SciELO - Scientific Electronic Library Online

vol.54Judicialization of medicines: effectiveness of rights or break in public policies?Trend of preventable deaths up to the 6th day of life in the state of São Paulo – 2008 to 2017 author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand



  • text new page (beta)
  • English (pdf)
  • Article in xml format
  • How to cite this article
  • SciELO Analytics
  • Curriculum ScienTI
  • Automatic translation


Related links


Revista de Saúde Pública

Print version ISSN 0034-8910On-line version ISSN 1518-8787

Rev. Saúde Pública vol.54  São Paulo  2020  Epub Dec 14, 2020 

Original Article

Population-based seroprevalence of SARS-CoV-2 and the herd immunity threshold in Maranhão

Antônio Augusto Moura da SilvaI

Lídio Gonçalves Lima-NetoII  III

Conceição de Maria Pedrozo e Silva de AzevedoIV  V

Léa Márcia Melo da CostaVI

Maylla Luanna Barbosa Martins BragançaVII

Allan Kardec Duailibe Barros FilhoVIII

Bernardo Bastos WittlinV  IX

Bruno Feres de SouzaX

Bruno Luciano Carneiro Alves de OliveiraIV

Carolina Abreu de CarvalhoXI

Erika Barbara Abreu Fonseca ThomazI

Eudes Alves Simões-NetoIX  XII

Jamesson Ferreira Leite JúniorXIII

Lécia Maria Sousa Santos CosmeII

Marcos Adriano Garcia CamposXIV

Rejane Christine de Sousa QueirozI

Sérgio Souza CostaX

Vitória Abreu de CarvalhoXIV

Vanda Maria Ferreira SimõesI

Maria Teresa Seabra Soares de Brito e AlvesI

Alcione Miranda dos SantosI

IUniversidade Federal do Maranhão. Departamento de Saúde Pública. São Luís, MA, Brasil

IISecretaria de Saúde do Estado do Maranhão. Laboratório Central do Maranhão. São Luís, MA, Brasil

IIIUniversidade CEUMA. São Luís, MA, Brasil

IVUniversidade Federal do Maranhão. Departamento de Medicina I. São Luís, MA, Brasil

VSecretaria de Saúde do Estado do Maranhão. Hospital Presidente Vargas. São Luís, MA, Brasil

VISecretaria de Saúde do Estado do Maranhão. Assessoria. São Luís, MA, Brasil

VIIUniversidade Federal do Maranhão. Departamento de Ciências Fisiológicas. São Luís, MA, Brasil

VIIIUniversidade Federal do Maranhão. Departamento de Engenharia Elétrica. São Luís, MA, Brasil

IXUniversidade Federal do Maranhão. Hospital Universitário. São Luís, MA, Brasil

XUniversidade Federal do Maranhão. Departamento de Engenharia da Computação. São Luís, MA, Brasil

XIUniversidade Federal do Maranhão. Curso de Medicina. Pinheiro, MA, Brasil

XIISecretaria Municipal de Saúde. São Luís, MA, Brasil

XIIISecretaria de Saúde do Estado do Maranhão. Centro de Informações Estratégicas de Vigilância em Saúde. São Luís, MA, Brasil

XIVUniversidade Federal do Maranhão. Programa de Pós-Graduação em Saúde Coletiva. São Luís, MA, Brasil



To estimate the seroprevalence of SARS-CoV-2 in the state of Maranhão, Brazil.


A population-based household survey was performed, from July 27, 2020 to August 8, 2020. The estimates considered clustering, stratification and non-response. Qualitative detection of IgM and IgG antibodies was performed in a fully-automated Elecsys® Anti-SARS-CoV-2 electrochemiluminescence immunoassay on the Cobas® e601 analyzer (Roche Diagnostics).


In total, 3,156 individuals were interviewed. Seroprevalence of total antibodies against SARS-CoV-2 was 40.4% (95%CI 35.6-45.3). Population adherence to non-pharmaceutical interventions was higher at the beginning of the pandemic than in the last month. SARS-CoV-2 infection rates were significantly lower among mask wearers and among those who maintained social and physical distancing in the last month compared to their counterparts. Among the infected, 26.0% were asymptomatic. The infection fatality rate (IFR) was 0.14%, higher for men and older adults. The IFR based on excess deaths was 0.28%. The ratio of estimated infections to reported cases was 22.2.


To the best of our knowledge, the seroprevalence of SARS-CoV-2 estimated in this population-based survey is one of the highest reported. The local herd immunity threshold may have been reached or might be reached soon.

DESCRIPTORS: Seroepidemiologic Studies; Coronavirus Infections; Immunity, Herd; Mortality


Brazil is one of the countries most severely affected by the coronavirus disease 2019 (COVID-19) pandemic. By September 21, 2020, 4,558,040 cases were reported, with 137,272 deaths 1. The national response has been controversial, testing capacity is low, and disagreements among the different levels of government over social distancing measures conveyed contradictory messages to the population. As a middle-income country, Brazil has high poverty rates and an extensive part of its population is engaged in informal activities that face difficulties to make ends meet and to follow stay-at-home measures 2 . As a consequence of all these facts, social distancing has not reached levels sufficient to curb and contain the COVID-19 pandemic 3 .

The state of Maranhão is located in the Northeast region of Brazil and has a population of 7,114,598 inhabitants in 2020 4 , with an area of 329,642 km², a little larger than that of Italy. It is one of the states in Brazil, where the pandemic gathered speed early. Its first case was reported on March 20, 2020, and by September 21, 2020 the number of deaths reported was 3,664. Deaths peaked in May and decreased thereafter. From May 3, 2020 to May 17, 2020, São Luís Island, where the state capital city is located, was put into lockdown. Reduction of social mobility reached at most 55% at the end of March and during lockdown at the capital, remaining low (40%–45%) during the worst phase of the pandemic. Despite low home quarantine adherence, the number of deaths decreased, and intensive care units occupancy diminished 5 .

