Abstract
Objective: To analyze regional trends in the incidence of 11 symptom categories among patients hospitalized with Severe Acute Respiratory Syndrome (SARS) due to COVID-19, stratified by sex and age group, across six epidemic waves in Brazil (2020–2022). Methods: This ecological time-series study used anonymized secondary data from the Brazilian Influenza Epidemiological Surveillance Information System (SIVEP-Gripe). Symptoms were classified into 11 categories and analyzed by geographic region. Generalized Additive Models (GAMs) with a logit link function were fitted using penalized splines and restricted maximum likelihood (REML) estimation in RStudio. Results: A declining trend in thermoregulatory symptoms was observed at the end of the first epidemic wave across all regions. The Northeast showed the highest trend in respiratory symptoms, whereas the North exhibited the lowest trend through the fifth wave. In the Central-West, digestive symptoms predominated, while the Southeast showed a higher trend in severe weakness among individuals aged over 35 years. In the South, children and older adults presented lower proportions of pain-related symptoms. During the second epidemic wave, a marked reduction in several symptom categories was observed, possibly reflecting the expansion of COVID-19 vaccination. Conclusion: The findings demonstrate a heterogeneous temporal pattern of COVID-19 in Brazil, influenced by regional and age-related differences, highlighting the combined effects of SARS-CoV-2 variants and vaccination coverage on the national symptom profile.
References
ALLAIRE JJ. RStudio: Integrated Development Environment for R. 2012. Disponível em: https://rstudio.com/.
ALMEIDA PD, et al. Análise espaço-temporal da covid-19 em um estado brasileiro. Revista Baiana de Enfermagem, 2021; 35.
AMADO LA, et al. Clinical Profile and Risk Factors for Severe COVID-19 in Hospitalized Patients from Rio de Janeiro, Brazil: Comparison between the First and Second Pandemic Waves. Journal of Clinical Medicine, 2023; 12(7): 2568.
BRASIL. Ministério da Saúde. Boletim Observatório COVID-19. Balanço de dois anos de pandemia Covid-19: janeiro de 2020 a janeiro de 2022. Rio de Janeiro: Fiocruz, 2022.
BRASIL. Ministério da Saúde. Covid-19. 2025. Disponível em: https://www.gov.br/saude/ptbr/assuntos/saude-de-a-a-z/c/covid-19/covid-19. Acessado em: 6 mar. 2025.
BRASIL. Ministério da Saúde. Doença pelo Novo Coronavírus – COVID-19. Semana Epidemiológica 52. Brasília, DF: Ministério da Saúde, 2022.
CÂMARA AJA, et al. Generalized additive model for count time series: an application to quantify the impact of air pollutants on human health. Pesquisa Operacional, 2021; 41: e241120.
CARVALHO GHA, et al. Tendência temporal dos casos confirmados de COVID-19 em crianças, adolescentes e adultos de São Carlos - SP entre 2020 e 2021: um estudo de série temporal. Revista de Medicina, 2024; 103(6): e222591.
DADOS ABERTOS. Portal de Dados Abertos. [s. d.]. Disponível em: https://dados.gov.br/dados/conjuntos-dados.
DASHBOARD. Rede Genômica-Dashboard. 2025. Genomahcov - Fiocruz.
DEMENECK H, et al. Clinical characteristics and outcomes of hospitalized patients with COVID-19 in a city of South Brazil: Have they changed through the first year of the pandemic? PLoS One, 2023; 18(6): e0286589.
DRUMMOND PD, et al. Profile and Outcomes of Hospitalized COVID-19 Patients during the Prevalence of the Omicron Variant According to the Brazilian Regions: A Retrospective Cohort Study from 2022. Vaccines, 2023; 11(1): 1568.
FAM JN, et al. SARS-CoV-2 strains and clinical profiles of COVID-19 patients in a Southern Brazil hospital. Frontiers in Immunology, 2024; 15: 1444620.
FARIAS NR, et al. Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil. Science, 2021; 372(6544): 815–821.
FREITAS ARR, et al. A emergência da nova variante P.1 do SARS-CoV-2 no Amazonas (Brasil) foi temporalmente associada a uma mudança no perfil da mortalidade. 2021. SciELO Preprints, 2021. DOI: 10.1590/SciELOPreprints.2030.
