Please use this identifier to cite or link to this item:
https://www.arca.fiocruz.br/handle/icict/68604
Type
ArticleCopyright
Open access
Collections
- BA - IGM - Artigos de Periódicos [3814]
- ENSP - Artigos de Periódicos [2412]
Metadata
Show full item record
HIGH-RESOLUTION SPATIOTEMPORAL ANALYSIS OF CHIKUNGUNYA EPIDEMICS BETWEEN 2019 AND 2020 IN SALVADOR, BRAZIL: A MUNICIPALITY-LEVEL TRANSMISSION DYNAMICS STUDY
Transmission dynamics
Epidemiology
Risk factors
Epidemic
Herd immunity
Spatiotemporal analysis
Incidence
Author
Affilliation
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil.
Secretaria Municipal de Saúde de Salvador. Salvador, BA, Brasil.
University of Kentucky. College of Medicine. Department of Microbiology, Immunology and Molecular Genetics. Lexington, KY, USA / Global Virus Network. Baltimore, MD, USA.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil / Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil.
Fundação Oswaldo Cruz. Escola Nacional de Saúde Pública Sergio Arouca. Rio de Janeiro, RJ, Brasil.
Emory University. Department of Environmental Sciences. Atlanta, GA, USA.
Global Virus Network. Baltimore, MD, USA / University of Texas Medical Branch. Department of Microbiology and Immunology. Galveston, TX, USA / University of Texas Medical Branch. World Reference Center for Emerging Viruses and Arboviruses. Galveston, TX, USA.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil / Universidade Federal da Bahia. Faculdade de Medicina. Salvador. BA, Brasil / Yale University. New Haven, CT, USA.
Emory University. Department of Environmental Sciences. Atlanta, GA, USA.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil / Universidade Federal da Bahia. Faculdade de Medicina. Salvador, BA, Brasil.
Secretaria Municipal de Saúde de Salvador. Salvador, BA, Brasil.
University of Kentucky. College of Medicine. Department of Microbiology, Immunology and Molecular Genetics. Lexington, KY, USA / Global Virus Network. Baltimore, MD, USA.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil / Universidade Federal da Bahia. Instituto de Saúde Coletiva. Salvador, BA, Brasil.
Fundação Oswaldo Cruz. Escola Nacional de Saúde Pública Sergio Arouca. Rio de Janeiro, RJ, Brasil.
Emory University. Department of Environmental Sciences. Atlanta, GA, USA.
Global Virus Network. Baltimore, MD, USA / University of Texas Medical Branch. Department of Microbiology and Immunology. Galveston, TX, USA / University of Texas Medical Branch. World Reference Center for Emerging Viruses and Arboviruses. Galveston, TX, USA.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil / Universidade Federal da Bahia. Faculdade de Medicina. Salvador. BA, Brasil / Yale University. New Haven, CT, USA.
Emory University. Department of Environmental Sciences. Atlanta, GA, USA.
Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Salvador, BA, Brasil / Universidade Federal da Bahia. Faculdade de Medicina. Salvador, BA, Brasil.
Abstract
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.
Keywords
Chikungunya virusTransmission dynamics
Epidemiology
Risk factors
Epidemic
Herd immunity
Spatiotemporal analysis
Incidence
Share