Determination of the spatial susceptibility to Yellow Fever using a multicriteria analysis

• Autor(a) principal: de Paiva,Camilla Adriane
• Data de Publicação: 2019
• Outros Autores: Oliveira,Adriana Paulo de Sousa, Muniz,Samuel Soares, Calijuri,Maria Lúcia, dos Santos,Vitor Juste, Alves,Sabrina do Carmo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Memórias do Instituto Oswaldo Cruz
• Texto Completo: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0074-02762019000100323
• topic: geographic information systems; risk estimation; vulnerability map; epidemiology

BACKGROUND The outbreak of sylvatic Yellow Fever (SYF) in humans during 2016-2017 in Brazil is one of the greatest in the history of the disease. The occurrence of the disease in areas with low vaccination coverage favoured the dissemination of the disease; therefore, it is necessary to identify the areas vulnerability to the YF virus (YFV) to assist in the adoption of preventive measures. OBJECTIVE To correlate the physical-environmental elements associated with the occurrence of SYF in humans via a multicriteria analysis. METHODS For the multicriteria analysis, preponderant elements related to SYF occurrences, including soil usage and coverage, temperature, precipitation, altitude, mosquito transmitters, and non-human primate occurrence areas, were considered. The results were validated by assessing the correlation between the incidence of SYF and the vulnerable areas identified in the multicriteria analysis. RESULTS Two regions with different vulnerability to the occurrence of the disease were identified in the multicriteria analysis, with emphasis on the southern areas of the state of São Paulo northeast areas of Minas Gerais, and the entire states of Rio de Janeiro and Espírito Santo. The map of SYF vulnerability obtained in the multicriteria analysis coincides with the areas in which cases of the disease have been recorded. The regions that presented the greatest suitability were in fact the municipalities with the highest incidence. MAIN CONCLUSIONS The multicriteria analysis revealed that the elements that were used are suited for and consistent in the prediction of the areas that are vulnerable to SYF. The results obtained indicate the proximity of the areas that are most vulnerable to the disease to densely populated areas where an Aedes aegypti infestation was observed, which confers a high risk of re-urbanisation of YF.