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

Abstract

AbstractBackgroundAedes (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 differentAedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used.MethodsWe 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).ResultsWe found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 183 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%, 33/148) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc) and only 48% of studies assessed predictive performance via out-of-sample validation procedures.ConclusionsHere we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We outline specific recommendations for future studies regarding aims and data choice, covariate selection, model formulation and evaluation.Author SummaryAedes-borne arboviruses such as dengue, Zika, chikungunya, and yellow fever pose a growing global threat. It is crucial to map their risk to target interventions and control their spread. A review of 183 studies found that risk mapping methods have evolved over time to respond to changing epidemiology and data availability. Initially, mapping risk involved using data from multiple areas and satellite imagery to develop models predicting transmission risk on a global or continental scale. Following Zika and chikungunya epidemics, mechanistic models based on national-level incidence data have been utilised to track the spread of epidemics across countries. The use of case-based surveillance systems has enabled more precise and detailed predictions at sub-national levels. Of the studies reviewed, half included temperature and rainfall as covariates, and human mobility was increasingly accounted for in arbovirus risk mapping. However, only 33 of the 148 studies robustly selected the variables included in their predictions, and only half of the studies assessed their accuracy against new data. The review suggests that future risk mapping studies should consider the purpose of the map, data quality, and methodological innovations to improve accuracy of risk maps to ensure they are useful for informing control ofAedes-borne arboviruses.