Development of a spatially explicit agent-based model to investigate the spread of yellow fever virus between fragmented landscapes

• Grant number: 21/10379-3
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Effective date (Start): February 01, 2022
• Effective date (End): January 31, 2023
• Field of knowledge: Health Sciences - Collective Health - Epidemiology
• Principal Investigator: Mauro Toledo Marrelli
• Grantee: Antônio Ralph Medeiros de Sousa
• Supervisor: Volker Grimm
• Host Institution: Faculdade de Saúde Pública (FSP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Research place: Helmholtz Association, Germany
• Associated to the scholarship: 18/18751-6 - Development of predictive models for the dynamics of transmission and spatial dispersion of the sylvatic yellow fever in Brazil., BP.PD

Abstract

Since 2014, Brazil has experienced the largest sylvatic yellow fever outbreak ever recorded in the country, with a high number of human cases and epizootics in non-human primates. The yellow fever vírus (YFV) has expanded from its endemic area in the Amazon basin to Brazilian Atlantic Coast, spreading through highly fragmented landscapes. This research project proposes modeling the transmission and dispersion dynamic of YFV to investigate the role of different landscape configurations on the prevalence of human cases and epizootics in monkeys, as well as to evaluate the effect of different levels of human vaccination coverage on the ability of the YFV to spread across the landscape. The basic idea is to develop a spatially explicit agent-based model to simulate the YFV dispersion dynamics in fragmented landscapes with mosquitoes, monkeys, and humans. The model will be implemented in NetLogo and pattern-oriented modeling will guide the model design and analysis. As expected results, we seek to better understand the role of landscape and humans in the propagation of YFV through forest areas and obtain a structurally realistic, valid, and useful model to propose new hypotheses and predict risk situations. (AU)