Herd immunity threshold to attain control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an ongoing debate. Although some consider it to be around 60%–70%, using the classical formula 1–1/R0, in which R0 is the basic reproductive number 6 , other reports have proposed that herd immunity could be as low as 10%–20% 7 or around 43% 8 , due to the heterogeneity in susceptibility or exposure to infection across population groups 7,8 . However, reported population-based seroprevalences of SARS-CoV-2 were lower than the herd immunity thresholds, ranging from extremely low infections rates, close to 1%–3% 9,10 , to values as high as 14.3% in Barcelona 11 , Spain, and 22.7% in New York City 12 . In Brazil, the highest reported population-based seroprevalences were 17.9%, for the São Paulo municipality 13 , and 66% for Manaus, where herd immunity may have played an important role in stablishing the size of the epidemic 14 .

The infection fatality rate (IFR) and the percentage of asymptomatic infections of SARS-CoV-2 are known with uncertainty. Early reports at the beginning of the pandemic estimated IFR at values between 0.6% and 1.3% 15,16 , and considered asymptomatic infections as being highly prevalent 15,17 . Most recent reviews, however, estimated a lower IFR with large variations across sites 10,18 and a much lower percentage of asymptomatic infections 11,19,20 .

Population-based surveys are necessary to monitor the infection progression, since most cases are undocumented 21 . However, few population-based studies on the prevalence of SARS-CoV-2 have been performed, especially in low and middle-income countries. In this population-based study, we estimated the overall seroprevalence of SARS-CoV-2 using a serum testing electrochemiluminescence immunoassay. Sociodemographic characteristics of the population, self-reported symptoms, adherence to non-pharmaceutical interventions (NPI), use of health services, previous molecular and antibody testing among the infected, and the IFR were also assessed.


Study Design and Participants

A cross-sectional survey to estimate the seroprevalence of antibodies against SARS-CoV-2 was conducted from July 27, 2020 to August 8, 2020 by population-based household sampling, in cooperation between the Universidade Federal do Maranhão and the Secretaria de Saúde do Estado do Maranhão, Brazil.

Conglomerate sampling in three stratified stages in four regions was used. The regions were the Island of São Luís, including the state capital, small municipalities (< 20,000 inhabitants), medium-sized municipalities (20,000 to 100,000 inhabitants) and large municipalities except for the island (> 100,000 inhabitants). In each stratum, in the first stage, 30 census tracts were selected by systematic sampling. In the second stage, 34 households were selected in each census tract by systematic sampling. In the third stage, an eligible resident (residing for at least six months in the household) aged one year or more was selected by simple random sampling using a table of random numbers.

Data Collection, Instruments, and Variables

Trained professionals from the municipal and state health departments were responsible for data collection. The starting point (identified with an × on the map) and the geographic boundaries of each census tracts were identified using a map provided by the Brazilian Institute of Geography and Statistics (IBGE). The first interview was held in the household closest to the starting point of each sector. Then, facing that domicile, the interviewer walked to the left with his/her right shoulder facing the wall/residences. Without including the visited house, the interviewer counted five residences and conducted the next interview in the fifth one. If the selected household was empty at the time or the elected person did not agree to participate in the survey, the next house to the left (neighbor) of the original one was taken as a replacement. If that house was also empty or if the elected person refused to participate the next house to the left was visited. Then the interviewer counted five domiciles and conducted the next interview in the fifth house after the original one. The team always proceeded to the left in relation to the last surveyed domicile and conducted the next interview in the fifth domicile. Non-residential buildings were excluded from the count. After completing the tour on the block, the interviewer facing the last visited domicile continued to the next adjacent block located to the left, always adopting the same strategy.

A questionnaire with closed-ended questions was applied in a face-to-face interview with the individual or his/her legal guardian. The questionnaire was composed of sociodemographic questions, adherence to NPI, self-reported symptoms, and the use of health services. The sociodemographic questions included sex, age group, self-reported skin color/race, head of the household's schooling, monthly family income in Brazilian Reals, and the number of the household residents. Head of the household's schooling was classified according to the International Standard Classification of Education (ISCED) 2011 into early childhood/primary/lower secondary education (levels 0–2), upper secondary/post-secondary non-tertiary education (levels 3–4), and tertiary education and beyond (levels 5–8) 22 . Skin color/race was categorized according to the IBGE and divided into white, brown, or black 23 .

Adherence to NPI at the beginning of the pandemic and in the last month included social distancing (yes, if the person never leaves home or seldom goes out, with a maximum of one outing every fifteen days, and no otherwise), wearing of face masks (yes, if the individual uses a mask on all exits and does not remove or seldom removes the mask from the face, and no otherwise), hand hygiene (yes, if the person sanitizes the hands more than six times per turn with soap or an alcohol gel, and no otherwise), and physical distancing (yes, if the individual never or hardly ever comes within 1.5 m of other people, and no otherwise).

A self-reported symptom rating, adapted from Pollán et al. (2020) 11 was used and the persons were classified into asymptomatic; oligosymptomatic: the presence of one to two symptoms without anosmia/hyposmia or ageusia/dysgeusia; and symptomatic: anosmia/hyposmia or ageusia/dysgeusia or more than two symptoms including fever, chills, sore throat, cough, dyspnea, diarrhea, nausea/vomiting, headache, fatigue, and myalgia.

Questions on the use of health services included if the individual looked for health services, received care when seeking health services, was hospitalized for over 24 hours, received a medical diagnosis of suspected COVID-19, performed RT-PCR for SARS-CoV-2, and performed an antibody test– point-of-care/serology for SARS-CoV-2.

Data were abstracted into the Epicollect5 Data Collection mobile application.

