# Mechanistic yellow fever modelling under climate change in Brazil and beyond: Information gaps and future steps

## TL;DR

This paper discusses how climate change is affecting yellow fever transmission in tropical regions and highlights the need for better models to understand and predict these changes.

## Contribution

The paper identifies key gaps in mechanistic yellow fever modeling and proposes future research directions to incorporate climate and environmental factors.

## Key findings

- Current models lack explicit integration of climate and environmental changes in yellow fever transmission.
- Workshop discussions revealed major uncertainties in vectors, non-human primates, and vaccination impact.
- The paper outlines priority areas for future research to improve model accuracy and applicability.

## Abstract

Yellow fever (YF) remains a significant public health threat in tropical regions, particularly in South America and Africa. The combined forces of climate change, land-use, urbanisation, globalisation, and insufficient surveillance and health infrastructure are driving the re-emergence and expansion of YF into new areas. While mathematical models have been used to estimate transmission risk, disease burden, and the impact of vaccination, there remains a crucial gap in mechanistic models that explicitly capture how climate and environmental changes directly influence YF transmission. To address this gap, we convened a workshop in Brazil as part of the Vaccine Impact Modelling Consortium’s Climate Change programme, bringing together national and international experts. The workshop aimed to present current modelling approaches, identify key knowledge gaps, and develop strategies to improve data collection and model applicability. Discussions highlighted major uncertainties regarding vectors, non-human primates, surveillance sensitivity, vaccination, and climatic and environmental drivers. This paper synthesises the outcomes of the workshop, including priority areas for future research and recommendations for advancing mechanistic YF modelling in the context of climate change, with a focus on both Brazil and broader tropical regions.