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. 2021 Aug 4;14(10):1284–1298. doi: 10.1016/j.jiph.2021.07.021

An Overview on the Epidemiology and Immunology of COVID-19

Maryam Meskini a,b, Mina Rezghi Rami c, Parang Maroofi d, Soumya Ghosh e, Seyed Davar Siadat a,b,⁎, Mojgan Sheikhpour a,b,⁎
PMCID: PMC8336978  PMID: 34420903

Abstract

Coronaviruses are a large family of viruses that cause illnesses ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), and the 2019 novel coronavirus infection (COVID-19). Currently, there is no analyzed data to examine the outbreak of COVID-19 by continent and no determination of prevalence trends; this article reviews COVID-19 epidemiology and immunology. Original research, reviews, governmental databases, and treatment guidelines are analyzed to present the epidemiology and immunology of COVID-19. Reports from patients who were COVID-19 infected showed typical symptoms of neutrophilia, lymphopenia, and increased systemic inflammatory proteins of IL-6 and C reactive protein (CRP). These observations agree with the results of severe conditions of MERS or lethal cases of SARS, in which there is an increased presence of neutrophils and macrophages in the airways. Additionally, analyzed data showed that Europe (49.37%), the Americas (27.4%), and Eastern Mediterranean (10.07%) had the most cumulative total per 100,000 population confirmed cases, and Africa (6.9%), Western Pacific (3.46%), and South-East Asia (2.72%) had the lowest cumulative total per 100,000 population confirmed cases. In general, the trend lines showed that the number of confirmed cases (cumulative total) and deaths (cumulative total) would decrease eventually.

Abbreviation list: MERS, Middle east respiratory syndrome; SARS, Severe acute respiratory syndrome; MERS-CoV, Middle east respiratory syndrome coronavirus; SARS-CoV, Severe acute respiratory syndrome coronavirus; COVID-19, Coronavirus disease 2019; CRP, C reactive protein; WHO, World health organization; PHEIC, Public health emergency of international concern; ACE2, Angiotensin-converting enzyme 2; PAMPs, Pathogen-associated molecular patterns; PRRs, Pattern recognition receptors; TLR-3, Toll-like receptors 3; RIG-I, Retinoic acid-inducible gene I; MDA5, Melanoma differentiation-associated protein 5; CXCL10, C-X-C motif chemokine 10; MCP-1, Monocyte chemoattractant protein 1; IP-10, Interferon gamma-induced protein 10; MIP, Macrophage inflammatory protein; ADE, Antibody-dependent enhancement; FcγR, Fcγ receptors; ARDS, Acute respiratory distress syndrome; HLH, Hemophagocytic lymphohistiocytosis

Keywords: Coronaviruses, COVID-19, Epidemiology, Immunology

Introduction

In December 2019, unidentified pneumonia emerged in Wuhan, China, where many of the original patients had visited the seafood market of Wuhan. The isolation of the related virus from patients and subsequent molecular analyses indicated a 2019 novel coronavirus infection, which was named coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO) [1,2,70]. The explosive growth of COVID-19 infection in January 2020 necessitated that the WHO declare this outbreak a public health emergency of international concern (PHEIC) [3,4].

Unfortunately, international travel spread the virus worldwide, and 192,284,207 confirmed cases, including 4,136,518 deaths, were reported by the WHO on 23 July 2021. After the shocking health threat from Severe Acute Respiratory Syndrome coronavirus (SARS-CoV), a significant negative impact was felt on affected countries' economies. Searches on SARS-CoV showed a ‘bat’ origin and the transmission to humans via Himalayan palm civets (Paguma larvata) and raccoon dogs (Nyctereutes procyonoides) [[5], [6], [7], [8],71]. Afterward, the well-known Middle East Respiratory Syndrome coronavirus (MERS-CoV) emerged with rare transmission to humans with a higher fatality rate. Alpha and beta coronaviruses dispersed in China are mainly and naturally carried in bats. The study of the genetic diversity and molecular evolution of these coronaviruses has gained intense interest [[9], [10], [11]].

Due to the many human casualties caused by the COVID-19 in a short time around the world, many scientists sought to find the infection's mechanism and to collect the following demographic data. There is, however, no analyzed data to study the course of the disease and its prevalence trend. Therefore, this study reviewed COVID-19 epidemiology and immunology using original research, reviews, governmental databases, and treatment guidelines.

Epidemiology of COVID-19

The COVID-19 epidemic started with the first announcement on Feb. 20, 2020, of the fatalities (2239 cases) in China, including 75 cases on the mainland, 68 in Hong Kong, 10 in Macao, 26 in Taiwan, and the confirmed reports (1200 cases) elsewhere [12]. Three stages can roughly be observed from the epidemiology of COVID-19 (Fig. 1 ).

Fig. 1.

Fig. 1

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Three stages of COVID-19 epidemiology.

Total information

In the first stage, the epidemiologic analysis showed close contact was the key factor in-person-to-person transmission [13,14]. In the second stage, the reported cases outside Wuhan, in Beijing City and Guangdong indicated the spread of the virus, with the total number of infected cases rising to 205. Then 29 provinces of China and six countries conveyed 846 confirmed reports with an increase of 20 times faster than the first stage. Even though Wuhan's lock-down was implemented, more than 5 million people had already left Wuhan due to the Chinese New Year. In the third stage, 50–80% of all confirmed cases were clustered around Beijing, Shanghai, Jiangsu, and Shandong on Feb. 10, 2020 [15]. When the numbers increased 240 times and reached 9826 confirmed cases, the WHO declared PHEIC. About 44,730 infected cases and 16,067 suspected cases were recorded in 1386 counties and regions in China on Feb. 11, 2020 [16]. In this stage, the fatality rate was high in China (1114 reported deaths) and low outside China (one fatality in the Philippines). With the growth of new clinical definitions for diagnosis, the confirmed cases bounded to 14,840 in China. In contrast, 60,329 reported cases were recognized in 25 countries, with a 1471 times increase since the last report [15].

