Skip to main content

Detection of IgG antibodies against the receptor binding domain of the spike protein and nucleocapsid of SARS-CoV-2 at university students from Southern Mexico: a cross-sectional study



Natural infection and vaccination against SARS-CoV-2 is associated with the development of immunity against the structural proteins of the virus. Specifically, the two most immunogenic are the S (spike) and N (nucleocapsid) proteins. Seroprevalence studies performed in university students provide information to estimate the number of infected patients (symptomatic or asymptomatic) and generate knowledge about the viral spread, vaccine efficacy, and epidemiological control. Which, the aim of this study was to evaluate IgG antibodies against the S and N proteins of SARS-CoV-2 at university students from Southern Mexico.


A total of 1418 serum samples were collected from eighteen work centers of the Autonomous University of Guerrero. Antibodies were detected by Indirect ELISA using as antigen peptides derived from the S and N proteins.


We reported a total seroprevalence of 39.9% anti-S/N (positive to both antigens), 14.1% anti-S and 0.5% anti-N. The highest seroprevalence was reported in the work centers from Costa Grande, Acapulco and Centro. Seroprevalence was associated with age, COVID-19, contact with infected patients, and vaccination.


University students could play an essential role in disseminating SARS-CoV-2. We reported a seroprevalence of 54.5% against the S and N proteins, which could be due to the high population rate and cultural resistance to safety measures against COVID-19 in the different regions of the state.

Peer Review reports


Coronavirus disease (COVID-19) is associated with the infection by the SARS-CoV-2. Initially, it was detected as an atypical viral pneumonia in Wuhan, China, in December of 2019 [1, 2]. The clinical symptoms of COVID-19 include cough, fever, myalgia, fatigue, diarrhea, and nauseas, among others [3, 4], it can also could evolve into severe pneumonia, dyspnea, multiple organ dysfunction, and the death of the patient [5]. SARS-CoV-2 is a positive-sense single-stranded RNA virus (+ ssRNA) of ~ 30 kb [6] that contains fourteen ORF (open reading frames) that encodes for nine accessory proteins, sixteen non-structural proteins (nsp 1–16), and four structural proteins: spike (S), envelope (E), membrane (M) and Nucleocapsid (N) [7, 8]. The S protein has 1273 amino acids length and a molecular weight of 180 kDa, contains two subunits (S1 and S2), and its primary function is the binding to the cellular receptor Angiotensin-converting enzyme 2 (ACE2) by the Receptor Binding Domain (RBD) [9]. Due to this function, the S protein is considered the main target during the design and development of vaccines. Meanwhile, the N protein has a 419 amino acids length and a molecular weight of around 45 kDa, is associated with viral replication and transcription, and maintains the ribonucleoprotein complex (RNP) [10]. Both antigens have been identified as highly immunogenic [11, 12], and their role in the host immune response has been tested by searching for IgG antibodies [13].

During the early stages of the disease, it has been proved that detecting antibodies against the N protein is more sensitive than anti-S [11]. The presence of IgM and IgG antibodies has been used to diagnose and confirm late cases of COVID-19, or to evaluate collective immunity [14], It provides evidence about natural infection due to virus spread or vaccine’s efficacy around the world, to establish new health policies and for epidemiological control [15]. The prevalence of IgG antibodies varies according to the elapsed time, country, and analyzed population [16]; during 2020, the reported prevalence was around 3.8–13.1% [17,18,19,20]. One study in India reported a seroprevalence of 20.7% and 69.2% during the first and second wave of COVID-19, respectively [21]. In Mexico, a seroprevalence of 21.3% was reported in asymptomatic patients [22], whereas in serum samples collected from February to December 2020, the seroprevalence was 33.5% [23].

In University students, a prevalence of around 4.0–4.7% was reported during 2020 in the United States [24,25,26], which increased to 46.8% in 2021, according to a study performed in the University of North Carolina [27]. Whereas in Brazil, the seroprevalence was 22.5% [28], in Mexico, one study reported a seroprevalence of 18.7% and 26.7%, in kids and teenagers, respectively [29]. Which it is necessary to perform studies focused on evaluating the seroprevalence of IgG antibodies against the S and N proteins of SARS-CoV-2 in university students to understand the immunization degree, provide information about the dynamic of the disease and associated factors, which was the main objective of this study.

