Publication Information: Umeå universitet, Avdelningen för hållbar hälsa
Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom; Centre for Mathematical Modelling of Infectious Diseases, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
High Meadows Environmental Institute, Princeton University, NJ, Princeton, United States
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, United Kingdom
School of Public Health and Community Medicine, Sahlgrenska Academy, Institute of Medicine, Global Health, University of Gothenburg, Gothenburg, Sweden; Population Health Research Section, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
Dengue Branch, Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
Department of Biology, University of Oxford, Oxford, United Kingdom
School of Geography and the Environment, University of Oxford, Oxford, United Kingdom; Oxford School of Global and Area Studies, University of Oxford, Oxford, United Kingdom
Department of Biology, Stanford University, CA, Stanford, United States
Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
Institute for Health Metrics and Evaluation, University of Washington, WA, Seattle, United States; Department of Health Metrics Sciences, School of Medicine, University of Washington, WA, Seattle, United States
Department of Geography and Emerging Pathogens Institute, University of Florida, FL, Gainesville, United States
Department of Genetics, University of Cambridge, Cambridge, United Kingdom
Abstract: 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 ...
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