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

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.

Fig 1. The main types of data currently used for arbovirus disease mapping.

For each data type, the figure summarises relative abundance and quality of data (bias, representativeness and consistency among areas and over time) (as assessed by author consensus, both rated 1–5), the main questions each data type aims to answer and associated public health actions that can be undertaken with such knowledge. Use cases that can draw on multiple categories are labelled in bi-directional arrows at the bottom of the figure.

Fig 2. Conceptual overview of a joint inference mapping approach showing example occurrence, incidence and seroprevalence data for Brazil (top row), the kinds of risk maps that can be generated from each of these data sets independently using current generation methods (middle row) and the time-varying more accurate maps that could be generated from a joint-inference modelling approach (bottom row, uses a simple equal weight ensemble for illustration purposes only).