Materials and methods

Study design

We developed a cross-sectional study that included a total of 1418 participants who were randomly selected from eighteen educative centers of the Universidad Autónoma de Guerrero located in six regions of the state of Guerrero (Supplementary material 1). The state of Guerrero has a territorial extension of 63,794 km2 and is located in the Southern region of Mexico (southern coordinates over the Pacific Ocean between the 16°18’57.60"N to 18°53’16.08"N of north latitude and 102°11’02.40"W 98°00’26.28"W of west longitude) (Supplementary material 2).

Sample collection and survey

Sample collection was performed in each educational center from November 2022 until May 2023. A blood sample was collected in a tube without anticoagulant by venipuncture; later, the tube was centrifuged at 3500 rpm for 10 min. The serum sample was placed into a new tube and stored at -20 °C. Clinical and health data was collected by a survey that included personal (name, age, and contact number) and health data (COVID-19, number of past infections, symptoms, hospitalization, previous vaccination, number of doses, and chronic diseases).

Indirect ELISA for IgG anti-S and anti-N

For antibody detection against the Spike protein of SARS-CoV-2, we used as antigen five peptides located in the RBD S1 domain of S protein and one peptide in the N protein; both antigens were synthesized as multi-antigenic peptides of eight ramifications (MAP8) as previously reported by our workgroup [30]. A clinical sensitivity of 92% and a specificity of 96% was obtained for both antigens, which is also depicted by the corresponding receiver operating characteristic (ROC) curve obtaining an area under the curve of 0.944. The ROC curve was calculated with 95% confidence intervals (0.89 to 0.99).

First, microtiter plates (Sigma-Aldrich) were coated with 100 µL/well of each antigen to a final concentration of 0.1 µg/mL in a coating buffer (50 mM Na2CO3/NaCO3H, pH 9.6). The plates were incubated for 1 h at 37 ºC and then blocked for 30 min at 37 ºC with 200 µL/well of 5% skimmed milk diluted in phosphate-buffered saline (PBS)-Tween 20 (0.05%). For the primary antibody, 100 µL/well of serum samples (1:50 dilution) were incubated by a duplicate for 45 min at 37 ºC. Later, 100 µL/well of mouse monoclonal anti-human IgG (Sigma-Aldrich; dilution 1:1500) was added for 45 min and incubated at 37 °C. After every step, the plates were washed thrice with 200 µL/well of PBS-Tween 20 0.05% for 5 min. The enzymatic reaction was developed using o-phenylenediamine dihydrochloride (Sigma-Aldrich) and stopped with 2 N H2SO4. The optical density (OD) was measured at 492 nm using a microplate reader. Samples with an OD > 0.250 were considered positive for both antigens.

Statistical analysis

The analysis relied on the statistical package CIETmap 2.1, a Windows interface for the R programming language [31]. We performed a univariate analysis and obtained descriptive results and simple frequencies. We examined descriptive data as frequency of each analyzed variable and measures of central tendency. The association between the presence of antibodies against the S and N proteins and analyzed variables was determined by bivariate and multivariate analysis, using the Mantel-Haenszel procedure [32]. Multivariate analysis began with a saturated model that included all the statistically significant variables associated with the outcome in bivariate analysis, removing the less significant associations one by one until only associated variables with the outcome at the 95% confidence level remained. We reported associations as odds ratios (OR) with 95% confidence intervals (95% CI) were calculated by the Miettinen method [33], and a significant P value < 0.05 was considered statistically significant. The territorial map of the state of Guerrero was performed in the software ArcMap V.10.8, used as a template for online maps. Later, by using Google Earth, we added the UTM coordinates of each center of the Universidad Autónoma de Guerrero, in which sampling was performed.


We surveyed 1418 college students with a mean age of 20.5 years (range 18–29). 67% were women, of which 2.3% were pregnant. The 45.3% had COVID-19, and the main symptoms were fever (78.9%), cough (67%), and sore throat (66.9%). Also, 98.7% received the complete vaccination scheme (two doses for almost all the included vaccines with the exception of CanSino and Johnson & Johnson) and 77.1% had a heterologous dose (Table 1). The vaccines applied in the second and booster doses are shown in supplementary material 3.