SARS-CoV-2 Antibodies Detection

For the qualitative determination of antibodies against SARS-CoV-2, 5.0 ml of whole blood was collected, and after centrifugation at 1800 g for 15 min, the serum was obtained. Then, a highly sensitive and specific sandwich electrochemiluminescence immunoassay (Elecsys® Anti-SARS-CoV-2 assay, Roche Diagnostics) was used to detect IgM and IgG antibodies against the SARS-CoV-2 nucleocapsid antigen according to the manufacturer's instruction using a fully automated Cobas® e601 immunoassay analyzer (Roche Diagnostics) 24 .

Sample Size Calculation

The formula used to determine the sample size in each stratum was given by


N being the population size in each stratum; P the expected prevalence in the stratum; Q=1-P; and CV the coefficient of variation of the prevalence estimates within the stratum. In each stratum, the expected prevalence of infection was 20%, and the coefficient of variation was 10%. For the final estimate, a design effect of 2 was added. Thus, the minimum number of individuals per stratum was 800, totaling 3,200 individuals to compose the sample. Predicting losses, the sample size was increased by 25% resulting in 4,000 observations.

Statistical Analysis

The basic sample weight of each selected unit (census sector, household, and individual) was estimated separately for each stratum, considering the inverse of the selection probability according to the sampling plan specified for the study.

The probability of selection of the census sector “j” in each stratum “i” of the sample is given by 30/Si, in which “Si” is the number of census sectors of the stratum “i” in the population and the probability of the domicile of the census sector “j” of the stratum “i” being selected was obtained from the following expression: 34/Dij, in which “Dij” is the number of domiciles in sector “j” of the stratum “i” in the population. The probability of each resident in the selected household was given by 1/(number of residents in the household). The number of sectors and domiciles was obtained from the 2010 Census of the IBGE.

Since losses, refusals, and non-responses occurred, the response rate in each stratum was also estimated. Considering that there were three stages, the final weight was obtained by the product of the basic weight in each stage and the response rate.

All analyses were performed using R version 4.0.2. Weighting factors, clustering, and stratification were incorporated into the analyses via the R survey package. Prevalence and 95% confidence interval (95%CI) of SARS-CoV-2 infection was obtained according to the sociodemographic characteristics, adherence to NPI, self-reported symptoms, and the use of health services. The chi-square test, considering the study design, was used to compare the prevalence between groups. The McNemar test was used to compare adherence to NPI over time.

The overall and sex- and age-specific IFR were estimated by dividing the estimated number of deaths by the estimated proportion of infections obtained by the serological survey multiplied by the stratified age and sex population estimates 4 . The number of deaths that occurred up to August 8, 2020 was abstracted from official sources 5 . The number of deaths occurring daily was estimated using Nowcasting by Bayesian Smoothing (NobBS), to consider reporting delays. This procedure incorporates uncertainty both in the delay distribution and in the evolution of the pandemic curve over time, resulting in smooth, time-correlated estimates of the number of deaths 25 . Simulations were conducted using the NobBS R package, with a negative binomial model with an adaptation phase of 10,000 iterations and a burn-in of 10,000 iterations for estimating deaths in the state of Maranhão, and the same parameters with 5,000 iterations for the São Luís Island. Furthermore, since there is underascertainment of deaths due to COVID-19, IFR was also estimated considering excess mortality due to all natural causes. Excess deaths were abstracted from the Panel to analyze the excess mortality from natural causes in Brazil in 2020 26 . Data on excess mortality is only available stratified by sex and two age groups (< 60 and ≥ 60 years) 27 . The 95% confidence intervals for the IFR were based on delta methods accounting for the binomial variance in the numerator (number of deaths) and the estimated variance, considering the complex sampling design in the denominator (number of infections) 28 .

Ethical Approval

Ethical approval was obtained from the Research Ethics Committee of the Carlos Macieira Hospital of the Maranhão State Health Secretariat under CAAE number 34708620.2.0000.8907. An informed consent form was provided by the participants or the parents/legal guardians.


A total of 3,289 individuals (80.6%) agreed to participate in our study. After the exclusion of samples with insufficient material or hemolyzed samples, and cases, in which it was not possible to link the result of the examination with the person, 3,156 participants had their blood samples analyzed (77.4%). Comparing the sampling with the population distribution (age and sex estimates for 2020), men and people of working age were underrepresented in the sample.

Seroprevalence of total antibodies against SARS-CoV-2 was 40.4% (95%CI 35.6-45.3) in the state of Maranhão, Brazil. Seroprevalence varied by region, from 20.0% in small municipalities with < 20,000 inhabitants, reaching 47.6% in medium-sized municipalities from 20,000 to 100,000 inhabitants (p = 0.006). Seroprevalence in the São Luís Island, including the capital city, was 38.9%. There were no significant differences in the prevalence according to the sex or age group ( Table 1 ).

Table 1 Prevalence of antibodies against SARS-CoV-2 by region, sex, age group, race, schooling, family income and number of residents, state of Maranhão, Brazil, 2020 