GOMES BBM, et al. Impact of SARS-CoV-2 variants on COVID-19 symptomatology and severity during five waves. Heliyon, 2024; 10: e40113.
HOJO-SOUZA NS, et al. Clinical symptom profile of hospitalized COVID-19 Brazilian patients according to SARS-CoV-2 variants. Epidemiology and Health, 2023; 45: e2023079.
KIM MK, et al. Clinical Characteristics of 40 Patients Infected With the SARS-CoV-2 Omicron Variant in Korea. Journal of Korean Medical Science, 2022; 37(3): e31.
KUNKEL M, et al. Curvas epidêmicas e o perfil de pacientes hospitalizados pela COVID-19 em região de fronteira. Revista Latino-Americana de Enfermagem, 2024; 32.
LEITE SJO, et al. Predição de séries temporais da COVID19: uma avaliação de redes neurais com células LSTM. Anais do 15. Congresso Brasileiro de Inteligência Computacional, 2021; 1–7.
LEUNG C, et al. Difference in clinical features of SARS-CoV-2 in pediatric patients before and after emergence of P.1. Pediatric Research, 2023; 93: 176–182.
LIZZI EA e GARRIDO MVG. Modelos aditivos generalizados geoespacial com componente de série temporal: estudo de caso de homicídios de homens negros. 2020. Proceeding Series of the Brazilian Society of Computational and Applied Mathematics, v. 7, n. 1, 2020. Trabalho apresentado no XXXIX CNMAC, Uberlândia - MG, 2019.
MEO SA, et al. Omicron new variant BA.2.86 (Pirola): Epidemiological, biological, and clinical characteristics – a global data-based analysis. 2023. Eur Rev Med Pharmacol Sci. 2023 Oct;27(19):9470-9476. doi: 10.26355/eurrev_202310_33975. PMID: 37843359.
MOURA EC, et al. Covid-19: evolução temporal e imunização nas três ondas epidemiológicas, Brasil, 2020–2022. Revista de Saúde Pública, 2022; 56: 105.
OLIVEIRA JT, et al. Regional drinking water composition effects on hip fracture risk: a spatial analysis of nationwide hospital admissions in Portugal. Saúde e Sociedade, 2020; 29(2): e200094.
R DEVELOPMENT CORE TEAM. R: A language and environment for statistical computing. Vienna: R Foundation, 2010.
SANSONE NMS, et al. Epidemiologic profile of severe acute respiratory infection in Brazil during the COVID-19 pandemic. Frontiers in Microbiology, 2022; 13: 911036.
SANTOS PR, et al. Influence of SARS-CoV-2 variants on COVID-19 epidemiological and clinical profiles: a comparative analysis of two waves of cases. Virology Journal, 2024; 21: 260.
SOTHE C, et al. Uso de Modelo Aditivo Generalizado para Análise Espacial da Suscetibilidade a Movimentos de Massa. Revista do Departamento de Geografia, 2017; 34: 68–81.
VOLOCH CM, et al. Genomic characterization of a novel SARS-CoV-2 lineage from Rio de Janeiro, Brazil. Journal of Virology, 2021; 95: e00119-21.
VULTURAR DM, et al. Delta Variant in the COVID-19 Pandemic: A Comparative Study on Clinical Outcomes Based on Vaccination Status. Journal of Personalized Medicine, 2024; 14(4): 358.
Wickham H, et al. dplyr: Grammar of Data Manipulation. 2017 R package version 1.2.1, https://dplyr.tidyverse.org.
WICKHAM H. Data tidying. 2014. Journal of Statistical Software. Volume VV, Issue II. http://www.jstatsoft.org/
WICKHAM H. ggplot2. WIREs Comput. Stat., 2011; 3(2): 180–185.
WOOD SN. On p-values for smooth components of an extended generalized additive model. Biometrika, 2013; 100(1): 221-228.
ZEISER FA, et al. First and second COVID-19 waves in Brazil: A cross-sectional study of patients’ characteristics related to hospitalization and in-hospital mortality. The Lancet Regional Health – Americas, 2022; 6.
