| Grant number: | 17/21816-0 |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| Start date: | April 20, 2018 |
| End date: | March 19, 2019 |
| Field of knowledge: | Biological Sciences - Ecology - Applied Ecology |
| Principal Investigator: | Milton Cezar Ribeiro |
| Grantee: | Renata de Lara Muylaert |
| Supervisor: | David Thomas Stuart Hayman |
| Host Institution: | Instituto de Biociências (IB). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil |
| Institution abroad: | Massey University, New Zealand |
| Associated to the scholarship: | 15/17739-4 - Landscape effects and the interaction between mammals and hantavirus in the Atlantic Forest, BP.DR |
Abstract Recent studies have pointed out the rise of emergent zoonotic diseases in the last years, and have associated their emergence with human activities, such as deforestation and agricultural expansion. Hantavirus disease is among these diseases, which has high lethality rates in Brazil. Several abundant and widely distributed rodents from the family Sigmodontinae are the reservoirs of these viruses. Modeling approaches that maximize predictive efficiency and reduces computational time or field effort are desired to predict emergent diseases. Two of these approaches are investigated here aiming on hantavirus disease in Brazil as a model of study. We aim to: 1) Understand the contribution of spatial and temporal structure for hantavirus disease distribution in Brazil; 2) Generate a disease risk map using Bayesian models and confirmed cases as a function of socioenvironmental vulnerability and agricultural expansion and deforestation. 3) Estimate the niche of rodent reservoirs of hantavirus in Brazil and evaluate their contribution as a risk map for the disease in Brazil; 4) Compare risk maps and validate them during the modelling procedure and with new confirmed cases (2014-2018). We expect agricultural expansion and risk group population density to be the main predictors of disease risk. We also expect that Bayesian models and component based models combined will improve predictability of hantavirus disease cases in Brazil. In addition, we expect that new cases will validate mainly the hantavirus Bayesian disease risk map, since the habitat suitability for rodent reservoirs might be misleading the real disease incidence. If suitability shows to be a good predictor of new cases, then Ecological Niche Models can be easily applied as effective strategy for the areas with higher disease risk, guiding control and surveillance investments and actions. | |
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