Variables n % weighted Population distribution (%) f infected % infected weighted (95%CI) p
Total 3156 100.0 100.0 1167 40.4 (35.6−45.3)
Region 0.006
São Luís Island including the capital 737 25.5 20.2 349 38.9 (24.5−53.2)
Municipalities with < 20,000 inhabitants 754 20.0 21.4 215 31.0 (24.3−37.8)
Municipalities with 20,000 to 100,000 inhabitants 839 41.6 45.1 346 47.6 (42.0−53.1)
Municipalities with > 100,000 inhabitants 826 12.9 13.2 257 35.2 (26.1−44.3)
Sex 0.134
Male 1200 37.1 49.1 426 37.2 (31.8−42.6)
Female 1956 62.9 50.9 741 42.4 (36.1−48.6)
Age group (years) a 0.230
1−9 124 5.2 16.5 49 42.6 (33.8−51.3)
10−19 330 14.7 18.6 125 43.0 (33.5−52.4)
20−29 427 12.5 18.0 184 49.2 (41.1−57.3)
30−39 475 15.0 16.0 170 44.4 (37.4−51.4)
40−49 502 16.3 12.0 191 32.2 (23.4−41.0)
50−59 501 14.6 8.5 168 39.1 (32.1−46.1)
60−69 409 11.7 5.7 144 40.3 (29.1−51.4)
≥ 70 386 9.8 4.8 136 34.3 (25.7−42.9)
Self-reported skin color/race b 0.080
White 590 20.0 - 200 32.2 (20.1−44.4)
Brown 2100 67.4 - 767 41.3 (37.1−45.4)
Black 396 12.6 - 177 49.1 (42.3−55.9)
Head of the household's schooling (years)* 0.011
Primary/Lower secondary 1369 37.7 - 487 40.9 (35.4−46.4)
Upper secondary 1251 41.9 - 512 46.2 (41.3−51.2)
Tertiary 517 20.4 - 161 27.5 (16.9−38.1)
Monthly family income (Brazilian Real) a , c 0.101
< 1000 607 17.7 - 222 40.8 (34.6−46.9)
1000 a < 2000 1405 42.5 - 540 45.9 (41.0−50.9)
2000 a < 3000 617 20.8 - 243 42.9 (35.7−50.2)
> 3000 493 19.0 - 155 27.9 (17.0−38.9)
Number of residents 0.028
1 386 3.9 - 122 35.4 (27.2−43.6)
2 840 15.7 - 302 37.7 (32.0−43.5)
3 739 21.2 - 297 44.9 (40.0−49.8)
4 628 28.0 - 234 38.8 (29.0−48.5)
≥ 5 563 31.2 - 212 40.9 (33.5−48.3)

95%CI: 95% confidence interval; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.

aNumbers did not add up to total because of missing values.

bYellow and indigenous were excluded because they were too few for a meaningful analysis.

c1 Brazilian Real (R$) is equivalent to approximately US$ 5.60 US dollars.

White people had a lower point prevalence (20.0%) when compared with both brown (41.3%) and black people (49.2%), but of borderline significance (between 0.05 and 0.10). Persons with tertiary education had a lower prevalence of infection (27.5%) than their counterparts (p = 0.011). Although point prevalence was lower among those with a monthly family income above 3,000 Brazilian Reals, the difference did not reach a significant level. Infection rates were higher among households with three dwellers (44.9%) (p = 0.028) ( Table 1 ).

Population adherence to NPI to contain the COVID-19 pandemic were mostly higher at the beginning of the pandemic than in the last month. Social distancing decreased from 52.7% to 37.4% (p < 0.001). The percentage of wearing a face mask decreased from 61.4% to 55.5% (p < 0.001). Differences in infection rates between those that maintained social distancing and those that did not were evident both at the beginning of the pandemic (36.4% vs 45.0%, p = 0.020) and in the last month (34.0% vs 44.3%, P = 0.015). SARS-CoV-2 infection rates were significantly lower in the last month among mask wearers and those that maintained a distance of at least 1.5 m from other people compared to their counterparts (p = 0.036 for mask-wearing and p = 0.030 for physical distancing) ( Table 2 ).

Table 2 Prevalence of antibodies against SARS-CoV-2 according to adherence to non-pharmaceutical interventions at the beginning of the pandemic and in the last month, state of Maranhão, Brazil, 2020 

Non-pharmaceutical interventions n % weighted f infected % infected weighted (95%CI) p
At the beginning of the pandemic
Social distancing 0.020
No 1392 47.3 557 45.0 (39.3−50.6)
Yes a 1764 52.7 610 36.4 (30.6−42.2)
Wearing of face masks 0.395
No 1153 38.6 423 42.3 (35.8−48.8)
Yes b 2003 61.4 744 39.3 (33.7−44.8)
Hand hygiene 0.285
No 1455 47.9 554 42.7 (36.9−48.4)
Yes c 1701 52.1 613 38.4 (31.9−44.9)
Physical distancing 0.065
No 1548 52.2 602 43.5 (37.6−49.4)
Yes d 1608 47.8 565 37.1 (31.4−42.8)
Last month
Social distancing 0.015
No 1875 62.6 757 44.3 (39.6−49.0)
Yes a 1281 37.4 410 34.0 (26.5−41.4)
Wearing of face masks 0.036
No 1310 44.5 517 45.9 (40.6−51.3)
Yes b 1846 55.5 650 36.0 (29.1−43.0)
Hand hygiene 0.095
No 1557 51.6 612 44.4 (39.1−49.7)
Yes c 1599 48.4 555 36.2 (28.7−43.8)
Physical distancing 0.030
No 1817 61.0 710 43.3 (38.0−48.6)
Yes d 1339 39.0 457 35.9 (29.7−42.2)

95%CI: 95% confidence interval; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.

aNever leaves home or seldom goes out, with a maximum of one outing every fifteen days.

bUses mask on all exits and does not remove or seldom removes the mask from the face.

cSanitizes the hands ≥ 6 times per turn (morning, afternoon, and night) with soap or alcohol gel.

dNever or hardly ever comes within 1.5 m of other people.

Differences in the self-reporting symptoms were highly significant comparing those with and without antibodies to SARS-CoV-2. Among the infected, 62.2% had more than three symptoms, whereas 26.0% were asymptomatic and, 11.8% reported only one or two symptoms (oligosymptomatic). The predominant symptoms among those who tested positive for SARS-CoV-2 were anosmia/hyposmia (49.5%), ageusia/dysgeusia (47.7%), fever (45.6%), headache (45.4%), myalgia (43.6%), and fatigue (41.1%) ( Table 3 ).

