Table 1
Total of confirmed cases and deaths due to COVID-19 in each region and globally (adapted from WHO, 2021) [8].
| REGION | CONFIRMED CASES | DEATHS (% LETHALITY RATE) |
|---|---|---|
| Africa | 2,924,244 | 74,143 (2.54) |
| Americas | 52,386,995 | 1,258,134 (2.40) |
| Eastern Mediterranean | 6,793,641 | 149,400 (2.20) |
| Europe | 40,438,291 | 897,540 (2.22) |
| South-East Asia | 13,819,871 | 211,740 (1.53) |
| Western Pacific | 1,694,716 | 30,076 (1.77) |
| Globally | 118,058,503 | 2,621,046 (2.22) |
[i] WHO, World Health Organization; %, percentage.
Figure 1
Comparison of COVID-19 data between Brazil and the most populated country of each continent (North America, Central America, Europe, Africa, Asia, Oceania) and Russia, as for March 12 2021. Total number of COVID-19 patients, total number of deaths (and total death rate), number of and deaths per one million of inhabitants, total number of active COVID-19 cases, total number of recovered cases (and total recovery rate), total number of SARS-CoV-2 RT-PCR tests made in the country, and number of inhabitants of SARS-CoV-2 RT-PCR tests made per one million inhabitants and vaccination rate. *People who have received at least one shot of a vaccine. The data were retrieved from WorldOMeter (https://www.worldometers.info/coronavirus/country/) and Our World In Data (http://ourworldindata.org/policy-responses-covid) [10, 24].
Figure 2
COVID-19: Stringency Index from Brazil. The graph represents a composite measure based on nine response indicators including school closures, workplaces closures, and travels ban, rescaled to value from 0 to 100 (100 = strictest). If policies vary at the subnational level, the index is shown as the response level of the strictest sub-region.
Source: Hale, Webster, Petherick, Phillips, and Kira (2020). Oxford COVID-19 Government Response Tracker – Last Update 15 March 2021, 08:00 (London time). Note: The index records the number and strictness of government policies and should be interpreted as “scoring” the appropriateness or effectiveness of a country’s response. Data search: OurWorldInData/coronavirus [24].
Figure 3
Stay-at-Home requirements during the COVID-19 pandemic.
Source: Hale, Webster, Petherick, Phillips, and Kira (2020). Oxford COVID-19 Government Response Tracker – Last Update 15 March 2021, 08:00 (London time). Note: There may be sub-national or regional differences in restrictions. The policy categories show may not apply at all sub-national levels. A country is coded as having these restrictions if a least some sub-national regions have implemented them. Data search: OurWorldInData/coronavirus [24].
Table 2
| STATES AND THE FEDERAL DISTRICT | CASES | DEATHS | CASE FATALITY RATE | CASES/100,00 INHABITANTS | DEATHS/100,00 INHABITANTS | ADHESION FOR SOCIAL ISOLATION (%) | RESPIRATORY VENTILATORS DISTRIBUTED BY MINISTRY OF HEALTH | RESPIRATORY VENTILATORS BILLED BY STATES | TOTAL AMOUNT PAID (R$) PER MILLION | AMOUNT PAID (R$) PER UNIT MILLION ON (THOUSAND) | TOTAL RT-PCR TESTS (TOTAL PAID R$) | TOTAL QUICK TESTS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Brasil | 11,277,717 | 272,889 | 2.42 | 5,366.6 | 129.9 | 14,725,497 (748,194,799) | 8,836,305 | |||||
