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Abstract Arboviruses transmitted mainly byAedes(Stegomyia)aegyptiandAe. albopictus, including dengue, chikungunya, and Zika viruses, and yellow fever virus in urban settings, pose an escalating global threat. Existing risk maps, often hampered by surveillance biases, may underestimate or misrepresent the true distribution of these diseases and do not incorporate epidemiological similarities despite shared vector species. We address this by generating new global environmental suitability maps forAedes-borne arboviruses using a multi-disease ecological niche model with a nested surveillance model fit to a dataset of over 21,000 occurrence points. This reveals a convergence in suitability around a common global distribution with recent spread of chikungunya and Zika closely aligning with areas suitable for dengue. We estimate that 5.66 (95% confidence interval 5.64-5.68) billion people live in areas suitable for dengue, chikungunya and Zika and 1.54 (1.53-1.54) billion people for yellow fever. We find large national and subnational differences in surveillance capabilities with higher income more accessible areas more likely to detect, diagnose and report viral diseases, which may have led to overestimation of risk in the United States and Europe. When combined with estimates of uncertainty, these suitability maps can be used by ministries of health to target limited surveillance and intervention resources in new strategies against these emerging threats. 

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.