Table 1 Symptoms, comorbidities, COVID-19 infection and vaccination status

We found a global seroprevalence of 14.1% for anti-S, 0.5% for anti-N, and 39.9% for both antigens (anti-S and anti-N). The highest seroprevalence for anti-S was the region of Costa Chica (18.7%) and Tierra Caliente (16.9%), while, for anti-S and anti-N was Costa Grande (57.7%) and Acapulco (51.8%), significant differences were observed between the reported prevalence among regions (Table 2).

Table 2 Seroprevalence of antibodies against proteins S and N in the six regions studied

Subsequently, we performed a bivariate test in which we showed that the presence of IgG anti-S was associated with age 18–23 years old, COVID-19 (at least once), contact with COVID-19 patients, and the complete vaccination scheme. For IgG anti-N, we find similar associations except direct contact with COVID-19 patients (Table 3).

Table 3 Bivariate analysis of the risk factors with the presence of IgG Anti-S and Anti-N antibodies

Finally, the multivariate test was refined and used to confirm that the age between 18 and 23 years (ORa 2.39), contact with COVID-19 patients (ORa 1.35), and complete vaccination scheme (ORa 1.58) were associated with the presence of IgG anti-S. Whereas the age between 18 and 23 years (ORa 2.08) and the complete vaccination scheme (ORa 1.90) were related to the presence of IgG anti-N in the analyzed population (Table 4).

Table 4 Adjusted multivariate analysis of the risk factors with the presence of IgG Anti-S and Anti-N antibodies in the analyzed population


Since the first case of COVID-19, a total of 770, 778, 396 cases and 6,958,499 deaths have been reported until September of 2023 [34]. This disease is associated with the infection by SARS-CoV-2 and has symptoms such as cough, fever, myalgia, fatigue, asthenia, and headache [35]. All populations are sensitive to the infection by SARS-CoV-2 [36]; however, during the first months of the pandemic, higher incidences were reported in third age patients; later, several studies provided information about a higher prevalence in the younger population during 2020 [37]. Seroprevalence studies in youth populations provide information about the number of asymptomatic cases [38] or estimations about the course of the disease [39] in those who live and attend crowded places such as universities in which they can acquire and disseminate the virus [40, 41].

In this study, we aimed to analyze the prevalence of IgG antibodies against the S and N proteins of SARS-CoV-2 in eighteen centers of the Autonomous University of Guerrero. Patients had between 18 and 29 years old, and around 36.9% reported having at least one comorbidity as obesity (23.1%), asthma (6.3%), EPOC (1.7%), hypertension (1%), diabetics (0.6%), rheumatoid arthritis (0.2%), cancer (0.2%), and lupus (0.1%). These comorbidities predispose to develop severe disease or hospitalization [42, 43]. Hypertension, diabetes, and obesity are predictors of worst prognostic due to the endothelial damage of patients, oxidative process, and cellular/tissue inflammation. Also, obesity and asthma generate hypoventilation states that are considered bad prognostic in COVID-19 [44], while autoimmune diseases, such as arthritis and lupus, generate immunosuppression states and promote susceptibility to infections by microorganisms as SARS-CoV-2 [45, 46].

Vaccination against COVID-19 is safe and necessary to prevent the infection by SARS-CoV-2. In our population, 98.7% applied at least one dose of the vaccines Pfizer, Sinovac, AstraZeneca, and CanSino. Those vaccines are based on mRNA, complete virus, and viral vectors, respectively. Previous studies reported that antibody levels after applying mRNA vaccines are higher than those generated by natural infection. In contrast, antibody levels generated by the application of viral vector are equal to those generated by natural infection [47, 48]. Also, it has been reported that the aforementioned vaccines promote cellular and humoral immune response, associated with the production of IgA, IgM, and IgG, and the development of memory T and B cells against epitopes derived from S protein or the RBD of SARS-CoV-2 [49, 50]. It has been reported that heterologous boost induces a higher concentration of neutralizing antibodies in comparison to a homologous boost [51, 52]. In this study, most of the patients received heterologous doses, and the most administered vaccine was AstraZeneca (42%), which has been proven to increase the humoral immune response [53].