Table 3 Reported symptoms of SARS-CoV-2 infection, state of Maranhão, Brazil, 2020 

Variables Non-infected (n = 1,989) Infected (n = 1,167) p
n % weighted (95%CI) n % weighted (95%CI)
Self-reported symptom rating a < 0.001
Asymptomatic 1104 52.1 (47.3−56.9) 320 26.0 (21.0−31.0)
Oligosymptomatic (1 to 2 symptoms) 427 22.8 (18.8−26.7) 134 11.8 (8.9−14.6)
Symptomatic (≥ 3 symptoms) 458 25.2 (21.8−28.5) 713 62.2 (56.2−68.3)
Self-reported symptoms
Fever 296 16.7 (13.1−20.4) 494 45.6 (39.9−51.3) < 0.001
Shivers 253 13.7 (9.9−17.6) 379 34.3 (29.5−39.2) < 0.001
Sore throat 345 18.3 (14.7−22.0) 378 34.5 (30.1−39.0) < 0.001
Cough 356 17.6 (13.7−21.5) 369 33.1 (29.7−36.5) < 0.001
Dyspnoea 184 10.9 (8.4−13.4) 209 18.6 (15.0−22.2) 0.001
Runny nose 370 19.2 (16.0−22.3) 364 32.2 (28.0−36.5) < 0.001
Palpitations 204 9.5 (7.1−11.9) 178 15.6 (12.1−19.2) < 0.001
Anosmia/Hyposmia 117 7.3 (5.0−9.6) 547 49.5 (42.5−56.5) < 0.001
Ageusia/Dysgeusia 133 8.2 (5.8−10.5) 535 47.7 (40.4−54.9) < 0.001
Diarrhoea 186 8.9 (6.5−11.2) 210 18.1 (15.1−21.2) < 0.001
Nausea/vomiting 146 7.2 (5.2−9.2) 177 15.1 (12.0−18.2) < 0.001
Headache 509 27.5 (22.7−32.2) 491 45.4 (38.7−52.0) < 0.001
Abdominal pain 201 10.8 (7.7−13.9) 196 19.8 (14.5−25.1) 0.009
Myalgia 368 17.9 (14.0−21.8) 485 43.6 (36.8−50.3) < 0.001
Fatigue 333 17.1 (13.0−21.3) 449 41.1 (34.5−47.7) < 0.001
Loss of appetite 217 11.1 (8.2−14.0) 396 35.2 (29.2−41.2) < 0.001

95%CI: 95% confidence interval; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.

aAsymptomatic: no symptoms; oligosymptomatic: presence of 1 to 2 symptoms without anosmia/hyposmia or ageusia/dysgeusia; symptomatic: anosmia/hyposmia or ageusia/dysgeusia or more than 2 symptoms including fever, chills, sore throat, cough, dyspnea, diarrhea, nausea/vomiting, headache, fatigue, and myalgia.

Among the infected, 27.6% sought medical care and most received it. A small minority (1.9%) was hospitalized for over 24 hours, 13.3% were told they were suspected of having COVID-19, 4.3% performed an RT-PCR for SARS-CoV-2, and 13.5% performed a point of care test/serology for SARS-CoV-2 ( Table 4 ).

Table 4 Use of health services by individuals with SARS-CoV-2 antibodies, state of Maranhão, Brazil, 2020 

Variables n % 95%CI
Looked for health service
No 888 72.4 64.6−80.3
Yes 279 27.6 19.7−35.4
Received care when sought health service
Yes 239 19.0 14.9−23.0
No 40 8.6 0.0−18.1
Did not look for health service 888 72.4 64.6−80.3
Hospitalized for over 24 hours
No 1148 98.1 96.9−99.2
Yes 19 1.9 0.8−3.1
Received a medical diagnosis of suspected COVID-19
No 1014 86.7 83.1−90.2
Yes 153 13.3 9.8−16.9
Performed RT-PCR for SARS-CoV-2
No 1123 95.7 93.5−98.0
Yes 44 4.3 2.0−6.5
Performed antibody test (point-of-care/serology) for SARS-CoV-2
No 1019 86.5 82.8−90.1
Yes 148 13.5 9.9−17.2
Total 1167 100.0

95%CI: 95% confidence interval; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.

The IFR was 0.14% for the state of Maranhão, and 0.28% for the São Luís Island, considering reporting delays by NobBS. IFR was higher for men and older adults ( Table 5 ). The estimate doubled to 0.28%, using data on excess mortality ( Table 6 ). The case reporting rate was 4.5% for the state of Maranhão, and 3.4% for the São Luís Island, resulting in a ratio of the estimated infection to the reported cases as 22.2 for the state of Maranhão, and 29.9 for the São Luís Island (data not shown).

Table 5 Estimated number of infections, deaths, and infection fatality rates of SARS-CoV-2 by sex and age groups, state of Maranhão and São Luís island, Brazil, 2020 

Sex Age Group, years Estimated number of infections Number of deaths (estimated by nowcasting) Infection fatality rate, % (95%CI)
0−9 274,241 8 0.00 (0.00−0.01)
10−19 236,559 12 0.00 (0.00−0.01)
20−29 303,111 21 0.01 (0.00−0.01)
30−39 198,478 76 0.04 (0.03−0.05)
40−49 100,908 150 0.15 (0.10−0.21)
50−59 101,695 274 0.27 (0.18−0.40)
60−69 64,637 584 0.90 (0.68−1.20)
≥ 70 67,000 1381 2.06 (1.56−2.72)
Total 1,299,992 2506 0.19 (0.17−0.22)
0−9 228,212 14 0.01 (0.00−0.01)
10−19 323,171 5 0.00 (0.00−0.00)
20−29 320,049 16 0.01 (0.00−0.01)
30−39 282,436 56 0.02 (0.01−0.03)
40−49 155,868 81 0.05 (0.04−0.08)
50−59 132,770 163 0.12 (0.09−0.16)
60−69 94,033 345 0.37 (0.26−0.52)
≥ 70 48,838 898 1.84 (1.25−2.69)
Total 1,533,005 1579 0.10 (0.09−0.12)
0−9 500,448 22 0.00 (0.00−0.01)
10−19 567,266 16 0.00 (0.00−0.00)
20−29 628,088 37 0.01 (0.00−0.01)
30−39 505,975 132 0.03 (0.02−0.03)
40−49 275,270 231 0.08 (0.06−0.11)
50−59 237,395 437 0.18 (0.15−0.22)
60−69 162,429 930 0.57 (0.43−0.76)
≥ 70 116,065 2279 1.96 (1.53−2.52)
Total 2,877,454 4085 0.14 (0.13−0.16)
São Luís Island
Overall Total 556,611 1544 0.28 (0.19−0.40)

IFR: infection fatality rate; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.

