High-resolution spatiotemporal analysis of chikungunya epidemics between 2019 and 2020 in Salvador, Brazil: a municipality-level transmission dynamics studyResearch in context
Summary: Background: Chikungunya virus (CHIKV) continues to cause explosive epidemics in Brazil. We investigated its transmission dynamics in Salvador, Brazil, to understand the factors driving its reemergence and spread. Methods: In this epidemiological study, we analyzed by census tracts the chik...
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Elsevier
2025-03-01
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| Series: | The Lancet Regional Health. Americas |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667193X25000134 |
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| author | Hernan D. Argibay Cristiane W. Cardoso William M. de Souza Raquel L. Souza Maysa Pellizzaro Geraldo M. Cunha Julie Clennon Scott C. Weaver Mitermayer G. Reis Uriel Kitron Guilherme S. Ribeiro |
| author_facet | Hernan D. Argibay Cristiane W. Cardoso William M. de Souza Raquel L. Souza Maysa Pellizzaro Geraldo M. Cunha Julie Clennon Scott C. Weaver Mitermayer G. Reis Uriel Kitron Guilherme S. Ribeiro |
| author_sort | Hernan D. Argibay |
| collection | DOAJ |
| description | Summary: Background: Chikungunya virus (CHIKV) continues to cause explosive epidemics in Brazil. We investigated its transmission dynamics in Salvador, Brazil, to understand the factors driving its reemergence and spread. Methods: In this epidemiological study, we analyzed by census tracts the chikungunya cases reported in Salvador during the 2019–2020 epidemics. We used SaTScan software to identify spatiotemporal clusters and assessed how census tract characteristics (socioeconomic, environmental, and prior chikungunya occurrence) influenced chikungunya incidence through a Bayesian spatial model using Integrated Laplace Approximation (INLA). Findings: Citywide, 19,129 cases (mean age: 40.2, range: 0–112; male: 41.8%, female: 58.0%, non-binary: 0.2%) were reported between 2016 and 2020, with a significant increase in 2019 and 2020 (4549 and 13,071 cases, respectively). We found nine spatiotemporal clusters in 2019 and seven in 2020, with 17.2% (387 of 2252) overlap of census tracts between the two years. The chikungunya incidence by census tract was negatively correlated with income and vegetation but positively correlated with land surface temperature. The census tract level incidence in 2020 exhibited a non-linear correlation with the 2019 incidence; up to a certain level, the 2020 risk increased as the 2019 incidence increased, but when the 2019 incidence was extreme, the 2020 risk was reduced. Interpretation: These findings suggest that CHIKV transmission is localized, even during what appeared to be a citywide epidemic, creating high-risk pockets within the city. Socioeconomic factors, environmental conditions, and prior chikungunya incidence, probably reflecting herd immunity, all influence case incidence. Funding: Secretary of Health of Salvador, Federal University of Bahia, Oswaldo Cruz Foundation, National Council for Scientific and Technological Development, Foundation for Research Support of the Bahia State, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES), Clinical and Applied Research Network in Chikungunya, Global Virus Network, Burroughs Wellcome Fund, Wellcome Trust, and the United States National Institutes of Health. |
| format | Article |
| id | doaj-art-23394e2905284d09b151664d76d9f2de |
| institution | Kabale University |
| issn | 2667-193X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | The Lancet Regional Health. Americas |
| spelling | doaj-art-23394e2905284d09b151664d76d9f2de2025-01-25T04:11:28ZengElsevierThe Lancet Regional Health. Americas2667-193X2025-03-0143101003High-resolution spatiotemporal analysis of chikungunya epidemics between 2019 and 2020 in Salvador, Brazil: a municipality-level transmission dynamics studyResearch in contextHernan D. Argibay0Cristiane W. Cardoso1William M. de Souza2Raquel L. Souza3Maysa Pellizzaro4Geraldo M. Cunha5Julie Clennon6Scott C. Weaver7Mitermayer G. Reis8Uriel Kitron9Guilherme S. Ribeiro10Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, BrazilSecretaria Municipal de Saúde de Salvador, Salvador, Bahia, BrazilDepartment of Microbiology, Immunology and Molecular Genetics, University of Kentucky, College of Medicine, Lexington, KY, USA; Global Virus Network, Baltimore, MD, USAInstituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, BrazilInstituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil; Instituto de Saúde Coletiva, Universidade Federal da Bahia, Salvador, Bahia, BrazilEscola Nacional de Saúde Pública, Fundação Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, BrazilDepartment of Environmental Sciences, Emory University, Atlanta, GA, USAGlobal