Seroprevalence studies evaluate the number of patients that were positive for the infection or vaccinated against the SARS-CoV-2 [54]. Antibody detection generates knowledge about past infection, transmissibility, response, and efficacy of vaccination [15] to establish health politicizes and epidemiological control [19]. In this study, we reported a total seroprevalence of 54.5%, in which, 14.1% corresponds precisely to IgG anti-S protein. Differences between the seroprevalence of both proteins could be due to S-protein being the main target antigen used to design and develop vaccines [9, 55]. Whereas antibodies against the N-protein are related to the early phases of the infection [11]. Similar studies performed on students have reported contrasting results. In the U.S.A. during 2020, the seroprevalence was 4.0-4.7% [24,25,26], while during 2021, the reported seroprevalence was from 22.5 to 46.8% [27, 28]. Antibody levels vary according to region and time [54]. We observed changes in the seroprevalence according to the geographic regions of the state; the highest rate was reported in Acapulco, Centro, and Costa Grande. These differences could be attributed to factors such as: cultural practices, mitigation efforts, health infrastructure, political decisions for the prevention and control of COVID-19, and the population flow of each region [56, 57]. Previous studies reported a diminution of the incidence of COVID-19 in zones with trim or without universities, compared with the prevalence reported in universities with high enrollment, due to constant socialization and behaviors promoting development of the disease [58]. Reported seroprevalences are due to the high grade of immunization in our population; this could affect the infectious rate and have a positive effect on the diminution of the number of infected patients, which is commonly denominated as community immunity [59, 60].

Finally, we associated analyzed factors with the production of antibodies. We reported a positive relation between age, COVID-19, contact with a COVID-19 patient, and vaccination with IgG against S and N proteins of SARS-CoV-2. Age is directly related to a progressive reduction in the ability of the immune system to trigger effective cellular and humoral responses against the infection or vaccines [61]. While COVID-19 and contact with COVID-19 patients promote antibody production through direct contact with the virus and the induction of innate and adaptive immune response that confers long-term protection; particularly, antibodies can be detected days after the start of the symptoms and are detectable several months after the infection [62]. Vaccines induce the production of IgM, IgA, and IgG during the first twelve days; these antibodies can neutralize and reduce the transmission of the disease [63, 64]. After vaccine application, the kinetics of antibody production is around three weeks and declines after that time [65, 66], and cellular immune response declines around four and fourteen weeks after the boost in mRNA vaccines [67]. In this study, the participants had an elapsed time of around six months since the last boost, which could explain the negative patients to IgG antibodies in vaccinated students.

Further investigations need to be performed in order to analyze the presence of IgG antibodies against the other structural proteins of SAR-CoV-2 (E and M) or evaluate the neutralizing capacity of IgG antibodies in order to have complete epidemiological data about the vaccination and natural infection by SARS-CoV-2 in university students. However, our results provide evidence of the high seroprevalence of anti-S and anti-N antibodies, which can promote collective immunity in this population.


We reported a seroprevalence of 54.5% against the S and N proteins. The highest seroprevalence of IgG antibodies was reported in Acapulco and Costa Chica, which could be due to the high population rate and cultural resistance to safety measures against COVID-19. Also, this seroprevalence reflects the high vaccination coverage and natural infection by SARS-CoV-2. Evaluating IgG antibodies against the S and N proteins is fundamental to understanding the impact and deceleration of the pandemic in university students in Guerrero.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.



Coronavirus disease 2019


Severe acute respiratory syndrome-coronavirus-2


Receptor binding domain


Angiotensin-converting enzyme 2


Positive-sense single-stranded RNA virus


Open reading frames; nsp: non-structural proteins


Ribonucleoprotein complex


Enzyme-linked immunosorbent assay


Multiantigenic peptides of eight ramifications


  1. Wu A, Peng Y, Huang B, Xiao D, Xianyue W, Peihua N, et al. Genome composition and divergence of the Novel Coronavirus (2019-nCoV) originating in China. Cell Host Amp Microbe. 2020;27:325–8.

    Article  CAS  Google Scholar 

  2. Guo YR, Cao QD, Hong ZS, Tan YY, Chen SD, Jin HJ et al. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak – an update on the status. Mil Med Res. 2020;7.

