Why the growth of arboviral diseases necessitates a new generation of global risk maps and future projections

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.

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Publication Details

Journal
PLoS Computational Biology
Published
2025-04-04
DOI
https://doi.org/10.1371/journal.pcbi.1012771
Citations
8
Primary Topic
Mosquito-borne diseases and control
Type
article
Field-Weighted Citation Impact
15.03

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article

Why the growth of arboviral diseases necessitates a new generation of global risk maps and future projections

Jane P. Messina, Richard J. Maude, Simon Cauchemez, Ingrid B. Rabe et al.
8 citations
PLoS Computational Biology
Mosquito-borne diseases and control
15.03
article

Why the growth of arboviral diseases necessitates a new generation of global risk maps and future projections

Jane P. Messina, Richard J. Maude, Simon Cauchemez, Ingrid B. Rabe, Ilaria Dorigatti, Hannah Clapham, Katy A. M. Gaythorpe, Velma K. Lopez, Jan C. Semenza, A. Townsend Peterson, Oliver J. Brady, Wenbiao Hu, Quan Minh Tran, Sadie J. Ryan, Diana P. Rojas, Leonardo Soares Bastos, Laith Hussain‐Alkhateeb, Michael A. Johansson, Ahyoung Lim, Erin A. Mordecai, Isabel Rodriquez-Barraquer, Henrik Salje, Jamie M. Caldwell
article en
8 citations

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.

PLoS Computational BiologyVol. 21(4)
Centers for Disease Control and Prevention (US), Northeastern University (US), Centre National de la Recherche Scientifique (FR), American Museum of Natural History (US), Institut Pasteur (FR), Queensland University of Technology (AU), National University of Singapore (SG), University of California, San Francisco (US), Princeton University (US), The Open University (GB), Université Paris Cité (FR), Heidelberg University (DE), University of Cambridge (GB), Mahidol University (TH), Puerto Rico Department of Health (PR), University Hospital Heidelberg (DE), University of Florida (US), University of Oxford (GB), London School of Hygiene & Tropical Medicine (GB), King Abdullah International Medical Research Center (SA), Institute for Biodiversity (DE), Biodiversity Research Institute (US), World Health Organization (CH), Mahidol Oxford Tropical Medicine Research Unit (TH), National University Health System (SG), Imperial College London (GB), Fundação Oswaldo Cruz (BR), University of Gothenburg (SE), University of Hong Kong (HK), Umeå University (SE), Medical Research Council (GB), Stanford University (US)
National Science Foundation, World Health Organization, Wellcome Trust, National Research Foundation of Korea, National Institutes of Health, Centers for Disease Control and Prevention, Stanford Woods Institute for the Environment, Medical Research Council, Stanford University Center for Innovation in Global Health, H2020 European Research Council
Openalex Percentile: Top 1%
Mosquito-borne diseases and control
15.03
DOIOpen Access PDFOpenAlex
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