| Midwest region | 1,196,427 | 24,014 | 2.01 | 7,341.4 | 147.4 | 972,888 (49,661,242) | 770,120 | |||||
| Goiás | 425,206 | 9,332 | 2.19 | 6,058.5 | 133.0 | 36.6 | 413 | 156,272 (8,281,255) | 252,240 | |||
| Mato Grosso | 266,939 | 6,097 | 2.28 | 7,660.8 | 175.0 | 38.8 | 216 | 120(50 national) | 7.4 (2.2) | 61.7 (44) | 203,808 (9,972,691) | 116,540 |
| Distrito Federal | 312,956 | 5,048 | 1.61 | 10,379.0 | 167.4 | 40.3 | 250 | 274,680 (14,013,668) | 300,640 | |||
| Mato Grosso do Sul | 191,326 | 3,537 | 1.85 | 6,884.7 | 127.3 | 37.1 | 155 | 11(+ 25 portable) | 1.5 (1.4) | 135 (55.7) | 338,128 (17,393,626) | 100,700 |
| South region | 2,183,813 | 35,899 | 1.64 | 7,285.2 | 119.8 | 2,887,556 (147,765,115) | 1,162,040 | |||||
| Santa Catarina | 717,454 | 8,377 | 1.17 | 10,013.6 | 116.9 | 38.7 | 98 | 50* | 33* | 165* | 334,264 (18,230,532) | 266,140 |
| Rio Grande do Sul | 720,461 | 14,363 | 1.99 | 6,332.5 | 126.2 | 43.6 | 486 | 571,284 (27,305,593) | 468,300 | |||
| Paraná | 745,898 | 13,159 | 1.76 | 6,523.5 | 115.1 | 37.6 | 544 | 1,982,008 (102,228,989) | 427,600 | |||
| North region | 1,226,601 | 28,982 | 2.36 | 6,655.1 | 157.2 | 1,440,140 (73,190,252) | 606,000 | |||||
| Acre | 61,394 | 1,094 | 1.78 | 6,961.3 | 124.0 | 43 | 170 | 129,724 (6,666,490) | 26,560 | |||
| Rondônia | 162,818 | 3,278 | 2.01 | 9,161.4 | 184.4 | 41 | 248 | 208,696 (10,412,872) | 58,060 | |||
| Tocantins | 122,426 | 1,623 | 1.33 | 7,783.6 | 103.2 | 35 | 115 | 168,196 (8,423,864) | 59,200 | |||
| Amazonas | 328,763 | 11,431 | 3.48 | 7,932.3 | 275.8 | 40.6 | 222 | 28 | 2.9 | 103.5 | 237,668 (11,761,688) | 162,060 |
| Amapá | 87,095 | 1,169 | 1.34 | 10,298.2 | 138.2 | 41.8 | 125 | 325,516 (15,139,839) | 23,840 | |||
| Pará | 379,196 | 9,171 | 2.42 | 4,407.8 | 106.6 | 38.2 | 409 | 400 | 50.4 | 126 | 260,236 (14,809,776) | 258,940 |
| Roraima | 84,909 | 1,216 | 1.43 | 14,016.9 | 200.7 | 39.4 | 162 | NI | NI | 215.4 | 110,104 (5,975,729) | 17,340 |
| Northeast region | 2,617,780 | 60,158 | 2.30 | 4,586.8 | 105.4 | 3,718,896 (187,054,809) | 2,103,440 | |||||
| Alagoas | 138,065 | 3,150 | 2.28 | 4,137.0 | 94.4 | 41.7 | 185 | 106,884 (5,947,398) | 112,920 | |||
| Pernambuco | 313,227 | 11,269 | 3.60 | 3,277.4 | 117.9 | 44.5 | 205 | 500 | NI | NI | 314,552(17,009,411) | 335,640 |
| Bahia | 730,542 | 12,961 | 1.77 | 4,911.8 | 87.1 | 42.2 | 491 | 300 | 48.7 | 162.4 | 836,932(40,490,720) | 531,300 |
| Paraíba | 234,254 | 4,832 | 2.06 | 5,829.9 | 120.3 | 41.8 | 285 | 30 | 4.9 | 164 | 155,548 (8,147,262) | 164,260 |
| Sergipe | 157.340 | 3,072 | 1.95 | 6,844.8 | 133.6 | 42.5 | 140 | 571,728 (26,433,375) | 79,760 | |||
| Piauí | 182,650 | 3,545 | 1.94 | 5,580.1 | 108.3 | 43.1 | 105 | 204,492(10,183,466) | 147,780 | |||
| Ceará | 456,948 | 12,087 | 2.65 | 5,003.8 | 132.4 | 42.1 | 268 | 1,039,460 (54,432,045) | 318,600 | |||
| Maranhão | 226,172 | 5,413 | 2.39 | 3,196.7 | 76.5 | 39.1 | 281 | 215,412 (10,812,174) | 233,800 | |||
| Rio Grande do Norte | 178,582 | 3,829 | 2.14 | 5,092.4 | 109.2 | 40.4 | 274 | NI | NI | ~70.4 | 273,888 (13,598,954) | 179,380 |
| Southeast region | 4,053,096 | 123,836 | 3.06 | 4,586.4 | 140.1 | 5,705,712(290,523,379) | 3,816,545 | |||||
