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Yellow fever in Ghana: Predicting emergence and ecology from historical outbreaks.
Judson, Seth D; Kenu, Ernest; Fuller, Trevon; Asiedu-Bekoe, Franklin; Biritwum-Nyarko, Alberta; Schroeder, Lee F; Dowdy, David W.
Afiliação
  • Judson SD; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Kenu E; Department of Epidemiology, University of Ghana School of Public Health, Accra, Ghana.
  • Fuller T; Institute of the Environment and Sustainability, University of California Los Angeles, USA.
  • Asiedu-Bekoe F; Disease Surveillance Department, Ghana Health Service, Accra, Ghana.
  • Biritwum-Nyarko A; Policy, Planning, Monitoring & Evaluation Division, Ghana Health Service, Accra, Ghana.
  • Schroeder LF; Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
  • Dowdy DW; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
medRxiv ; 2024 Jan 30.
Article em En | MEDLINE | ID: mdl-38352600
ABSTRACT
Understanding the epidemiology and ecology of yellow fever in endemic regions is critical for preventing future outbreaks. Ghana is a high-risk country for yellow fever. In this study we estimate the epidemiology, ecological cycles, and areas at risk for yellow fever in Ghana based on historical outbreaks. We identify 2371 cases and 887 deaths (case fatality rate 37.4%) from yellow fever reported in Ghana from 1910 to 2022. Since implementation of routine childhood vaccination in 1992, the estimated mean annual number of cases decreased by 81% and the geographic distribution of yellow fever cases also changed. While there have been multiple large historical outbreaks of yellow fever in Ghana from the urban cycle, recent outbreaks have originated among unvaccinated nomadic groups in rural areas with the sylvatic/savanna cycles. Using machine learning and an ecological niche modeling framework, we predict areas in Ghana that are similar to where prior yellow fever outbreaks have originated based on temperature, precipitation, landcover, elevation, and human population density. We find differences in predictions depending on the ecological cycles of outbreaks. Ultimately, these findings and methods could be used to inform further subnational risk assessments for yellow fever in Ghana and other high-risk countries.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: MedRxiv Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: MedRxiv Ano de publicação: 2024 Tipo de documento: Article