Regrettably, as of Feb. 11, 2020, 1716, medical-related staff from 422 medical institutions were infected. Among them, 64% were infected in Wuhan city and 23.3% in the rest of Hubei [17]. Preliminary evaluation of the dynamics of COVID-19 transmission indicated the basic reproductive number of about 1.4–3.9 for COVID-19 [18]. The R0 of SARS-CoV and MERS-CoV was 2.3–3.7 and 0.50–0.92 respectively in the absence of interventions [19]. The weekly operational reports of the WHO until July 23, 2021is given in Table 1 . The July 23, 2021 report of the WHO showed 192,284,207 confirmed cases of COVID-19, including 4,136,518 deaths.

Table 1.

A short list of weekly WHO reports about COVID-19 ending Feb. 8, 2021.

Date of report Confirmed cases Deaths Key Features
Dec. 7, 2020 66,243,918 1,528,984 1. WHO calls for global solidarity to maintain HIV services.
2. WHO and the Iraqi Governorate of Ninewa has established an isolation unit at Hamam Aleel Field Hospital to treat suspected and confirmed cases of COVID-19.
3. As of Dec. 4, 2020, The Solidarity Response Fund has raised or committed more than US$ 238 million.
4. WHO announced the recent launch of the Strategic Preparedness and Response Plan (SPRP) Monitoring and Evaluation Dashboard.
Dec. 14, 2020 70,461,926 1,599,704 1. Landmark alliance launches in Africa to fight COVID-19 misinformation.
2. Nepal enhances laboratory capacity for COVID-19 and influenza.
3.'WHO's Contingency Fund for Emergencies (CFE) provided $8.9 million for COVID-19 preparedness and response worldwide.
Dec. 21, 2020 75,704,857 1,690,061 1. PAHO prepares for COVID-19 vaccine deployment.
2. Joint Intra-Action Review carried out in the Republic of Moldova in collaboration with the Ministry of Health Labour and Social Protection.
3. WHO and IFRC sign a memorandum of understanding based on the EMT Initiative.
Jan. 11, 2021 88,828,387 1,926,625 1. As of Dec. 18, 2020, The Solidarity Response Fund has raised or committed more than US$ 240 million.
2. Islamic Republic of Iran tackles COVID-19 by enhancing primary health care.
3. WHO Country Office in Montenegro supports COVID-19 response and continuity of essential health services.
Jan. 19, 2021 93,956,883 2,029,084 1. WHO supports the installation of public address systems at 50 remote health centers in Lao People’s Democratic Republic.
2. WHO SEAR countries gear up for massive vaccination campaign – this time for COVID-19 virus.
3. US$ 50 million Iran COVID-19 Emergency Response Project (ICERP) scales up nationwide response to the epidemic.
Jan. 26, 2021 98,925,221 2 127,294 1. WHO works with Romania’s Ministry of Health and health professionals in the country to make telemedicine.
2. WHO Afghanistan continues to strengthen COVID-19 testing capacity across the country.
3. Vaccination Deployment Readiness map was launched on the Partners Platform.
Feb. 1, 2021 102,399,513 2, 217,005 1. The Pan American Health Organization (PAHO) launched a mobile application, MedPPE.
2. WHO-led UN Crisis-Management Team coordinating 23 UN entities across nine areas of work.
3. Mauritius conducts a COVID-19 vaccine simulation exercise before the national vaccine roll-out.
Feb. 8, 2021 105,394,301 2,302,302 1. WHO-led UN Crisis-Management Team coordinating 23 UN entities across nine areas of work.
2. WHO launches EARS, an AI-powered public-access social listening tool.
3. Countries submit vaccination plans for consideration of the next round of allocation.
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Geographic distribution, confirmed cases, and related deaths

Globally, over 192 million confirmed cases of COVID-19 were reported by the WHO until July 23, 2021. The updated data from confirmed cases and related deaths worldwide can be found on the WHO website. Since the first reports of cases from Wuhan at the end of 2019, cases have been reported in all continents except Antarctica. The number of confirmed cases and related deaths are reported in Table 2 .

Table 2.

Geographic distribution, confirmed cases and related deaths as of July 23, 2021.