  3. Tsai SC, Lu CC, Bau DT, Chiu YJ, Yen YT, Hsu YM, et al. Approaches towards fighting the COVID 19 pandemic (review). Int J Mol Med. 2020;47:3–22.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Hernandez Acosta RA, Garrigos ZE, Marcelin JR, Vijayvargiya P. COVID-19: Pathogenesis and clinical manifestations. Infect Dis Clin North Am. 2022;36:231–49.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Li G, Fan Y, Lai Y, Han T, Li Z, Zhou P, et al. Coronavirus infections and immune responses. J Med Virol. 2020;92:424–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kim D, Lee JY, Yang JS, Kim JW, Kim VN, Chang H. The Architecture of SARS-CoV-2 Transcriptome. Cell. 2020;18:914–21.

    Article  Google Scholar 

  7. Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet. 2020;395:565–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bai C, Zhong Q, Gao GF. Overview of SARS-CoV-2 genome-encoded proteins. Sci China Life Sci. 2022;65(2):280–94.

    Article  CAS  PubMed  Google Scholar 

  9. Astuti I. Ysrafil. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): an overview of viral structure and host response. Diabetes Amp Metab Syndr. 2020;14:407–12.

    Article  Google Scholar 

  10. Kadam SB, Sukhramani GS, Bishnoi P, Pable AA, Barvkar VT. SARS-CoV‐2, the pandemic coronavirus: Molecular and structural insights. J Basic Microbiol. 2021;61:180–202.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Burbelo PD, Riedo FX, Morishima C, Rawlings S, Smith D, Das S, et al. Sensitivity in detection of antibodies to Nucleocapsid and Spike proteins of severe Acute Respiratory Syndrome Coronavirus 2 in patients with Coronavirus Disease 2019. J Infect Dis. 2020;222:206–13.

    Article  CAS  PubMed  Google Scholar 

  12. Sun B, Feng Y, Mo X, Zheng P, Wang Q, Li P, et al. Kinetics of SARS-CoV-2 specific IgM and IgG responses in COVID-19 patients. Emerg Microbes Amp Infect. 2020;9:940–8.

    Article  CAS  Google Scholar 

  13. To KK, Tsang OT, Leung WS, Tam AR, Wu TC, Lung DC, et al. Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study. Lancet Infect Dis. 2020;20:565–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Tang YW, Schmitz JE, Persing DH, Stratton CW. Laboratory diagnosis of COVID-19: Current issues and challenges. J Clin Microbiol. 2020;58:e00512–20.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Deeks JJ, Dinnes J, Takwoingi Y, Davenport C, Spijker R, Taylor-Phillips S, et al. Antibody tests for identification of current and past infection with SARS-CoV-2. Cochrane Database Syst Rev. 2020;25:CD013652.

    Google Scholar 

  16. Aballay LR, Coquet JB, Scruzzi GF, Haluszka E, Franchini G, Raboy E et al. Estudio De base poblacional de seroprevalencia y factores asociados a la infección por SARS-CoV-2 en Córdoba, Argentina. Cad Saude Publica. 2022;38.

  17. Xu X, Sun J, Nie S, Li H, Kong Y, Liang M, et al. Seroprevalence of immunoglobulin M and G antibodies against SARS-CoV-2 in China. Nat Med. 2020;26:1193–5.

    Article  CAS  PubMed  Google Scholar 

  18. Noh JY, Seo YB, Yoon JG, Seong H, Hyun H, Lee J, et al. Seroprevalence of Anti-SARS-CoV-2 antibodies among outpatients in Southwestern Seoul, Korea. J Korean Med Sci. 2020;35:e311.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  20. Silveira MF, Barros AJD, Horta BL, Pellanda LC, Victora GD, Dellagostin OA, et al. Population-based surveys of antibodies against SARS-CoV-2 in Southern Brazil. Nat Med. 2020;26:1196–9.

    Article  CAS  PubMed  Google Scholar 

  21. Jahan N, Brahma A, Kumar MS, Bagepally BS, Ponnaiah M, Bhatnagar T, et al. Seroprevalence of IgG antibodies against SARS-CoV-2 in India, March 2020 to August 2021: a systematic review and meta-analysis. Int J Infect Dis. 2022;116:59–67.

    Article  CAS  PubMed  Google Scholar 

  22. Remes-Troche JM, Ramos-de-la-Medina A, Manríquez-Reyes M, Martínez-Pérez-Maldonado L, Lara EL, Solís-González MA. Initial gastrointestinal manifestations in patients with severe Acute Respiratory Syndrome Coronavirus 2 infection in 112 patients from Veracruz in Southeastern Mexico. Gastroenterology. 2020;159:1179–81.