Table 6 Estimated number of infections, excess deaths, and infection fatality rates of SARS-CoV-2 by sex and age groups, state of Maranhão, Brazil, 2020 

Sex Age Group, years SARS-CoV-2 seroprevalence, % (95%CI) Estimated number of infections Number of deaths (estimate based on excess deaths due to natural causes) a Infection fatality rate, % (95%CI)
0−59 36.38 (30.54−42.22) 1,149,733 1366 0.12 (0.10−0.14)
≥ 60 39.85 (32.74−46.95) 133,937 3903 2.91 (2.43−3.49)
Total 37.18 (31.81−42.55) 1,299,992 5270 0.41 (0.35−0.47)
0−59 44.05 (38.71−49.40) 1,415,266 563 0.04 (0.03−0.05)
≥ 60 36.02 (24.46−47.58) 146,117 2278 1.56 (1.13−2.15)
Total 42.37 (36.11−48.63) 1,533,005 2840 0.19 (0.16−0.22)
0−59 41.26 (36.84−45.69) 2,629,556 1929 0.07 (0.07−0.08)
≥ 60 37.54 (29.24−45.85) 278,482 6181 2.22 (1.78−2.77)
Total 40.44 (35.57−45.32) 2,877,454 8110 0.28 (0.25−0.32)

IFR: infection fatality rate; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2.

aFonte: Conselho Nacional de Secretários de Saúde. Painel de análise do excesso de mortalidade por causas naturais no Brasil em 2020. Brasília, DF: CONASS; 2020 [cited 2020 Sept 21]. Available from:

Figure 1 shows dates of introduction of compulsory NPI, the weekly number of deaths by dates of occurrence and reporting and estimates of the weekly number of deaths based on NobBS, considering reporting delays. The pandemic peaked from May 17, 2020 to May 23, 2020 in the state of Maranhão and from May 3, 2020 to May 9, 2020 in the São Luís Island ( Figure 2 ). Since then, the number of deaths has been decreasing, and economic activity has been gradually increasing whereas most restrictions, apart from banning mass gatherings and opening of public schools and universities, have been eased. Nearly three months since the beginning of the relaxation of social distancing, and despite increasing community mobility, reported deaths analyzed by date of occurrence remain low.

Figure 1 Weekly number of deaths by occurrence and reporting date, and estimated by Bayesian nowcasting from March 15 to September 19, state of Maranhão, Brazil, 2020. 

Figure 2 Weekly number of deaths by occurrence and reporting date, and estimated by Bayesian nowcasting from March 15 to September 19, São Luís Island, state of Maranhão, Brazil, 2020. 


The population-based seroprevalence of SARS-CoV-2 in the state of Maranhão, Brazil was 40.4%. We believe this is the first population-based study to report a prevalence rate in this range, for an area as big as Italy.

Over 90% of all infected people develop detectable antibodies against SARS-CoV-2 two weeks after infection 29 . Moreover, SARS-CoV-2 leads to robust memory T cell responses, suggesting that infection may at least prevent subsequent severe disease 30 . Furthermore, cross-reactivity between SARS-CoV-2 and coronaviruses that cause the common cold may elicit additional protection against infection 31 . Due to all these factors and based on a high seroprevalence of 40.4% achieved in the survey, our data suggests that the local herd immunity threshold may have been reached or might be reached soon, depending on the patterns of heterogeneity in susceptibility or exposure to infection 7,8 .

Nevertheless, the achievement of herd immunity will not be sustained if protection wanes 32 . Thus, durable immunity may not be attained before vaccination, and consequently, the population would remain susceptible to future recurrent outbreaks 6 .

In our study, we used the Elecsys® Anti-SARS-CoV-2 electrochemiluminescence immunoassay, which presented a high specificity rate of 99.7% (95%CI 99.2-100.0) and a positive predictive value (PPV) of 97.4% with a 10% seroprevalence rate 33 . Electrochemiluminescence immunoassays have been shown to present higher sensitivity than lateral flow immunoassays 34 . Some existing lateral flow immunoassays do not attain an ideal performance to be used in seroprevalence surveys, especially if they are used with finger-prick 35 . Therefore, since the test we used is more sensitive and specific, we could detect a higher percentage of people with antibodies against SARS-CoV-2 with few false-positive results. The distribution and percentage of self-reported symptoms among the infected in our survey were similar to what has been reported by others 9,11,36 , providing further evidence that a high false-positive rate in our study is unlikely. However, a negative Roche's Anti-SARS-CoV-2 serology assay does not rule out infection 37 . Moreover, sensitivity may decline over time due to seroreversion 38 . Therefore, we may have underascertained the true SARS-CoV-2 infection rate.

We could not find evidence that infection rates differ by sex, age group, skin color, or income; however, given the survey's complex sampling design, our sample size lacked the statistical power to answer these questions. The infection rates were lower among those with tertiary education, in agreement with the São Paulo study 39 .

Infection rates were lower among mask wearers and among those that maintained social and physical distancing, suggesting that the use of face masks 40 and social 41,42 and physical distancing 40 were necessary to prevent further infections and deaths. However, adherence to NPI to curb the COVID-19 pandemic tended to diminish.

Infected people were mostly symptomatic (62.2%), and anosmia/hyposmia and ageusia/dysgeusia were the two most reported symptoms. Most cases were mild. These findings are in agreement with recent studies 19,36 .

