Why the growth of arboviral diseases necessitates a new generation of global risk maps and future projections
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Global risk maps are an important tool for assessing the global threat of mosquito and tick-transmitted arboviral diseases. Public health officials increasingly rely on risk maps to understand the drivers of transmission, forecast spread, identify gaps in surveillance, estimate disease burden, and target and evaluate the impact of interventions. Here, we describe how current approaches to mapping arboviral diseases have become unnecessarily siloed, ignoring the strengths and weaknesses of different data types and methods. This places limits on data and model output comparability, uncertainty estimation and generalisation that limit the answers they can provide to some of the most pressing questions in arbovirus control. We argue for a new generation of risk mapping models that jointly infer risk from multiple data types. We outline how this can be achieved conceptually and show how this new framework creates opportunities to better integrate epidemiological understanding and uncertainty quantification. We advocate for more co-development of risk maps among modellers and end-users to better enable risk maps to inform public health decisions. Prospective validation of risk maps for specific applications can inform further targeted data collection and subsequent model refinement in an iterative manner. If the expanding use of arbovirus risk maps for control is to continue, methods must develop and adapt to changing questions, interventions and data availability.
Editor(s)
Alizon, Samuel
Date Issued
2025-04-04
Date Acceptance
2025-04-01
Citation
PLoS Computational Biology, 2025, 21 (4), pp.e1012771-e1012771
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Start Page
e1012771
End Page
e1012771
Journal / Book Title
PLoS Computational Biology
Volume
21
Issue
4
Copyright Statement
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Identifier
10.1371/journal.pcbi.1012771
Publication Status
Published
Article Number
e1012771
Date Publish Online
2025-04-04