    Article  CAS  PubMed  Google Scholar 

  23. Muñoz-Medina JE, Grajales-Muñiz C, Salas-Lais AG, Fernandes-Matano L, López-Macías C, Monroy-Muñoz IE, et al. SARS-CoV-2 IgG antibodies Seroprevalence and Sera Neutralizing Activity in MEXICO: A National Cross-sectional Study during 2020. Microorganisms. 2021;9:850.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Tilley K, Ayvazyan V, Martinez L, Nanda N, Kawaguchi ES, O’Gorman M, et al. A cross-sectional study examining the seroprevalence of severe Acute Respiratory Syndrome Coronavirus 2 antibodies in a University Student Population. J Adolesc Health. 2020;67:763–8.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kianersi S, Ludema C, Macy JT, Garcia-Colato E, Chen C, Luetke M, et al. A cross-sectional analysis of demographic and behavioral risk factors of severe Acute Respiratory Syndrome Coronavirus 2 Seropositivity among a sample of U.S. College Students. J Adolesc Health. 2021;69:219–26.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Bullis SS, Grebber B, Cook S, Graham NR, Crmolli M, Dickson D, et al. SARS CoV-2 seroprevalence in a US school district during COVID-19. BMJ Paediatr Open. 2021;5:e001259.

    Article  PubMed  Google Scholar 

  27. Diepstra K, Bullington BW, Premkumar L, Shook-Sa BE, Jones C, Pettifor A. SARS-CoV-2 seroprevalence: demographic and behavioral factors associated with seropositivity among college students in a university setting. J Adolesc Health. 2022;71:559–69.

    Article  PubMed  PubMed Central  Google Scholar 

  28. de Souza Araújo AA, Quintans-Júnior LJ, Heimfarth L, Schimieguel DM, Corrêa CB, de Moura TR et al. Seroprevalence of SARS-CoV-2 antibodies in the poorest region of Brazil: results from a population-based study. Epidemiol Infect. 2021;149.

  29. Canto-Osorio F, Stern D, Pérez-Ferrer C, et al. Seroprevalencia Del SARS-CoV-2 en niños y adolescentes mexicanos en edad de educación primaria y secundaria. Salud pública De México. 2021;63:803–6.

    Article  PubMed  Google Scholar 

  30. Cortés-Sarabia K, Cruz-Rangel A, Flores-Alanis A, Salazar-García M, Jiménez-García S, Rodríguez-Martínez G, et al. Clinical features and severe acute respiratory syndrome-coronavirus-2 structural protein-based serology of Mexican children and adolescents with coronavirus disease 2019. PLoS ONE. 2022;17:e0273097.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Andersson N, Mitchell S, CIETmap. Free GIS and epidemiology software from the CIET group, helping to build the community voice into planning. In World Congress of Epidemiology. Montreal, Canada, August 2002.

  32. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22:719–48.

    CAS  PubMed  Google Scholar 

  33. Miettinen OS. Simple interval estimation of the risk ratio. Am J Epidemiol. 1974;100:515–6.

    Google Scholar 

  34. Word Health Organization Coronavirus (COVID-19). Dashboard. WHO Coronavirus (COVID-19) Dashboard | WHO Coronavirus (COVID-19) Dashboard With Vaccination Data. Available: Accessed 13 de oct 2023.

  35. Koupaei M, Mohamadi MH, Yashmi I, Shahabi AH, Shabani AH, Heidary M, et al. Clinical manifestations, treatment options, and comorbidities in COVID-19 relapse patients: a systematic review. J Clin Lab Anal. 2022;36(5):e24402.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical characteristics of Coronavirus Disease 2019 in China. New Engl J Med. 2020;382:1708–20.

    Article  CAS  PubMed  Google Scholar 

  37. Leidman E, Duca LM, Omura JD, Proia K, Stephens JW, Sauber-Schatz EK. COVID-19 trends among persons aged 0–24 years — United States, March 1–December 12, 2020. MMWR Morb Mortal Wkly Rep. 2021;70:88–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Davies NG, Klepac P, Liu Y, Prem K, Jit M, CMMID COVID-19 working group. Age-dependent effects in the transmission and control of COVID-19 epidemics. Nat Med. 2020;26:1205–11.

