PandemonCAT

PandemonCAT monitors COVID-19 dynamics in Catalonia by integrating georeferenced epidemiological, vaccination, mobility, environmental, and socioeconomic data to support spatiotemporal analysis of incidence and mortality.

Key Features:

• Geospatial coverage and updates: Weekly updates at Catalonia basic health areas (ABSs) with georeferenced maps of health areas and regions.
• Vaccination mapping: Visualization of vaccination campaign progression showing the proportion of the population receiving one or two vaccine doses segmented by age groups.
• Environmental and socioeconomic integration: Inclusion of environmental and socioeconomic variables alongside epidemiological data for combined analysis.
• Mobility data aggregation: Monthly representation of public mobility patterns before, during, and after lockdown phases.
• Standardized epidemiological metrics: Reporting of smoothed standardized COVID-19 infected cases and mortality rates across health areas and regions.
• Software implementation: Developed using open-source R with the Shiny package for data processing and map generation.

Scientific Applications:

• Spatiotemporal epidemiology: Analysis of geographic and temporal patterns of COVID-19 incidence and mortality across ABSs and regions.
• Vaccination impact assessment: Evaluation of vaccination coverage (one versus two doses) by age group and geography to inform vaccination studies.
• Mobility and intervention analysis: Examination of monthly mobility changes across pre-, during-, and post-lockdown phases relative to infection dynamics.
• Environmental and socioeconomic correlation studies: Exploration of associations between environmental/socioeconomic variables and pandemic dynamics.
• Public health decision support: Evidence to inform targeted epidemiological investigations and region-specific public health actions.

Methodology:

Data integration of vaccination, mobility, environmental, socioeconomic, and epidemiological datasets; georeferencing to Catalonia ABSs and regions; weekly data updates; monthly aggregation of mobility for pre/during/post-lockdown comparisons; calculation and reporting of smoothed standardized COVID-19 infected case and mortality rates; implementation in R with the Shiny package.