Why the growth of arboviral diseases necessitates a new generation of global risk maps and future projections
6
Issued Date
2025-04-01
Resource Type
ISSN
1553734X
eISSN
15537358
Scopus ID
2-s2.0-105001950828
Journal Title
PLoS Computational Biology
Volume
21
Issue
4 APRIL
Rights Holder(s)
SCOPUS
Bibliographic Citation
PLoS Computational Biology Vol.21 No.4 APRIL (2025)
Suggested Citation
Brady O.J., Bastos L.S., Caldwell J.M., Cauchemez S., Clapham H.E., Dorigatti I., Gaythorpe K.A.M., Hu W., Hussain-Alkhateeb L., Johansson M.A., Lim A., Lopez V.K., Maude R.J., Messina J.P., Mordecai E.A., Peterson A.T., Rodriquez-Barraquer I., Rabe I.B., Rojas D.P., Ryan S.J., Salje H., Semenza J.C., Tran Q.M. Why the growth of arboviral diseases necessitates a new generation of global risk maps and future projections. PLoS Computational Biology Vol.21 No.4 APRIL (2025). doi:10.1371/journal.pcbi.1012771 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/109492
Title
Why the growth of arboviral diseases necessitates a new generation of global risk maps and future projections
Author(s)
Author's Affiliation
Mahidol Oxford Tropical Medicine Research Unit
Université Paris Cité
The University of Hong Kong Li Ka Shing Faculty of Medicine
King Saud bin Abdulaziz University for Health Sciences
Centers for Disease Control and Prevention San Juan
UCSF School of Medicine
London School of Hygiene & Tropical Medicine
University of Cambridge
Umeå Universitet
Northeastern University
Organisation Mondiale de la Santé
University of Oxford
Fundacao Oswaldo Cruz
National University of Singapore
Universität Heidelberg
Göteborgs Universitet
Imperial College London
University of Florida
Stanford University
The Open University
Queensland University of Technology
Nuffield Department of Medicine
University KS Natural History Museum
Princeton University
Université Paris Cité
The University of Hong Kong Li Ka Shing Faculty of Medicine
King Saud bin Abdulaziz University for Health Sciences
Centers for Disease Control and Prevention San Juan
UCSF School of Medicine
London School of Hygiene & Tropical Medicine
University of Cambridge
Umeå Universitet
Northeastern University
Organisation Mondiale de la Santé
University of Oxford
Fundacao Oswaldo Cruz
National University of Singapore
Universität Heidelberg
Göteborgs Universitet
Imperial College London
University of Florida
Stanford University
The Open University
Queensland University of Technology
Nuffield Department of Medicine
University KS Natural History Museum
Princeton University
Corresponding Author(s)
Other Contributor(s)
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.