WHO Region Country Cases - cumulative total CCT CNR (7D) CNRPP CNR (24 h) Deaths - cumulative total DCT DNR (7D) DNRPP DNR (24 h)
Africa Algeria 158,213 360.8 8307 18.94 1208 4008 9.14 113 0.26 14
Angola 41,405 125.98 875 2.66 178 977 2.97 26 0.08 4
Benin 8324 68.66 80 0.66 80 107 0.88 0 0 0
Botswana 97,657 4152.74 11,524 490.04 5755 1375 58.47 101 4.29 47
Burkina Faso 13,537 64.76 7 0.03 0 169 0.81 0 0 0
Burundi 6128 51.54 326 2.74 110 8 0.07 0 0 0
Cabo Verde 33,452 6016.69 272 48.92 57 297 53.42 3 0.54 0
Cameroon 81,871 308.41 404 1.52 0 1332 5.02 2 0.01 0
Central African Republic 7147 147.98 5 0.1 0 98 2.03 0 0 0
Chad 4965 30.23 6 0.04 0 174 1.06 0 0 0
Comoros 4014 461.59 −58 −6.67 3 147 16.9 1 0.11 0
Congo 13,117 237.71 184 3.33 0 176 3.19 4 0.07 0
Côte d’Ivoire 49,386 187.22 387 1.47 98 324 1.23 5 0.02 2
Democratic Republic of the Congo 47,174 52.67 1963 2.19 308 1021 1.14 37 0.04 1
Equatorial Guinea 8848 630.66 20 1.43 0 123 8.77 0 0 0
Eritrea 6480 182.72 98 2.76 7 32 0.9 2 0.06 0
Eswatini 21,880 1885.94 1187 102.31 144 735 63.35 37 3.19 5
Ethiopia 278,105 241.91 662 0.58 146 4363 3.8 13 0.01 3
Gabon 25,309 1137.11 64 2.88 0 163 7.32 1 0.04 0
Gambia 7161 296.32 551 22.8 0 197 8.15 9 0.37 0
Ghana 100,250 322.63 2136 6.87 276 819 2.64 13 0.04 1
Guinea 24,823 189.02 438 3.34 13 195 1.48 10 0.08 0
Guinea-Bissau 4117 209.2 119 6.05 9 74 3.76 4 0.2 0
Kenya 195,111 362.85 4091 7.61 801 3826 7.12 80 0.15 15
Lesotho 12,679 591.85 526 24.55 49 357 16.66 19 0.89 8
Liberia 5404 106.85 98 1.94 0 148 2.93 0 0 0
Madagascar 42,631 153.95 114 0.41 3 941 3.4 5 0.02 0
Malawi 46,417 242.64 4919 25.71 952 1410 7.37 109 0.57 21
Mali 14,528 71.74 31 0.15 3 530 2.62 1 0 0
Mauritania 23,223 499.46 1036 22.28 130 517 11.12 14 0.3 1
Mauritius 3388 266.4 1024 80.52 207 19 1.49 1 0.08 0
Mayotte 19,465 7134.87 14 5.13 5 174 63.78 0 0 0
Mozambique 105,866 338.71 11,133 35.62 2153 1221 3.91 164 0.52 31
Namibia 114,400 4502.33 5044 198.51 495 2665 104.88 395 15.55 45
Niger 5594 23.11 39 0.16 0 195 0.81 1 0 1
Nigeria 170,306 82.62 1232 0.6 184 2130 1.03 4 0 0
Réunion 34,615 3866.25 1320 147.43 0 266 29.71 10 1.12 0
Rwanda 61,375 473.86 9750 75.28 1309 704 5.44 88 0.68 11
Saint Helena 0 0 0 0 0 0 0 0 0 0
Sao Tome and Principe 2417 1102.85 17 7.76 0 37 16.88 0 0 0
Senegal 54,820 327.4 6550 39.12 523 1256 7.5 47 0.28 10
Seychelles 17,541 17835.83 307 312.16 0 79 80.33 11 11.18 0
Sierra Leone 6206 77.8 84 1.05 5 117 1.47 4 0.05 1
South Africa 234,2330 3949.39 89,090 150.21 14,858 68,625 115.71 2653 4.47 433
South Sudan 10,917 97.53 0 0 0 117 1.05 0 0 0
Togo 14,801 178.78 375 4.53 0 140 1.69 6 0.07 0
Uganda 91,355 199.72 2275 4.97 193 2483 5.43 234 0.51 58
United Republic of Tanzania 609 1.02 100 0.17 100 21 0.04 0 0 0
Zambia 189,731 1032.05 7602 41.35 1158 3196 17.38 205 1.12 34
Zimbabwe 93,421 628.55 14,549 97.89 2301 2870 19.31 452 3.04 61
Americas Anguilla 113 753.23 2 13.33 0 0 0 0 0 0
Antigua and Barbuda 1277 1304.01 10 10.21 2 42 42.89 0 0 0
Argentina 479,8851 10617.92 96,194 212.84 14,632 102,818 227.49 2568 5.68 437
Aruba 11,271 10556.73 86 80.55 15 109 102.09 1 0.94 0
Bahamas 13,781 3504.44 446 113.42 0 274 69.68 13 3.31 0
Barbados 4302 1497 89 30.97 10 48 16.7 0 0 0
Belize 13,865 3486.93 252 63.38 49 332 83.5 1 0.25 0
Bermuda 2535 4070.79 8 12.85 0 33 52.99 0 0 0
Bolivia (Plurinational State of) 465,351 3986.55 7139 61.16 1174 17,546 150.31 201 1.72 41
Bonaire 1661 7941.67 25 119.53 2 17 81.28 0 0 0
Brazil 19,473,954 9161.65 264,225 124.31 54,517 545,604 256.68 8210 3.86 1424
British Virgin Islands 2210 7308.93 279 922.71 0 23 76.07 19 62.84 0
Canada 1,424,715 3774.86 2884 7.64 495 26,512 70.25 54 0.14 4
Cayman Islands 629 957.09 9 13.69 2 2 3.04 0 0 0
Chile 160,4713 8394.52 10,217 53.45 1859 34,792 182 585 3.06 181
Colombia 4,679,994 9197.58 114,622 225.27 11,244 117,482 230.89 3145 6.18 351
Costa Rica 395,667 7767.13 8945 175.59 1532 4925 96.68 83 1.63 10
Cuba 308,599 2724.55 45,513 401.82 7745 2137 18.87 411 3.63 65
Curaçao 12,962 7899.18 509 310.19 79 126 76.79 0 0 0
Dominica 209 290.31 10 13.89 3 0 0 0 0 0
Dominican Republic 338,902 3124.12 2758 25.42 611 3931 36.24 24 0.22 2
Ecuador 478,615 2712.77 5893 33.4 0 30,752 174.3 8880 50.33 0
El Salvador 84,144 1297.28 1781 27.46 1292 2529 38.99 59 0.91 10
Falkland Islands (Malvinas) 60 1722.65 0 0 0 0 0 0 0 0
French Guiana 29,419 9849.61 705 236.04 134 170 56.92 7 2.34 1
Grenada 165 146.64 2 1.78 0 1 0.89 0 0 0
Guadeloupe 17,982 4494.11 173 43.24 0 278 69.48 4 1 0
Guatemala 344,221 1921.35 16,466 91.91 3364 10,029 55.98 195 1.09 13
Guyana 21,733 2763.07 510 64.84 65 515 65.48 12 1.53 1
Haiti 19,762 173.31 135 1.18 0 523 4.59 11 0.1 0
Honduras 284,187 2869.24 7198 72.67 1501 7535 76.08 179 1.81 28
Jamaica 51,542 1740.6 629 21.24 138 1167 39.41 31 1.05 4
Martinique 14,964 3987.58 2157 574.79 0 102 27.18 4 1.07 0
Mexico 2,693,495 2089.07 76,668 59.46 15,198 237,207 183.98 1700 1.32 397
Montserrat 21 420.08 0 0 0 1 20 0 0 0
Nicaragua 7313 110.39 269 4.06 0 194 2.93 1 0.02 0
Panama 425,599 9863.78 6995 162.12 1144 6723 155.81 62 1.44 7
Paraguay 447,146 6269.1 6090 85.38 879 14,446 202.54 380 5.33 52
Peru 2,097,811 6362.43 11,928 36.18 1798 195,429 592.71 677 2.05 97
Puerto Rico 142,359 4976.1 1259 44.01 180 2566 89.69 7 0.24 1
Saba 7 362.13 0 0 0 0 0 0 0 0
Saint Barthélemy 1057 10692.97 5 50.58 0 1 10.12 0 0 0
Saint Kitts and Nevis 557 1047.15 13 24.44 0 3 5.64 0 0 0
Saint Lucia 5496 2993.02 57 31.04 12 87 47.38 0 0 0
Saint Martin 2523 6526.29 51 131.92 0 30 77.6 0 0 0
Saint Pierre and Miquelon 28 483.18 2 34.51 1 0 0 0 0 0
Saint Vincent and the Grenadines 2266 2042.55 8 7.21 3 12 10.82 0 0 0
Sint Eustatius 20 637.15 0 0 0 0 0 0 0 0
Sint Maarten 2695 6284.69 39 90.95 8 34 79.29 0 0 0
Suriname 24,490 4174.68 774 131.94 55 625 106.54 28 4.77 5
Trinidad and Tobago 36,626 2617.1 1390 99.32 272 1003 71.67 40 2.86 3
Turks and Caicos Islands 2459 6351.05 15 38.74 1 18 46.49 0 0 0
United States of America 33,875,385 10234.17 231,856 70.05 0 604,546 182.64 1376 0.42 0
United States Virgin Islands 4286 4104.38 167 159.92 37 33 31.6 1 0.96 0
Uruguay 379,613 10928.11 1909 54.96 237 5905 169.99 51 1.47 9
Venezuela (Bolivarian Republic of) 295,746 1040.04 7647 26.89 1019 3426 12.05 99 0.35 18
Eastern Mediterranean Afghanistan 143,439 368.47 4388 11.27 256 6357 16.33 285 0.73 32
Bahrain 268,092 15755.52 473 27.8 0 1381 81.16 2 0.12 0
Djibouti 11,628 1176.92 6 0.61 0 155 15.69 0 0 0
Egypt 283,862 277.39 372 0.36 0 16,465 16.09 40 0.04 0
Iran (Islamic Republic of) 3,623,840 4314.46 159,785 190.24 20,313 88,063 104.85 1471 1.75 226
Iraq 1,526,943 3796.24 60,414 150.2 8106 18,101 45 394 0.98 81
Jordan 762,706 7475.21 3020 29.6 0 9922 97.24 50 0.49 0
Kuwait 388,881 9106.07 6797 159.16 0 2255 52.8 81 1.9 0
Lebanon 552,328 8092.19 2901 42.5 0 7888 115.57 6 0.09 0
Libya 227,433 3309.9 12,865 187.23 732 3322 48.35 73 1.06 13
Morocco 566,356 1534.4 16,512 44.74 0 9498 25.73 80 0.22 0
occupied Palestinian territory, including east Jerusalem 344,717 6757.28 372 7.29 0 3859 75.65 6 0.12 0
Oman 289,042 5660.14 0 0 0 3498 68.5 0 0 0
Pakistan 998,609 452.08 17,402 7.88 2158 22,928 10.38 239 0.11 40
Qatar 224,834 7803.88 923 32.04 196 600 20.83 1 0.03 0
