Spatiotemporal dynamics of hantavirus cardiopulmonary syndrome transmission risk in Brazil
Nenhuma Miniatura disponível
Data
2019-10-31
Autores
Muylaert, Renata L. [UNESP]
Sabino-Santos, Gilberto
Prist, Paula R.
Oshima, Júlia E.F. [UNESP]
Niebuhr, Bernardo Brandão [UNESP]
Sobral-Souza, Thadeu
De Oliveira, Stefan Vilges
Bovendorp, Ricardo Siqueira
Marshall, Jonathan C.
Hayman, David T.S.
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
Background: Hantavirus disease in humans is rare but frequently lethal in the Neotropics. Several abundant and widely distributed Sigmodontinae rodents are the primary hosts of Orthohantavirus and, in combination with other factors, these rodents can shape hantavirus disease. Here, we assessed the influence of host diversity, climate, social vulnerability and land use change on the risk of hantavirus disease in Brazil over 24 years. Methods: Landscape variables (native forest, forestry, sugarcane, maize and pasture), climate (temperature and precipitation), and host biodiversity (derived through niche models) were used in spatiotemporal models, using the 5570 Brazilian municipalities as units of analysis. Results: Amounts of native forest and sugarcane, combined with temperature, were the most important factors influencing the increase of disease risk. Population at risk (rural workers) and rodent host diversity also had a positive effect on disease risk. Conclusions: Land use change—especially the conversion of native areas to sugarcane fields—can have a significant impact on hantavirus disease risk, likely by promoting the interaction between the people and the infected rodents. Our results demonstrate the importance of understanding the interactions between landscape change, rodent diversity, and hantavirus disease incidence, and suggest that land use policy should consider disease risk. Meanwhile, our risk map can be used to help allocate preventive measures to avoid disease.
Descrição
Palavras-chave
Approximate Bayesian inference, Emerging diseases, Integrated nested Laplace approximations, Land use change, Latent Gaussian models, Polygon-based analysis, Public health, Zero inflation
Como citar
Viruses, v. 11, n. 11, 2019.