    Article  CAS  PubMed  Google Scholar 

  39. Björkander S, Du L, Zuo F, Ekström S, Wang Y, Wan H, et al. SARS-CoV-2–specific B- and T-cell immunity in a population-based study of young Swedish adults. J Allergy Clin Immunol. 2022;149:65–e758.

    Article  PubMed  Google Scholar 

  40. Salvatore PP, Sula E, Coyle JP, Caruso E, Smith AR, Levine RS, et al. Recent increase in COVID-19 cases reported among adults aged 18–22 years — United States, May 31–September 5, 2020. MMWR Morb Mortal Wkly Rep. 2020;69:1419–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Walke HT, Honein MA, Redfield RR. Preventing and responding to COVID-19 on College campuses. JAMA. 2020;324:1727–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Havers FP, Whitaker M, Self JL, Chai SJ, Kirley PD, Alden NB, et al. Hospitalization of adolescents aged 12–17 years with Laboratory-confirmed COVID-19 — COVID-NET, 14 States, March 1, 2020–April 24, 2021. MMWR Morb Mortal Wkly Rep. 2021;70:851–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Kest H, Kaushik A, Shaheen S, Debruin W, Zaveri S, Colletti M, et al. Epidemiologic characteristics of adolescents with COVID-19 disease with Acute Hypoxemic Respiratory failure. Crit Care Res Pract. 2022;2022:1–5.

    Article  Google Scholar 

  44. Pena-Garcia Y, Suarez-Padilla A, Arreuebarrena-Blanco N. Characterization of positive and suspected COVID-19 cases with comorbidities. Rev Finlay. 2020;10:314–9.

    Google Scholar 

  45. Orozco BJ, Imbachí SA, Ospina AI. Manifestaciones pulmonares de la artritis reumatoide, una revisión en tiempos de pandemia por SARS-CoV-2. Rev Colomb Reumatol. 2021;29:S56–65.

    Google Scholar 

  46. Thanou A, Sawalha AH. SARS-CoV-2 and systemic Lupus Erythematosus. Curr Rheumatol Rep. 2021;28:8.

    Article  Google Scholar 

  47. Manisty C, Otter AD, Treibel TA, McKnight Á, Altmann DM, Brooks T, et al. Antibody response to first BNT162b2 dose in previously SARS-CoV-2-infected individuals. Lancet. 2021;397:1057–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Reynolds CJ, Pade C, Gibbons JM, Butler DK, Otter AD, Menacho K, et al. Prior SARS-CoV-2 infection rescues B and T cell responses to variants after first vaccine dose. Science. 2021;372:1418–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Abufares HI, Oyoun Alsoud L, Alqudah MAY, Shara M, Soares NC, Alzoubi KH, et al. COVID-19 vaccines, effectiveness, and Immune responses. Int J Mol Sci. 2022;23:15415.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Costa PR, Correia CA, Marmorato MP, Dias JZC, Thomazella MV, Cabral da Silva A, et al. Humoral and cellular immune responses to CoronaVac up to one year after vaccination. Front Immunol. 2022;13:1032411.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Costa Clemens SA, Weckx L, Clemens R, Almeida Mendes AV, Ramos Souza A, Silveira MBV, et al. Heterologous versus homologous COVID-19 booster vaccination in previous recipients of two doses of CoronaVac COVID-19 vaccine in Brazil (RHH-001): a phase 4, non-inferiority, single blind, randomised study. Lancet. 2022;399:521–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Altmann DM, Boyton RJ. COVID-19 vaccination: the road ahead. Science. 2022;375:1127–32.

