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Spatiotemporal dynamics of hantavirus disease in a fast-changing country

Grant number: 17/21816-0
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): April 20, 2018
Effective date (End): March 19, 2019
Field of knowledge:Biological Sciences - Ecology
Principal Investigator:Milton Cezar Ribeiro
Grantee:Renata de Lara Muylaert
Supervisor abroad: David Thomas Stuart Hayman
Home Institution: Instituto de Biociências (IB). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
Local de pesquisa : 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.

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MUYLAERT, RENATA L.; BOVENDORP, RICARDO SIQUEIRA; SABINO-SANTOS, JR., GILBERTO; PRIST, PAULA R.; MELO, GERUZA LEAL; PRIANTE, CAMILA DE FATIMA; WILKINSON, DAVID A.; RIBEIRO, MILTON CEZAR; HAYMAN, DAVID T. S. Hantavirus host assemblages and human disease in the Atlantic Forest. PLoS Neglected Tropical Diseases, v. 13, n. 8 AUG 2019. Web of Science Citations: 0.
FERRO E SILVA, ANDREIA MANTOVANI; SOBRAL-SOUZA, THADEU; VANCINE, MAURICIO HUMBERTO; MUYLAERT, RENATA LARA; DE ABREU, ANA PAULA; PELLOSO, SANDRA MARISA; DE BARROS CARVALHO, MARIA DALVA; DE ANDRADE, LUCIANO; RIBEIRO, MILTON CEZAR; DE ORNELAS TOLEDO, MAX JEAN. Spatial prediction of risk areas for vector transmission of Trypanosoma cruzi in the State of Parana, southern Brazil. PLoS Neglected Tropical Diseases, v. 12, n. 10 OCT 2018. Web of Science Citations: 1.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.