Virus Network, Baltimore, MD, USA; Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX, USA; World Reference Center for Emerging Viruses and Arboviruses, University of Texas Medical Branch, Galveston, TX, USAInstituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil; Faculdade de Medicina, Universidade Federal da Bahia, Salvador, Bahia, Brazil; Yale University, New Haven, CT, USADepartment of Environmental Sciences, Emory University, Atlanta, GA, USAInstituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil; Faculdade de Medicina, Universidade Federal da Bahia, Salvador, Bahia, Brazil; Corresponding author.Summary: Background: Chikungunya virus (CHIKV) continues to cause explosive epidemics in Brazil. We investigated its transmission dynamics in Salvador, Brazil, to understand the factors driving its reemergence and spread. Methods: In this epidemiological study, we analyzed by census tracts the chikungunya cases reported in Salvador during the 2019–2020 epidemics. We used SaTScan software to identify spatiotemporal clusters and assessed how census tract characteristics (socioeconomic, environmental, and prior chikungunya occurrence) influenced chikungunya incidence through a Bayesian spatial model using Integrated Laplace Approximation (INLA). Findings: Citywide, 19,129 cases (mean age: 40.2, range: 0–112; male: 41.8%, female: 58.0%, non-binary: 0.2%) were reported between 2016 and 2020, with a significant increase in 2019 and 2020 (4549 and 13,071 cases, respectively). We found nine spatiotemporal clusters in 2019 and seven in 2020, with 17.2% (387 of 2252) overlap of census tracts between the two years. The chikungunya incidence by census tract was negatively correlated with income and vegetation but positively correlated with land surface temperature. The census tract level incidence in 2020 exhibited a non-linear correlation with the 2019 incidence; up to a certain level, the 2020 risk increased as the 2019 incidence increased, but when the 2019 incidence was extreme, the 2020 risk was reduced. Interpretation: These findings suggest that CHIKV transmission is localized, even during what appeared to be a citywide epidemic, creating high-risk pockets within the city. Socioeconomic factors, environmental conditions, and prior chikungunya incidence, probably reflecting herd immunity, all influence case incidence. Funding: Secretary of Health of Salvador, Federal University of Bahia, Oswaldo Cruz Foundation, National Council for Scientific and Technological Development, Foundation for Research Support of the Bahia State, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES), Clinical and Applied Research Network in Chikungunya, Global Virus Network, Burroughs Wellcome Fund, Wellcome Trust, and the United States National Institutes of Health.http://www.sciencedirect.com/science/article/pii/S2667193X25000134Chikungunya virusTransmission dynamicsEpidemiologyRisk factorsEpidemicHerd immunity |
| spellingShingle | Hernan D. Argibay Cristiane W. Cardoso William M. de Souza Raquel L. Souza Maysa Pellizzaro Geraldo M. Cunha Julie Clennon Scott C. Weaver Mitermayer G. Reis Uriel Kitron Guilherme S. Ribeiro High-resolution spatiotemporal analysis of chikungunya epidemics between 2019 and 2020 in Salvador, Brazil: a municipality-level transmission dynamics studyResearch in context The Lancet Regional Health. Americas Chikungunya virus Transmission dynamics Epidemiology Risk factors Epidemic Herd immunity |
| title | High-resolution spatiotemporal analysis of chikungunya epidemics between 2019 and 2020 in Salvador, Brazil: a municipality-level transmission dynamics studyResearch in context |
| title_full | High-resolution spatiotemporal analysis of chikungunya epidemics between 2019 and 2020 in Salvador, Brazil: a municipality-level transmission dynamics studyResearch in context |
| title_fullStr | High-resolution spatiotemporal analysis of chikungunya epidemics between 2019 and 2020 in Salvador, Brazil: a municipality-level transmission dynamics studyResearch in context |
| title_full_unstemmed | High-resolution spatiotemporal analysis of chikungunya epidemics between 2019 and 2020 in Salvador, Brazil: a municipality-level transmission dynamics studyResearch in context |
| title_short | High-resolution spatiotemporal analysis of chikungunya epidemics between 2019 and 2020 in Salvador, Brazil: a municipality-level transmission dynamics studyResearch in context |
| title_sort | high resolution spatiotemporal analysis of chikungunya epidemics between 2019 and 2020 in salvador brazil a municipality level transmission dynamics studyresearch in context |
| topic | Chikungunya virus Transmission dynamics Epidemiology Risk factors Epidemic Herd immunity |
| url | http://www.sciencedirect.com/science/article/pii/S2667193X25000134 |
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