Abstract

Extreme weather events, environmental degradation, and social inequalities are increasing the vulnerability of communities in climate change hotspots to infectious disease outbreaks. However, the data required to assess and respond to these risks are complex and difficult to integrate. Furthermore, tools for spatiotemporal modelling and operational climate and health early warning systems remain limited for frontline decision-makers.

To streamline this process, the Global Health Resilience (GHR) group, in partnership with an international team of researchers and practitioners, is developing GHRtools, a suite of R packages to enhance anticipatory action planning. These tools are designed within an open-source framework and have been co-designed and tested in collaboration with partners in Brazil, Colombia, Peru, Dominican Republic, Barbados and South Sudan (within the HARMONIZE and IDExtremes projects).

The digital tools are structured into five interconnected R packages:
* data4health: Supports the processing of line-list disease data. It offers standard R-based functions to clean, check and aggregate data, as well as a R-shiny interface for users with no coding experience.
* clim4health: Obtains and processes reanalysis, seasonal forecasts and hindcasts, and weather station data. Capabilities include forecast verification, postprocessing, downscaling and spatiotemporal aggregations.
* GHRexplore: Provides a wide variety of visualizations for exploratory analysis of temporal and spatio-temporal climate and health data.
* GHRmodel: Supports modeling health outcomes using Bayesian hierarchical spatio-temporal models with complex covariate effects (e.g., linear, non-linear, lagged, interactions, distributed lag non-linear models) in the R-INLA framework.
* GHRpredict: Computes probabilistic predictions of disease case counts and outbreak risk of models developed in GHRmodel, and evaluates predictive performance via cross-validation.

We present the structure and functionality of these tools as well as some examples of their implementation in operational settings, where observed and forecasted hydrometeorological indicators such as temperature anomalies, drought severity, and extreme precipitation are being used in Bayesian hierarchical spatio-temporal models to predict infectious disease outbreaks months in advance.