Saudi Arabia 514,446 1477.7 8321 23.9 1162 8130 23.35 95 0.27 15
Somalia 15,162 95.4 77 0.48 0 781 4.91 0 0 0
Sudan 37,138 84.69 0 0 0 2776 6.33 0 0 0
Syrian Arab Republic 25,849 147.7 35 0.2 0 1905 10.89 3 0.02 0
Tunisia 555,997 4704.42 29,510 249.69 0 17,913 151.57 904 7.65 0
United Arab Emirates 667,080 6744.72 10,726 108.45 1547 1910 19.31 25 0.25 3
Yemen 6997 23.46 30 0.1 0 1371 4.6 5 0.02 0
Europe Albania 132,797 4614.53 168 5.84 34 2456 85.34 0 0 0
Andorra 14,464 18719.99 225 291.21 85 127 164.37 0 0 0
Armenia 228,382 7707.19 1271 42.89 221 4579 154.53 21 0.71 4
Austria 650,776 7311.22 2529 28.41 421 10,523 118.22 1 0.01 0
Azerbaijan 339,274 3346.17 1473 14.53 212 4999 49.3 9 0.09 1
Belarus 437,664 4631.7 6552 69.34 1069 3365 35.61 68 0.72 10
Belgium 1,112,161 9652.13 7896 68.53 1 25,217 218.85 8 0.07 2
Bosnia and Herzegovina 205,384 6260.15 117 3.57 39 9673 294.84 8 0.24 2
Bulgaria 423,440 6091.36 643 9.25 121 18,189 261.66 26 0.37 2
Croatia 362,305 8927.8 956 23.56 176 8245 203.17 11 0.27 0
Cyprus 95,307 10732.71 6850 771.39 1046 398 44.82 15 1.69 4
Czechia 1,672,140 15636.33 1557 14.56 207 30,347 283.78 12 0.11 0
Denmark 309,420 5313.97 5951 102.2 805 2542 43.66 2 0.03 0
Estonia 132,262 9952.17 519 39.05 83 1271 95.64 0 0 0
Faroe Islands 958 1960.5 44 90.04 0 1 2.05 0 0 0
Finland 102,042 1846.82 2450 44.34 412 978 17.7 0 0 0
France 5,813,457 8938.37 95,742 147.21 21,769 110,566 170 104 0.16 10
Georgia 398,081 9979.03 13,694 343.28 2460 5656 141.78 140 3.51 20
Germany 3,752,592 4512.13 10,811 13 2089 91,492 110.01 155 0.19 34
Gibraltar 4704 13962.19 218 647.06 33 94 279.01 0 0 0
Greece 469,042 4375.98 18,530 172.88 2601 12,875 120.12 56 0.52 5
Greenland 85 149.72 19 33.47 1 0 0 0 0 0
Guernsey 908 1408.45 47 72.9 4 17 26.37 0 0 0
Holy See 26 3213.84 0 0 0 0 0 0 0 0
Hungary 809,101 8281.89 376 3.85 85 30,020 307.28 5 0.05 0
Iceland 6967 1913.31 249 68.38 0 30 8.24 0 0 0
Ireland 289,139 5824.2 8355 168.3 1188 5026 101.24 8 0.16 0
Isle of Man 2821 3317.57 1072 1260.7 182 29 34.1 0 0 0
Israel 857,554 9907.57 7040 81.34 365 6457 74.6 12 0.14 0
Italy 4,302,393 7213.76 24,074 40.36 5056 127,920 214.48 80 0.13 15
Jersey 7077 6565.18 1837 1704.14 215 69 64.01 0 0 0
Kazakhstan 568,915 3029.9 27,022 143.91 0 8538 45.47 365 1.94 0
Kosovo [1] 107,911 6009.53 71 3.95 28 2255 125.58 1 0.06 0
Kyrgyzstan 155,005 2375.85 8713 133.55 1127 2227 34.13 73 1.12 10
Latvia 138,344 7251.97 303 15.88 44 2549 133.62 8 0.42 0
Liechtenstein 3174 8191.6 13 33.55 0 58 149.69 0 0 0
Lithuania 280,541 10040.51 969 34.68 245 4409 157.8 5 0.18 1
Luxembourg 73,309 11708.68 677 108.13 94 821 131.13 2 0.32 0
Malta 33,198 6451.68 1364 265.08 166 420 81.62 0 0 0
Monaco 2744 6992.15 89 226.79 16 33 84.09 0 0 0
Montenegro 100,854 16057.97 243 38.69 0 1624 258.57 3 0.48 0
Netherlands 1,827,273 10496.99 61,457 353.05 6301 17,789 102.19 16 0.09 3
North Macedonia 155,981 7486.92 115 5.52 16 5489 263.47 2 0.1 1
Norway 135,234 2519.46 1358 25.3 265 799 14.89 3 0.06 0
Poland 2,881,948 7592.44 707 1.86 108 75,235 198.21 30 0.08 4
Portugal 943,244 9161.35 23,044 223.82 3622 17,248 167.52 61 0.59 16
Republic of Moldova 258,365 6404.74 599 14.85 128 6236 154.59 17 0.42 4
Romania 1,082,057 5598.15 518 2.68 104 34,266 177.28 12 0.06 1
Russian Federation 6,078,522 4165.24 170,523 116.85 23,811 152,296 104.36 5428 3.72 795
San Marino 5107 15048.03 13 38.31 0 90 265.19 0 0 0
Serbia 719,462 10386.79 1369 19.76 228 7095 102.43 17 0.25 3
Slovakia 392,259 7187.03 225 4.12 40 12,534 229.65 10 0.18 0
Slovenia 258,467 12332.26 421 20.09 69 4761 227.16 0 0 0
Spain 4,249,258 8977.44 155,222 327.94 17,218 81,194 171.54 76 0.16 3
Sweden 109,6341 10615.65 2434 23.57 582 14,651 141.86 0 0 0
Switzerland 708,703 8188.73 3636 42.01 5 10,329 119.35 1 0.01 0
Tajikistan 14,761 154.77 316 3.31 0 117 1.23 6 0.06 0
The United Kingdom 5,602,325 8252.55 321,223 473.18 39,315 128,980 189.99 387 0.57 84
Turkey 5,563,903 6597.06 56,448 66.93 9586 50,761 60.19 346 0.41 52
Turkmenistan 0 0 0 0 0 0 0 0 0 0
Ukraine 2,247,419 5138.87 3814 8.72 763 52,811 120.76 109 0.25 21
Uzbekistan 122,786 366.86 4406 13.16 738 819 2.45 30 0.09 5
South-East Asia Bangladesh 1,146,564 696.2 62,642 38.04 6364 18,851 11.45 1386 0.84 166
Bhutan 2470 320.11 72 9.33 12 2 0.26 0 0 0
Democratic People's Republic of Korea 0 0 0 0 0 0 0 0 0 0
India 31,293,062 2267.61 266,233 19.29 35,342 419,470 30.4 6939 0.5 483
Indonesia 3,082,410 1126.93 301,607 110.27 49,071 80,598 29.47 9201 3.36 1566
Maldives 76,454 14143.9 718 132.83 0 218 40.33 2 0.37 0
Myanmar 258,870 475.78 40,131 73.76 5506 6459 11.87 1923 3.53 326
Nepal 676,708 2322.52 12,132 41.64 1982 9679 33.22 173 0.59 18
Sri Lanka 291,298 1360.36 9238 43.14 0 3902 18.22 257 1.2 0
Thailand 467,707 670.07 85,800 122.92 14,575 3811 5.46 712 1.02 114
Timor-Leste 10,281 779.78 232 17.6 54 26 1.97 0 0 0
Western Pacific American Samoa 0 0 0 0 0 0 0 0 0 0
Australia 32,427 127.17 911 3.57 159 915 3.59 3 0.01 0
Brunei Darussalam 311 71.09 29 6.63 2 3 0.69 0 0 0
Cambodia 70,419 421.19 5808 34.74 811 1188 7.11 163 0.97 20
China 120,000 8.16 461 0.03 82 5630 0.38 23 0 4
Cook Islands 0 0 0 0 0 0 0 0 0 0
Fiji 21,361 2382.86 7475 833.85 918 161 17.96 87 9.71 15
French Polynesia 19,234 6847.08 176 62.65 39 145 51.62 1 0.36 1
Guam 8231 4876.91 26 15.41 14 143 84.73 1 0.59 0
Japan 857,799 678.23 26,606 21.04 5282 15,106 11.94 92 0.07 9
Kiribati 0 0 0 0 0 0 0 0 0 0
Lao People's Democratic Republic 4119 56.61 1027 14.12 256 5 0.07 1 0.01 0
Malaysia 964,918 2981.27 84,136 259.95 13,034 7574 23.4 961 2.97 134
Marshall Islands 4 6.76 0 0 0 0 0 0 0 0
Micronesia (Federated States of) 0 0 0 0 0 0 0 0 0 0
Mongolia 152,539 4653.01 9411 287.07 0 755 23.03 48 1.46 0
Nauru 0 0 0 0 0 0 0 0 0 0
New Caledonia 131 45.88 2 0.7 0 0 0 0 0 0
New Zealand 2499 51.82 53 1.1 20 26 0.54 0 0 0
Niue 0 0 0 0 0 0 0 0 0 0
Northern Mariana Islands (Commonwealth of the) 188 326.63 1 1.74 0 2 3.47 0 0 0
Palau 0 0 0 0 0 0 0 0 0 0
Papua New Guinea 17,524 195.86 99 1.11 0 192 2.15 6 0.07 0
Philippines 1,530,266 1396.47 39,614 36.15 5828 26,891 24.54 577 0.53 17
Pitcairn Islands 0 0 0 0 0 0 0 0 0 0
Republic of Korea 185,733 362.27 10,687 20.84 1630 2066 4.03 15 0.03 3
Samoa 1 0.5 0 0 0 0 0 0 0 0
Singapore 63,791 1090.38 939 16.05 170 36 0.62 0 0 0
Solomon Islands 20 2.91 0 0 0 0 0 0 0 0
Tokelau 0 0 0 0 0 0 0 0 0 0
Tonga 0 0 0 0 0 0 0 0 0 0
Tuvalu 0 0 0 0 0 0 0 0 0 0
Vanuatu 3 0.98 0 0 0 0 0 0 0 0
Viet Nam 78,269 80.41 35,981 36.96 7125 370 0.38 163 0.17 0
Wallis and Futuna 454 4036.99 0 0 0 7 62.24 0 0 0
Other Other 764 0 0 13 0 0
Total (Global) 192,284,207 2466.908864 3,533,643 45.33485 483,475 4,136,518 53.06943 68,246 0.875562 8366
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CCT: Cases - cumulative total per 100,000 population, CNR (7D): Cases - newly reported in last seven days, CNRPP: Cases - newly reported in last seven days per 100,000 population, CNR (24 h): Cases - newly reported in last 24 h, DCT: Deaths - cumulative total per 100,000 population, DNR (7D): Deaths - newly reported in last seven days, DNRPP: Deaths - newly reported in last seven days per 100,000 population, DNR (24 h): Deaths - newly reported in last 24 h.