| São Paulo | 2,164,066 | 63,010 | 2.91 | 4,712.8 | 137.2 | 38.5 | 838 | 3,000b | 550 | 189.2 | 2,536,944 (133,193,867) | 1,743,880 |
| Espírito Santo | 340,808 | 6,656 | 1.95 | 8,480.7 | 165.6 | 38.6 | 210 | 178,728 (8,856,083) | 202,300 | |||
| Rio de Janeiro | 601,666 | 34,083 | 5.66 | 3,484.9 | 197.4 | 40.8 | 993 | 1,000* | 183.5* | 183.5* | 2,228,728(114,858,287) | 1,049,245 |
| Minas Gerais | 946,556 | 20,087 | 2.12 | 4,471.5 | 94.9 | 37.8 | 561 | 1,047 | 51 | 48.7 | 761,312 (33,615,140) | 821,120 |
[i] NI, not informed. The data was collected at https://covid.saude.gov.br. Accessed on March 12, 2021. The number for social isolation was obtained at InLoco [56]. The number of respiratory ventilators distributed by Ministry of Health was collected on https://www.gov.br/pt-br/noticias/saude-e-vigilancia-sanitaria/2020/07/governo-federal-ja-entregou-mais-de-8-4-mil-ventiladores-pulmonares [9]. *, the purchase was canceled due to possible irregularities; **, the information was retrieved during July 2020. a, 1,820 were purchased with a cost of R$ 242,200,000.
Table 3
Distribution of indigenous people affected by COVID-19. Suspected, confirmed, recovered cases, and deaths distributed by the special indigenous health district (dSEI) on March 12, 2021 [9].
| dSEI | SUSPECTED CASES | CONFIRMED CASES | ACTIVE CASES | CLINICAL CURE (RECOVERED CASES) | DEATHS |
|---|---|---|---|---|---|
| Alagoas and Sergipe | 18 | 331 | 13 | 311 | 5 |
| Altamira | 0 | 1,713 | 3 | 1,707 | 2 |
| Alto Rio Juruá | 0 | 863 | 8 | 844 | 10 |
| Alto Rio Negro | 34 | 2,234 | 144 | 2,063 | 25 |
| Alto Rio Purus | 0 | 638 | 9 | 621 | 7 |
| Alto Rio Solimões | 0 | 2154 | 25 | 2074 | 47 |
| Amapá and Norte do Pará | 25 | 978 | 37 | 934 | 5 |
| Araguaia | 0 | 346 | 8 | 331 | 7 |
| Bahia | 18 | 932 | 70 | 852 | 8 |
| Ceará | 90 | 1,092 | 113 | 969 | 8 |
| Cuiabá | 32 | 1,301 | 56 | 1,221 | 24 |
| Guamá-Tocantins | 12 | 1,509 | 8 | 1,481 | 17 |
| Interior Sul | 103 | 2,647 | 143 | 2,456 | 46 |
| Kaiapó do Mato Grosso | 9 | 1,000 | 0 | 994 | 5 |
| Kaiapó do Pará | 29 | 1,222 | 0 | 1,177 | 9 |
| Leste de Roraima | 22 | 3,855 | 238 | 3,553 | 56 |
| Litoral Sul | 7 | 1,279 | 3 | 1,257 | 17 |
| Manaus | 28 | 1,136 | 2,252 | 962 | 16 |
| Maranhão | 0 | 1,687 | 1,042 | 1,654 | 27 |
| Mato Grosso Do Sul | 0 | 4,261 | 25 | 4,143 | 85 |
| Médio Rio Purus | 0 | 517 | 0 | 512 | 5 |
| Médio Rio Solimões and Afluentes | 6 | 765 | 22 | 730 | 11 |
| Minas Gerais and Espírito Santo | 14 | 580 | 39 | 534 | 6 |
| Parintins | 45 | 596 | 11 | 570 | 12 |
| Pernambuco | 17 | 624 | 4 | 607 | 10 |
| Porto Velho | 23 | 1,344 | 31 | 1,301 | 11 |
| Potiguara | 2 | 709 | 1 | 704 | 4 |
| Rio Tapajós | 0 | 2,016 | 47 | 1,950 | 16 |
| Tocantins | 1 | 1,176 | 0 | 1,162 | 10 |
| Vale do Javari | 0 | 822 | 0 | 818 | 2 |
| Vilhena | 70 | 899 | 0 | 883 | 15 |
| Xavante | 1 | 908 | 20 | 832 | 50 |
| Xingu | 103 | 1,029 | 278 | 718 | 16 |
| Yanomami | 9 | 1,485 | 807 | 664 | 11 |
| Total | 718 | 44,648 | 2,309 | 41,589 | 605 |
Figure 4
Differences between the Brazilian Private and Public health care systems from 01 March 2020 to 10 March 2021; ICU (Intensive Care Unit). The data were retrieved from Registro Nacional de Terapia Intensiva. [42].
