
| Belarus | 549,817 | 4228 | 0.77 |
| Croatia | 411,917 | 8722 | 2.12 |
| Czechia | 1,696,016 | 30,485 | 1.80 |
| Estonia | 160,832 | 1383 | 0.86 |
| Greece | 668,811 | 15,012 | 2.24 |
| Hungary | 825,799 | 30,253 | 3.66 |
| Latvia | 164,801 | 2773 | 1.68 |
| Lithuania | 342,827 | 5134 | 1.50 |
| Macedonia | 193,458 | 6758 | 3.49 |
| Montenegro | 133,767 | 1965 | 1.47 |
| Poland | 2,914,962 | 75,774 | 2.60 |
| Kosovo | 160,195 | 2954 | 1.84 |
| Moldavia | 301,431 | 6927 | 2.30 |
| Romania | 1,303,900 | 38,260 | 2.93 |
| Serbia | 981,329 | 8531 | 0.87 |
| Slovakia | 419,473 | 12,697 | 3.03 |
| Slovenia | 298,270 | 4597 | 1.54 |
| Ukraine | 2,482,518 | 57,526 | 2.32 |
Division-wise distribution of COVID-19 cases, death and case fatality rate (Brasil Coronavírus Brasil COVID-19, 2021).
| Division | Cases | Deaths | Case fatality ratio (%) |
|---|---|---|---|
| Southeast | 8.407.378 | 284.281 | 3.38 |
| Northeast | 4.803.969 | 117.049 | 2.44 |
| South | 4.159.429 | 93.615 | 2.25 |
| Midwest | 2.281.330 | 57.329 | 2.51 |
| North | 1.846.968 | 46.555 | 2.52 |
| Brazil | 21.499.074 | 598.829 | 2.79 |