Table 3 shows that Europe (49.37%), the Americas (27.4%), and Eastern Mediterranean (10.07%) had the most cumulative total per 100,000 population confirmed cases until July 23, 2021, Africa (6.9%), Western Pacific (3.46%), and South-East Asia (2.72%) had the lowest cumulative total per 100,000 population confirmed cases. Until July 23, 2021, Europe (45.35%), the Americas (27.24%), and Africa (9.89%) had the most newly reported cases in the last seven days per 100,000 population confirmed cases. In the same period, Western Pacific (8.18%), Eastern Mediterranean (6.3%), and South-East Asia (3.01%) had the lowest newly reported cases in the last seven days per 100,000 population confirmed cases. Furthermore, Europe (52.91%), the Americas (31.32%), and Eastern Mediterranean (6.93%) had the most cumulative total per 100,000 population death cases until July 23, 2021, Africa (5.36%), Western Pacific (2.2%), and South-East Asia (1.24%) had the lowest cumulative total per 100,000 population death cases. Until July 23, 2021, the Americas (60.46%), Africa (17.18%), and Europe (7.14%) had the most newly reported death cases in the last seven days per 100,000 population. In the same period, Western Pacific (5.82%), Eastern Mediterranean (5.44%), and South-East Asia (3.92%) had the lowest newly reported death cases in the last seven days per 100,000 population.