Table 4
Spearman correlation between adhesion to social isolation measures (% of population) during the COVID-19 pandemic and number of votes to President Jair Messias Bolsonaro (% of population in the first and second round of election) [69].
| PHYSICAL ISOLATION | CORRELATION | FIRST ROUND OF ELECTIONS | SECOND ROUND OF ELECTIONS |
|---|---|---|---|
| First month (% after 30 days of first confirmed case) | Correlation coefficient | –0.299** | –0.197* |
| p-value | 0.002 | 0.046 | |
| Minor adhesion (%) | Correlation coefficient | –0.280** | –0.175 |
| p-value | 0.004 | 0.076 | |
| Major adhesion (%) | Correlation coefficient | –0.293** | –0.218* |
| p-value | 0.003 | 0.027 |
Table 5
Correlation between number of vaccine shots, SARS-CoV-2 RT-PCR tests and confirmed COVID-19 cases, deaths due COVID-19, and lethality rate. Data retrieved from 112 countries and territories.
| MARKERS | DATA | VACCINATION’S DOSAGES | VACCINATION PER 100 PEOPLE | NUMBER OF RT-PCR TESTS | NUMBER OF RT-PCR TESTS/1M INHABITANTS |
|---|---|---|---|---|---|
| Confirmed COVID-19 Cases | CC | 0.832 | –0.051 | 0.849 | –0.075 |
| P-value | <0.001 | 0.593 | <0.001 | 0.438 | |
| Death due COVID-19 | CC | 0.786 | –0.104 | 0.802 | –0.138 |
| P-value | <0.001 | 0.274 | <0.001 | 0.152 | |
| Lethality | CC | 0.284 | –0.210 | 0.335 | –0.251 |
| P-value | 0.002 | 0.026 | <0.001 | 0.008 | |
| Confirmed cases/1M | CC | 0.235 | 0.418 | 0.164 | 0.533 |
| P-value | 0.013 | <0.001 | 0.087 | <0.001 | |
| Death/1M | CC | 0.303 | 0.209 | 0.260 | 0.274 |
| P-value | 0.001 | 0.027 | 0.006 | 0.004 | |
| Number of RT-PCR tests | CC | 0.849 | –0.033 | 0.095 | |
| P-value | <0.001 | 0.731 | 0.327 | ||
| Number of RT-PCR tests/1M Inhabitants | CC | 0.023 | 0.645 | 0.095 | |
| P-value | 0.812 | <0.001 | 0.327 |
[i] CC, coefficient correlation; 1M, one million; RT-PCR, real time polymerase chain reaction. The Supplement 2 demonstrated the data used to perform the correlation. The information for COVID-19 Cases, Death due COVID-19, Lethality and SARS-CoV-2 RT-PCR was obtained using WorldOMeter. Cases of Coronavirus in Brazil. 2021. Accessed on March 11, 2021. Available at https://www.worldometers.info/coronavirus/country/ [10]. The reference for the number of vaccination’s dosages was retrieved from Coronavirus (COVID-19) Vaccinations on March 11, 2021 at https://ourworldindata.org/covid-vaccinations.