Our estimate of the IFR for the state of Maranhão was lower than the rate (0.71%) estimated for Brazil 9 , the 0.90% estimate described for the UK 36 and the combined estimate of 0.68% from a meta-analysis by Meyerowitz-Katz et al. (2020) 18 , but more in line with the 0.24% combined estimate obtained by Ioannidis (2020) 10 and with the range of 0.30%-0.50% estimated by Bayesian Network Analysis 16 . Variations in IFR may be due to differences in the testing capacity, age structures, selective testing of high-risk populations, patterns of how deaths are attributed to COVID-19 6 , and strain on the health services 43 . Therefore, IFR is likely to vary across populations. However, the IFR in Maranhão is one of the lowest reported to date 10 , even after considering reporting delays and excess deaths.

In our study, the case reporting rate was 4.5% for the state of Maranhão and 3.4% for the São Luís Island, resulting in a ratio of the estimated infection to the reported cases as 22.2 for the state of Maranhão and 29.9 for the São Luís Island. These ratios were higher than the value of 10.3 reported for Brazil 9 .

Our study has strong points: it is population-based, had a high response rate of 77.4%, and the use of a serum electrochemiluminescence immunoassay testing instead of a lateral flow immunoassay with finger-prick. There are some limitations: for some estimates, the confidence intervals were wide, and thus our power to detect statistically significant associations was lower than that desired; some population groups (men and people of working age) were underrepresented in our sample.


Secretaria de Saúde do Estado do Maranhão and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes - finance code 001, Brasil)


1. Johns Hopkins University. COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. Baltimore, MD: CCSE; 2020 [cited 2020 Sept 21]. Available from: ]

2. The Lancet. COVID-19 in Brazil: “So what?” Lancet. 2020;395(10235):1461. ]

3. Candido DS, Claro IM, Jesus JG, Souza WM, Moreira FRR, Dellicour S, et al. Evolution and epidemic spread of SARS-CoV-2 in Brazil. Science. 2020;369(6508):1255-60. ]

4. Instituto Brasileiro de Geografia e Estatística. Projeção da população por sexo e idades simples, em 1° de julho - 2010/2060. Rio de Janeiro: IBGE; 2018 [cited 2020 Aug 18]. Available from: [ Links ]

5. Secretaria de Estado da Saúde do Maranhão. Bol Epidemiol COVID-19. 20 ago 2020 [cited 2020 Aug 21]. Available from: ]

6. Randolph HE, Barreiro LB. Herd immunity: understanding COVID-19. Immunity. 2020;52(5):737-41. ]

7. Aguas R, Corder RM, King JG, Gonçalves G, Ferreira MU, Gomes MGM. Herd immunity thresholds for SARS-CoV-2 estimated from unfolding epidemics. medRxiv [Preprint]. 2020 [posted 2020 Jul 24]. ]

8. Britton T, Ball F, Trapman P. A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2. Science. 2020;369(6505):846-9. ]

9. Hallal PC, Hartwig FP, Horta BL, Victora GD, Silveira M, Struchiner C, et al. SARS-CoV-2 antibody prevalence in Brazil: results from two successive nationwide serological household surveys. Lancet Glob Heal. 2020;8(11):e1390–8. ]

10. Ioannidis J. The infection fatality rate of COVID-19 inferred from seroprevalence data. medRxiv [Preprint]. 2020 [posted 2020 Jul 14]. ]

11. Pollán M, Pérez-Gómez B, Pastor-Barriuso R, Oteo M, Hernán MA, Pérez-Olmeda M, et al. Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population-based seroepidemiological study. Lancet. 2020;396(10250):535-44. ]

12. Rosenberg ES, Tesoriero JM, Rosenthal EM, Chung R, Barranco MA, Styer LM, et al. Cumulative incidence and diagnosis of SARS-CoV-2 infection in New York. Ann Epidemiol. 2020;48:23-29.e4. ]

13. Tess BHC, Alves MCGP, Reinach F, Granato CFH, Rizzati EG, Pintão MC, et al. Inquérito domiciliar para monitorar a soroprevalência da infecção pelo vírus SARS-CoV-2 em adultos no município de São Paulo. São Paulo, SP: Projeto SoroEPI-MSP; 2020 [cited 2020 Aug 21]. Available from: ]

14. Buss LF, Prete Jr CA, Abrahim CMM, Mendrone Jr AM, Salomon T, Almeida-Neto C, et al. COVID-19 herd immunity in the Brazilian Amazon. medRxiv [Preprint]. 2020 [posted 2020 Sept 21]. ]

15. Russell TW, Hellewell J, Jarvis CI, Zandvoort K, Abbott S, Ratnayake R, et al. Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020. Eurosurveillance. 2020;25(12):2000256. ]

16. Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis. 2020;20(6):669-77. ]

17. Ing AJ, Cocks C, Green JP. COVID-19: in the footsteps of Ernest Shackleton. Thorax. 2020;75(8):693-4. ]

18. Meyerowitz-Katz G, Merone L. A systematic review and meta-analysis of published research data on COVID-19 infection-fatality rates. Int J Infect Dis. 2020;101:138–48. ]

19. Menezes AMB, Victora CG, Hartwig FP, Silveira MF, Horta BL, Barros AJD, et al. High prevalence of symptoms among Brazilian subjects with antibodies against 2 SARS-CoV-2: a nationwide household survey. medRxiv [Preprint]. 2020 [posted 2020 Aug 12]. ]

20. Byambasuren O, Cardona M, Bell K, Clark J, McLaws ML, Glasziou P. Estimating the extent of true asymptomatic COVID-19 and its potential for community transmission: systematic review and meta-analysis. medRxiv [Preprint]. 2020 [posted 2020 Sept 13] ]

21. Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science. 2020;368(6490):489-93. ]

22. UNESCO. The International Standard Classification of Education ISCED 2011. Vol 5. Québec (CA): UNESCO Institute for Statistics; 2012 [cited 2020 Aug 21]. Available from: ]

23. Travassos C, Williams DR. The concept and measurement of race and their relationship to public health: a review focused on Brazil and the United States. Cad Saude Publica. 2004;20(3):660-78. ]

