bsc-es.r-universe.dev

[bsc-es] epidesc 0.1.0

carles.milagarcia@bsc.es (Carles Milà) — Mon, 10 Aug 2026 07:28:08 GMT

Provides tools to easily compute a series of epidemiological indicators to characterise different transmission profiles of infectious diseases with a simple pipeline: format the dates in epiyearweek format, choose the descriptors and their parameters, and compute them. The package is based on the publication 'How heterogeneous is the dengue transmission profile in Brazil? A study in six Brazilian states' by Ferreira de Almeida et al (2002) .

[carlesmila] clim4health 0.1.0

carles.milagarcia@bsc.es (Carles Milà) — Tue, 30 Jun 2026 21:10:20 GMT

Obtain, transform and export climate data including reanalyses, (seasonal) forecasts and hindcasts, and weather stations for their use in epidemiological analyses. It is organised in three sequential blocks, input (download and load data), transform (downscaling, verification, spatiotemporal aggregation and threshold-based indicators) and output (visualising and exporting data). Downscaling methods include those described in Duzenli et al. (2026) and verification methods are based on those in Manubens et al. (2018) .

[carlesmila] GHRexplore 0.2.2

carles.milagarcia@bsc.es (Carles Milà) — Thu, 12 Feb 2026 09:09:07 GMT

A collection of commonly used visualizations of temporal and spatio-temporal health data including case counts, incidence rates, and covariates. The available plot types include time series, heatmaps, seasonality plots, maps and more. The package supports standard data transformations such as temporal and spatial aggregations, while offering extensive customization options for the resulting figures.

[carlesmila] GHRmodel 0.1.1

carles.milagarcia@bsc.es (Carles Milà) — Fri, 07 Nov 2025 16:41:19 GMT

Supports modeling health outcomes using Bayesian hierarchical spatio-temporal models with complex covariate effects (e.g., linear, non-linear, interactions, distributed lag linear and non-linear models) in the 'INLA' framework. It is designed to help users identify key drivers and predictors of disease risk by enabling streamlined model exploration, comparison, and visualization of complex covariate effects. See an application of the modelling framework in Lowe, Lee, O'Reilly et al. (2021) .