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A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk.

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Lim, Ah-Young 
Jafari, Yalda 
Caldwell, Jamie M 
Clapham, Hannah E 
Gaythorpe, Katy AM 


BACKGROUND: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.


Acknowledgements: This work was discussed with the Technical Advisory Group on arboviruses (TAG-Arbovirus), the Secretariat of the Global Arbovirus Initiative (Raman Velayudhan, Laurence Cibrelus, Jennifer Horton, Marie-Eve Raguenaud, Maria Van Kerkhove, Qingxia Zhong), and the participants of the arbovirus risk mapping meeting held in Seattle in October 2022 as part of the ASTMH (Isabel Rodriguez-Barraquer, Leo Bastos, Simon Cauchemez, Ilaria Dorigatti, Neil Ferguson, Simon Hay, Wenbiao Hu, Axel Kroeger, Velma Lopez, A. Townsend Peterson, Maile Philips, David Pigott, Krystina Rysava, Sophie von Dobschütz, and Anna Winters).

Funder: Princeton University Climate and Disease program with funding from High Meadows Environmental Institute Grand Challenges and Environmental Studies Strategic Fund and the Joseph & Susan Gatto Foundation

Funder: Stanford Center for Innovation in Global Health, and the Stanford Woods Institute for the Environment

Funder: NSF CIBR: VectorByte: A Global Informatics Platform for studying the Ecology of Vector-Borne Diseases; Grant(s): NSF DBI 2016265


Aedes-borne diseases, Arboviruses, Chikungunya, Dengue, Geostatistical models, Predictive modelling, Risk mapping, Yellow fever, Zika, Animals, Humans, Arboviruses, Aedes, Arbovirus Infections, Yellow Fever, Zika Virus Infection, Chikungunya Fever, Zika Virus, Mosquito Vectors, Dengue

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BMC Infect Dis

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Springer Science and Business Media LLC
UK Medical Research Council Career Development Award (MR/V031112/1, MR/V031112/1)
Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2022R1A6A3A03061207)
MOE/NUS (A-0006111-00-00)
MRC Centre for Global Infectious Disease Analysis (MR/R015600/1, MR/R015600/1)
Wellcome Trust (grant number 220211)
National Science Foundation (DEB-2011147 with Fogarty International Center)
National Institutes of Health (R35GM133439, R01AI168097, R01AI102918)
Verena ( (NSF BII 2021909 and NSF BII 2213854)
European Union’s Horizon Europe research and innovation programme (No 101057554 and No 101060568)