24. Muench P, Jochum S, Wenderoth V, Ofenloch-Haehnle B, Hombach M, Strobl M, et al. Development and validation of the Elecsys Anti-SARS-CoV-2 Immunoassay as a highly specific tool for determining past exposure to SARS-CoV-2. J Clin Microbiol. 2020;58(10):e01694-20. ]

25. McGough SF, Johansson MA, Lipsitch M, Menzies NA. Nowcasting by Bayesian Smoothing: a flexible, generalizable model for real-time epidemic tracking. PLoS Comput Biol. 2020;16(4):e1007735. ]

26. Conselho Nacional de Secretários de Saúde. Painel de análise do excesso de mortalidade por causas naturais no Brasil em 2020. Brasília, DF: CONASS; 2020 [cited 2020 Sept 21]. Available from: ]

27. Marinho F, Torrens A, Teixeira R, França E, Nogales AM, Xavier D, et al. Excess mortality in Brazil: detailed description of trends in mortality during the COVID-19 pandemic. New York: Vital Strategies; 2020 [cited 2020 Aug 21]. Available from: ]

28. Pastor-Barriuso R, Pérez-Gómez B, Hernán MA, Pérez-Olmeda M, Yotti R, Oteo J, et al. SARS-CoV-2 infection fatality risk in a nationwide seroepidemiological study. medRxiv [Preprint]. 2020 [posted 2020 Aug 7]. ]

29. Health Information and Quality Authority. Evidence summary of the immune response following infection with SARS- CoV-2 or other human coronaviruses. Dublin (IRL): HIQA; 2020 [cited 2020 Aug 21]. Available from: ]

30. Sekine T, Perez-Potti A, Rivera-Ballesteros O, Stralin K, Gorin JB, Olsson A, et al. Robust T cell immunity in convalescent individuals with asymptomatic or mild COVID-19. Cell. 2020;183(1):158-68.e14. ]

31. Grifoni A, Weiskopf D, Ramirez SI, Mateus J, Dan JM, Moderbacher CR, et al. Targets of T Cell responses to SARS-CoV-2 Coronavirus in humans with COVID-19 disease and unexposed individuals. Cell. 2020;181(7):1489-1501.e15. ]

32. Long QX, Tang XJ, Shi QL, Deng HJ, Yuan J, HU JL, et al. Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections. Nat Med. 2020;26(8):1200-4. ]

33. Perkmann T, Perkmann-Nagele N, Breyer MK, Breyer-Kohansal-R, Bugghuber OC, Hartl S, et al. Side by side comparison of three fully automated SARS-CoV-2 antibody assays with a focus on specificity. Clin Chem. 2020 Aug 10;hvaa198. Epub ahead of print [ Links ]

34. Bastos ML, Tavaziva G, Abidi SK, Campbell JR, Haraoui LP, Johnston JC, et al. Diagnostic accuracy of serological tests for covid-19: systematic review and meta-analysis. BMJ. 2020;370:m2516. ]

35. Flower B, Brown JC, Simmons B, Moshe M, Frise R, Penn R, et al. Clinical and laboratory evaluation of SARS-CoV-2 lateral flow assays for use in a national COVID-19 seroprevalence survey. Thorax. 2020 Aug 12;thoraxjnl-2020-215732. Epub ahead of print. [ Links ]

36. Ward H, Atchison CJ, Whitaker M, Ainslie KEC, Elliott JE, Okell LC, et al. Antibody prevalence for SARS-CoV-2 in England following the first peak of the pandemic: REACT2 study in 100,000 adults. medRxiv [Preprint]. 2020 [posted 2020 Aug 21]. ]

37. Mahase E. Covid-19: two antibody tests are “highly specific” but vary in sensitivity, evaluations find. BMJ. 2020;369;m2066. ]

38. Muecksch F, Wise H, Batchelor B, Squires M, Semple E, Richardson C, et al. Longitudinal analysis of clinical serology assay performance and neutralising antibody levels in COVID19 convalescents. medRxiv [Preprint]. 2020 [posted 2020 Aug 6]. ]

39. Tess BH, Granato CFH, Alves MCGP, Pintão MC, Rizzatti E, Nunes MC, et al. SARS-CoV-2 seroprevalence in the municipality of São Paulo, Brazil, ten weeks after the first reported case. medRxiv [Preprint]. 2020 [posted 2020 June 29,]. ]

40. Chu DK, Akl EA, Duda S, Solo K, Yaacoub S, Schünemann HJ, et al. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet. 2020;395(10242):1973-87. ]

41. Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020;584(7820):257-61. ]

42. Walker PGT, Whittaker C, Watson OJ, Baguelin M, Winskill P, Hamlet A, et al. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science. 2020;369(6502):413-22. ]

43. Kenyon C. COVID-19 infection fatality rate associated with incidence: a population-level analysis of 19 Spanish autonomous communities. Biology (Basel). 2020;9(6):128. ]

Received: October 23, 2020; Accepted: October 27, 2020

Correspondence: Antônio Augusto Moura da Silva, Universidade Federal do Maranhão Departamento de Saúde Pública Rua Barão de Itapary, 155 65020-070 São Luís, MA, Brasil. E-mail:

Conflict of Interest:

The authors declare no conflict of interest.

Authors’ Contribution:

Conception and design of the work, acquisition, analysis, and interpretation of the data, and writing of the manuscript: AAMS, LGLN, CMPSA, LMC, MLBMB, AKDBF, BBW, BLCAO, CAC, EBAFT, EASN, JFLJ, MAGC, RCSQ, VAC, VMFS, MTSBA and MAS. Acquisition and analysis of the data: BFS, SSC and LMSC. Revised and approved the final version of the article, and take public responsibility for its content: AAMS, LGLN, CMPSA, LMC, MLBMB, AKDBF, BBW, BLCAO, CAC, EBAFT, EASN, JFLJ, BFS, SSC, LMSC, MAGC, RCSQ, VAC, VMFS, MTSBA and MAS.

Creative Commons License This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.