Table 6
Major differences between in Brazilian private and public health care (March 01 2020 to March 10 2021) [42].
| MARKERS | ALL | PRIVATE | PUBLIC |
|---|---|---|---|
| COVID-19 new hospitalizations | 106,546 | 74,405 | 32,141 |
| Ventilatory support | |||
| Noninvasive ventilatory support | 31.8% | 32.4% | 30.2% |
| Mechanical ventilation | 46.9% | 39.6% | 64.0% |
| Mechanical ventilation (days) | 13 | 14 | 11.5 |
| Amines | 33.0% | 27.8% | 45.3% |
| Kidney Support | 12.0% | 10.1% | 16.5% |
| ICU hospitalizations (days) | 12.2 | 11.9 | 12.7 |
| > 7 days | 49.9% | 48.0% | 54.4% |
| > 21 days | 14.5% | 14.4% | 14.9% |
| ICU mortality | 34.1% | 27.5% | 49.7% |
| Hospital mortality | |||
| All patients | 35.6% | 28.9% | 51.9% |
| All patients with no ventilatory support | 9.2% | 7.4% | 16.6% |
| All patients with ventilatory support | 66.6% | 63.1% | 71.6% |
| Dialysate patients | 74.2% | 71.2% | 78.6% |
[i] ICU, intensive care unit.
Figure 5
Health care workers affected by COVID-19 in Brazil after one year of pandemic. The data are given as percentages. Source: Brazil, Ministry of Health. 44th and 52nd Special Epidemiological Reports, 2020/2021. Accessed on 17 March 2021. Available at https://coronavirus.saude.gov.br/boletins-epidemiologicos [9].
Table 7
Number of health care worker accompted by SARS-CoV-2 infection during the first year of COVID-19 pandemic in Brazil.
| Health care worker | Number of COVID-19 cases until 27 February 2021 (%) |
|---|---|
| Nursing technicians and assistants | 159,786 (33.2%) |
| Nurses | 73,819 (15.3%) |
| Physicians | 53,549 (11.1%) |
| Community health agents | 24,540 (5.1%) |
| Health receptionist | 18,672 (3.9%) |
| Physiotherapists | 14,439 (3.0%) |
| Pharmaceuticals | 13,031 (2,7%) |
| Dental surgeons | 12,958 (2.7%) |
| Health promotion workers | 11,641 (2.4%) |
| Psychologists and psychoanalysts | 7,421 (1.5%) |
| Other HCW* | 91,429 (19.0%) |
| Total | 481,285 |
[i] * Other HCW accounts for: managers and operations specialists in companies, departments, and health service units, endemic health agents, ambulance drivers, caregivers, health managers, dentistry technician, nutritionists, pharmacy and pharmaceutical manipulation technicians, social workers and home economists, technicians from health laboratories and blood banks, public health agents, biomedical, radiology assistants, attention, defense, and protection workers for people at risk and adolescents in conflict with the law, technologists and technicians in diagnostic and therapeutic methods, work safety technicians, other teaching professionals, health laboratory assistants, veterinarians and zootechnicians, telephone operators, speech therapists, rescuers (except doctors and nurses), physicists, technicians in food production, preservation and quality, physical education professionals, occupational therapists, orthopedists and psychomotricists, piotechnology professionals, teachers, biologists, production, quality, safety and related engineers, biological sciences researchers, electro-electronics and photonics technician working in healthcare, orthopedic immobilization technicians, health and environmental agents, technologists and technicians in complementary and aesthetic therapies, chemistry teachers, photographic and radiological laboratory workers, technicians in orthopedic prostheses, health records and information workers, optics and optometry technicians, food and related engineers, music therapists, art therapists, equotherapists or naturologists, doulas, lay midwives, electricity and electrotechnical technicians, professionals of creative, equotherapic, and naturological therapies, biotechnology support technicians, funeral service workers, osteopaths and chiropractors, bioengineering support technicians, necropsy technicians, and taxidermists.
The information was acquired from Ministério da Saúde. 44º e 52º Boletim epidemiológico especial. 2020/2021. Accessed on 17 March 2021. Available at https://coronavirus.saude.gov.br/boletins-epidemiologicos [9].