Table 3.

Global distribution of confirmed cases and related deaths until July 23, 2021.

WHO Region Cases - cumulative total CCT CNR CNRPP Cases - newly reported in last 24 h Deaths - cumulative total DCT DNR DNRPP Deaths - newly reported in last 24 h
Africa 4,722,513 61510.9 190,877 2000.1 33,821 110,958 785.56 4884 50.01 807
Americas 75,349,353 242,607 937,013 5504.45 121,309 1,982,643 4583.23 29,119 175.92 3161
Eastern Mediterranean 12,035,379 89158.24 334,929 1273.74 34,470 229,078 1015.13 3760 15.85 410
Europe 58,740,133 437049.1 1,068,577 9164.82 145,599 1,209,595 7740.79 7749 20.79 1112
South-East Asia 37,305,824 24163.26 778,805 608.82 112,906 543,016 182.65 20,593 11.41 2673
Western Pacific 4,130,241 30701.44 223,442 1653.71 35,370 61,215 322.49 2141 16.95 203
Total 192,283,443 885,190 3,533,643 20205.64 483,475 4,136,505 14629.85 68,246 290.93 8366
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CCT: Cases - cumulative total per 100,000 population, CNR: Cases - newly reported in last seven days, CNRPP: Cases - newly reported in last seven days per 100,000 population, DCT: Deaths - cumulative total per 100,000 population, DNR: Deaths - newly reported in last seven days, DNRPP: Deaths - newly reported in last seven days per 100,000 population.

Notably, the confirmed cases-cumulative and confirmed cases-cumulative total per 100000 population in Africa, Eastern Mediterranean, Western Pacific, and Europe exhibited a raised trend in comparison to confirmed cases in the Americas and South-East Asia where the trend showed a fall. Concurrently, a similar trend for the death cases were also observed for all these continents (Fig. 2 ).

Fig. 2.

Fig. 2

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The number of cumulative total confirmed cases and death cases, also, the number of cumulative total confirmed cases and death cases per 100,000 population, until July 23, 2021.

The trend lines in Fig. 3 shows that until July 23, 2021, the number of newly cumulative total cases and cumulative total cases per 100000 population in the last seven days increased and decreased, respectively. In this period, Europe had the highest and South-East Asia had the lowest number of newly cumulative total cases and cumulative total cases per 100000 population in the last seven days.

Fig. 3.

Fig. 3

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The number of newly cumulative total cases and cumulative total cases per 100,000 population in the last seven days until July 23, 2021.

The trend line in Fig. 4 shows that until July 23, 2021, the number of newly reported cases in the last 24 h increased, and the number of newly reported deaths in the last 24 h decreased. Europe had the highest number of newly reported cases in the last 24 h while the Americas had the highest number of newly reported deaths in the last 24 h. Africa had the lowest number of newly reported cases, while the Western Pacific had the lowest number of newly reported deaths in the last 24 h. The trend line in Fig. 5 shows that until July 23, 2021, the number of newly cumulative total deaths and cumulative total deaths per 100,000 population decreased. Americas had the highest number of newly cumulative total deaths, Europe had the highest cumulative total deaths per 100,000 population. The Western Pacific had the lowest number of newly cumulative total deaths and cumulative total deaths per 100,000 population. The trend line shows that until July 23, 2021, the number of newly cumulative total deaths and cumulative total deaths per 100,000 population decreased in the last seven days. Americas had the higher number of newly cumulative total deaths and the highest cumulative total death per 100,000 population in the last seven days. Until July 23, 2021, Western Pacific had the lowest number of newly cumulative total deaths, but Eastern Mediterranean had the lowest cumulative total deaths per 100,000 population in the last seven days (Fig. 6 ). Furthermore, the trend line shows that until July 23, 2021, the number of newly cumulative total confirmed and deaths cases decreased. Although Americas had the higher number of newly cumulative total confirmed and death cases, Western Pacific had the lowest number of newly cumulative total confirmed and death cases until July 23, 2021 (Fig. 7 ).

Fig. 4.

Fig. 4

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Depicts the number of newly reported cases and deaths in the last 24 hours until July 23, 2021. The trend lines showed an increase and decrease of the newly reported cases and deaths, respectively in the last 24 hours.

Fig. 5.

Fig. 5

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The number of newly cumulative total deaths and cumulative total deaths per 100000 population until July 23, 2021. The trend lines showed a decrease of newly cumulative total deaths and cumulative total deaths per 100000 population.

Fig. 6.

Fig. 6

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The number of newly cumulative total deaths and cumulative total deaths per 100,000 population in the last seven days until July 23, 2021. The trend line shows that the number of newly cumulative total deaths and cumulative total deaths per 100,000 population decreased in the last seven days. The number of newly cumulative total deaths and cumulative total deaths per 100000 population in the last seven days until July 23, 2021. The trend lines showed a decrease of newly cumulative total deaths and cumulative total deaths per 100000 population in the last seven days.

Fig. 7.

Fig. 7

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The number of newly cumulative total confirmed and deaths cases until July 23, 2021. The trend lines showed a decrease of newly cumulative total confirmed and deaths cases.

Immunology of COVID-19

Most infected people (more than 80%) will develop mild to moderate illness without symptoms and recover without hospitalization, but less than 20% of infected patients have severe symptoms and are critically ill [20,21]. Presently, there is incomplete evidence on host factors affecting individual outcomes in COVID-19. Fever, dry cough, and tiredness are the most common symptoms; less common symptoms include aches and pains, sore throat, diarrhea, conjunctivitis, headache, loss of taste or smell, skin rash, and discoloration fingers or toes [22,23].

The first line of immunological defense against COVID-19, as with SARS-CoV-2 infection, is the innate immune system. The development of COVID-19 infection is thought to occur from a complex interplay between multiple pathophysiological mechanisms as with SARS-CoV-2, where the mechanisms regulate SARS-CoV-2 infection and contribute to specific tissue damage in organs [24]. There are various immunity pathways mediated during SARS-CoV-2 infection, which are related to innate immunity, adaptive immunity, and autoimmunity. Pathological findings in tissue samples of patients with COVID-19 provide valuable information about our understanding of pathophysiology and the development of evidence-based treatment regimens [25].

Infection mechanisms and immune evasion

To find the escape mechanism of COVID-19 from the host’s immune response, one may extrapolate knowledge of SARS-CoV counterparts and MERS-CoV. Remarkably, COVID-19 has almost 80% RNA sequence homology in common with SARS-CoV, and 50% with MERS-CoV [17], with COVID-19 demonstrating different genomic regions compared to SARS-CoV. The viral spike protein bonded to the host cell receptor is longer than other related coronaviruses, particularly SARS-CoV with about 30 amino acids [26]. Thus, similar immune evasion strategies may be used by all coronaviruses. Nevertheless, undiscovered mechanisms may also be employed by COVID-19 [27]. Both SARS-CoV and COVID-19 use the host cell receptor, angiotensin-converting enzyme 2 (ACE2), to start the infection [28]. The ACE2 is found on surfactant generating type 2 alveolar cells and on related cells in the airways, which serves as an entry for viruses into the body [[29], [30], [31]]. High ACE2 expression is also observed on the intestinal epithelium [32].

Expression of ACE2

The expression of ACE2 on cardiac and vascular endothelial cells may elucidate cardiovascular complications in patients [16]. It is not evident whether and how the SARS-CoV-2 can also infect immune cells containing monocytes/macrophages and T cells. On monocytes and macrophages, the expression of ACE2 is not ubiquitously observed, and for SARS-CoV-2 this may offer a mechanism of entry into immune cells. Immune complexes, including other receptors and/or phagocytosis of the virus, are also apparent [[33], [34], [35]]. The expression of type I interferon (T1IFN) and signals of downstream modification responses into an ‘anti-viral state’, consequently encourages infection control and pathogen clearance [36]. Initially, immune cells find a virus-related infection from pathogen-associated molecular patterns (PAMPs). Pattern recognition receptors (PRRs) are then activated and cause the activation of the immune cell. SARS-CoV, COVID-19, and MERS-CoV are among the RNAs viruses, which the endosomal RNA PRRs distinguish, including toll-like receptors 3 (TLR-3) and sensors of cytoplasmic RNA, namely retinoic acid-inducible gene I (RIG-I) and melanoma differentiation-associated protein 5 (MDA5) [[37], [38], [39]].

Pathogen clearance and recovery have emerged due to activation and priming of innate and adaptive immune responses. The suppression of these mechanisms by COVID-19 in some cases to escape recognition by the immune system is seen in more severe infections and worse prognosis [[40], [41], [42], [43]].

To some extent, the novel coronaviruses may also discharge these mechanisms inducing T cell apoptosis [44,45]. Lymphocytes may also become exhausted due to pro-inflammatory cytokine expression by native immune cells engaged in the lungs and trigger hyper-inflammation during a cytokine storm [46,47].

Hyper inflammation

In some cohort studies, the key results were associated with negative consequences in COVID-19, as in SARS or MERS, as hyper-inflammation with more severe disease was suggested. Among 99 patients infected by COVID-19, the report showed the typical symptoms with percentages of 38, 35, and 52 related to neutrophilia, lymphopenia, and increased systemic inflammatory proteins of IL-6 CRP, respectively [48]. A study involving 41 individuals with severe disease terminating in an intensive care unit (ICU) admission or death presented with interconnected neutrophilia and lymphopenia [20]. In another study, substantial leukopenia (11.8%), lymphopenia (77.6%), thrombopenia (41.2%), anemia (48.2%), hypofibrinogenemia (22.4%), and hypo-albuminemia (78.8%) was reported among 85 cases of death from COVID-19 [49]. These observations agree with the results of severe conditions of MERS or lethal cases of SARS in which the presence of neutrophils and macrophages were increased in the airways [49,50]. Other studies of severe clinical phenotypes and ICU dependency of patients have presented a link with higher levels of plasma from innate chemokines, definitely the pro-inflammatory cytokine TNF-α, chemokine (C-C motif) ligand 2 (CCL2), C-X-C motif chemokine 10 (CXCL10), monocyte chemoattractant protein 1 (MCP-1), interferon gamma-induced protein 10 (IP-10), and macrophage inflammatory protein (MIP-)1 A/CCL3 [51,52]. This is a condition previously described in SARS and MERS inflammation with poor consequences.

Enhanced activation of the innate immune system contributes to morbidity and mortality in COVID-19, contradictory to immune evasion mechanisms, including expression activation of T1IFN, IL-1β, IL-6, and TNF-α. One probable description is that the endothelial induction, vascular cell damage, and cell death have resulted from replicating the COVID-19 virus. Cell deaths are due to inflammation, including necrosis or pyroptosis in pro-inflammatory cytokine expression, recruitment, and activation of immune cells [53]. It is proposed that uninfected immune cells recruited to the infection site show inflammatory responses of unwell and robust control, leading to damage of tissues and systemic inflammation [54].

The other probable explanation relates to the production of neutralizing antibodies against coronaviruses in the early stages of damaged organs. The phenomenon of antibody-dependent enhancement (ADE) increases damage throughout viral infections. It should be noted that the promotion of virus particle uptake is connected to immune system complexes in binding to Fcγ receptors (FcγR). Viral replication in immune cells and immune complexes are both mediated inflammatory responses in the damaged tissues of acute respiratory distress syndrome (ARDS) (Fig. 8 ) [46,55]. The histopathologic reports of tissues from COVID-19 patients showed the advanced features associated with immune complex-mediated vasculitis, including monocyte infiltration, thickening of blood vessels, and hemorrhage [[56], [57], [58]].

Fig. 8.

Fig. 8

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A schematic illustration of inflammatory mechanisms in complex immune vasculitis.

Generally, patients with severe symptoms of COVID-19 experience cytokine storm, lymphopenia, and often lymphatic tissue atrophy, specifically lymph nodes [59,60]. This cytokine storm corresponds to the reports of hemophagocytic lymphohistiocytosis (HLH), inspiring cell death and hypo-cellularity of lymphatic organs [[61], [62], [63]].

Effective host factors

The available data associated with age is insufficient, but children seemingly do not progress to severe indicators or difficulties associated with COVID-19. This is surprising as children are prone to viral infections comprising seasonal coronaviruses (75%) before four years. Nonetheless, increasing age leads to antibody decrease, especially over sixty years [64]. It can diminish the effective response of immune systems to COVID-19 in the elderly, as the reactivity is restricted to anti-seasonal coronavirus and anti-SARS antibodies with increased inflammation and complications.

The other age-dependent mechanism may be allied with live vaccinations (e.g., BCG). Vaccines protect the target antigen, which leads to non-specific heterologous effects due to the induction of innate immune mechanisms—individuals who receive BCG vaccinations as infants in response to S. aureus or Candida spp. produce increased pro-inflammatory IL-1β and TNF-α levels and reduced infection-related mortality [38].

Conversely, immune responses in a non-homogenous manner may also contribute to inflammation complications. Normally in adults, T cells do not have a memory of antigens they have not been exposed to, but cross-reactive memory T cells lead to slender responses by preferring clones with high affinity. The feature of immune senescence is due to the limited memory T cell repertoires, associated with disease progression and damage of T cell-mediated infections of hepatitis and virulent mononucleosis [65,66]. Lately it has been recommended that in children and young women, a higher expression of ACE2 is expected, which decreases with age. In contrast, the lowest expression is seen in chronic diseases such as diabetes and hypertension, in reverse correlation with risk for severe disease and negative effects [66].

Immune modulating treatment

According to earlier SARS, MERS studies, and COVID-19 cohort studies, the determinants of old age, diabetes, metabolic syndrome, obesity, male, coronary heart disease, chronic obstructive pulmonary disease, and kidney disease are among the most reported risk factors [67]. It is noteworthy that in China and Italy, the suppression of the immune system was not acknowledged among these risk factors [68]. However, immune suppression and its associated functions may enhance virus spread.

Moreover, the infected cases receiving immune-modulating treatment may be prone to secondary infections due to the association of COVID-19 with lymphopenia. Some immune-modulating drugs can defend against viral infections. Unrestrained treatment termination of immune-modulating drugs may cause disease flares in autoimmune/inflammatory conditions or organ rejection. As evident, the risk for a viral infection is increased. Thus, international communities recommend treatment continuation in the absence of symptoms and modifications of current treatment regimens with clinical service monitoring [68,69].

Conclusion

The outbreak of COVID-19 has caused concern around the world, and it is not evident whether and how SARS-CoV-2 can also infect immune cells. Different studies reported neutrophilia, lymphopenia, leukopenia, thrombopenia, anemia, hypofibrinogenemia, hypo-albuminemia, and increased systemic inflammatory proteins of IL-6 CRP. In severe conditions of MERS or lethal cases of SARS, neutrophils and macrophages are increased in the airways. The analysis of available data can help authorities in deciding how to control the virus worldwide. Thus, this study collected and analyzed data from articles and databases. Various researchers in different parts of the world analyze the available data to predict the prevalence of coronavirus in different countries; still, no analysis has been published that can predict the situation and future peaks. Following the review and analyzing of the published data on the WHO website and the data generated from the reported cases and trend lines, this study predicts that the number of confirmed cases (cumulative total) and deaths (cumulative total) caused due to coronavirus in different continents would decrease eventually. Intriguingly, although, the trend lines indicating that the number of confirmed cases (newly reported in the last 24 hours and last seven days) would increase, the number of deaths cases (newly reported in the last 24 hours and last seven days) will decrease in the long run. In the future, additional analyses based on the updated data and information are essential to confirm the prediction of this study.

Funding

The current study does not received any funding.

Declaration of conflicting interests

There was no conflict of interest.

Acknowledgment

Thanks to the Pasteur Institute of Iran for providing research conditions.

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