    Article  CAS  PubMed  Google Scholar 

  53. Munro AP, Janani L, Cornelius V, Aley PK, Babbbage G, Baxter D, et al. Safety and immunogenicity of seven COVID-19 vaccines as a third dose (booster) following two doses of ChAdOx1 nCov-19 or BNT162b2 in the UK (COV-BOOST): a blinded, multicentre, randomised, controlled, phase 2 trial. Lancet. 2021;398:2258–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Song SK, Lee DH, Nam JH, Kim KT, Do JS, Kang DW, et al. IgG seroprevalence of COVID-19 among individuals without a history of the Coronavirus Disease infection in Daegu, Korea. J Korean Med Sci. 2020;35:e269.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Dai L, Gao GF. Viral targets for vaccines against COVID-19. Nat Rev Immunol. 2020;21:73–8.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Hensel J, McAndrews KM, McGrail DJ, Dowlatshahi DP, LeBleu VS, Kalluri R. Protection against SARS-CoV-2 by BCG vaccination is not supported by epidemiological analyses. Sci Rep. 2020;10:18377.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Anderson RM, Heesterbeek H, Klinkenberg D, Hollingsworth TD. How will country-based mitigation measures influence the course of the COVID-19 epidemic? Lancet. 2020;395:931–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Leidner AJ, Barry V, Bowen VB, Silver R, Musiel T, Kang G, et al. Opening of large institutions of Higher Education and County-Level COVID-19 incidence — United States, July 6–September 17, 2020. MMWR Morb Mortal Wkly Rep. 2021;70:14–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Lima I, Balbi PP. Estimates of the collective immunity to COVID-19 derived from a stochastic cellular automaton based framework. Nat Comput. 2022;21:449–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Randolph HE, Barreiro LB. Herd immunity: understanding COVID-19. Immunity. 2020;52:737–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Fulop T, Witkowski JM, Olivieri F, Larbi A. The integration of inflammaging in age-related diseases. Semin Immunol. 2018;40:17–35.

    Article  CAS  PubMed  Google Scholar 

  62. Chvatal-Medina M, Mendez-Cortina Y, Patiño PJ, Velilla PA, Rugeles MT. Antibody responses in COVID-19: a review. Front Immunol. 2021;12:633184.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kashte S, Gulbake A, El-Amin Iii SF, Gupta A. COVID-19 vaccines: rapid development, implications, challenges and future prospects. Hum Cell. 2021;34:711–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Tregoning JS, Flight KE, Higham SL, Wang Z, Pierce BF, et al. Progress of the COVID-19 vaccine effort: viruses, vaccines and variants versus efficacy, effectiveness and escape. Nat Rev Immunol. 2021;21:626–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Gilboa M, Regev-Yochay G, Mandelboim M, Indenbaum V, Asraf K, Fluss R, et al. Durability of Immune Response after COVID-19 Booster Vaccination and Association with COVID-19 Omicron infection. JAMA Netw Open. 2022;5:e2231778.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Bingula R, Chabrolles H, Bonnet B, Archimbaud C, Brebion A, Cosme J. el al. Increase over time of antibody levels 3 months after a booster dose as an indication of better protection against Omicron infection. Emerg Microbes Infect. 2023; 12:2184176.

  67. Liu X, Munro APS, Feng S, Janani L, Aley PK, Babbage G. at al. Persistence of immunogenicity after seven COVID-19 vaccines given as third dose boosters following two doses of ChAdOx1 nCov-19 or BNT162b2 in the UK: Three month analyses of the COV-BOOST trial. J Infect. 2022; 84:795–813.

Download references


We are very grateful to the members of the LIDM for their technical support: Omar Velázquez-Moreno, Kenet Palomares-Monterrubio, Mario Campos-Ruiz, Edgar Hurtado-Ortega, Karla Martínez-Pacheco, Vianey Guzmán-Silva and Alberto Meza-Hernández.


This research received no external funding.

Author information

Authors and Affiliations



Conceptualization, J.A.B-C., K.C.-S., O.D.M.-H. and B.I.-A.; methodology, J.A.B-C., K.C.-S., U.J-B.; resources, M.A.L-V., B.M.S.G., B.I.A., and O.D.M.-H.; data analysis, J. L-S., B.M.S.G., and V.M.A-C, writing original draft preparation, J.A. B-C., K.C.-S., A.V.V.; writing—review and editing, M.A.L.-V, B.I.A, J.L-S. A.

Corresponding authors

Correspondence to Oscar Del Moral-Hernández or Berenice Illades-Aguiar.

Ethics declarations

Ethics approval and consent to participate

This is a cross-sectional study with human subjects, before sample collection, all participants included in this study signed an informant consent and results were treated as confidential. This study was approved by the ethics committee of the Autonomous University of Guerrero (CB-004/22).

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bailón-Cuenca, J.A., Cortés-Sarabia, K., Legorreta-Soberanis, J. et al. Detection of IgG antibodies against the receptor binding domain of the spike protein and nucleocapsid of SARS-CoV-2 at university students from Southern Mexico: a cross-sectional study. BMC Infect Dis 24, 584 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: