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List malaria layers by species & metric from Malaria Atlas Project

Source: R/l4h_layers_available_malaria.R
l4h_layers_available_malaria.Rd

Provides a tidy listing of all malaria data layers in the Malaria Atlas Project GeoServer, including separate columns for species (Plasmodium falciparum vs. Plasmodium vivax), year, month, and metric (e.g. incidence_rate, parasite_rate, mortality_count, etc.). These layers cover modeled estimates of malaria prevalence, incidence, mortality and more, at global scale, broken down by species and time.

[Stable]

Usage

l4h_layers_available_malaria(year = NULL, species = NULL, measure = NULL)

Arguments

year

Integer or integer vector. Optional filter on dataset year (e.g. 2024).

species

Character or character vector. Optional filter on species:

  • "plasmodium falciparum"

  • "plasmodium vivax"

measure

Character or character vector. Optional filter on metric type: e.g. "incidence_rate", "parasite_rate", "mortality_count", "reproductive_number".

Value

A tibble with columns:

  • workspace — always "malaria"

  • year — dataset year

  • month — dataset month (NA if annual)

  • species — full species name (lowercase)

  • measure — metric type (lowercase)

  • dataset_id — original coverage ID for subsequent WMS/WCS requests

Details

The Malaria Atlas Project https://malariaatlas.org/ provides globally modeled raster surfaces of key malaria indicators. This function retrieves all available coverage IDs from the GeoServer, parses out the species, time, and metric, and returns them in a user-friendly table for easy filtering and selection.

Credits

Pioneering geospatial health analytics and open‐science tools. Developed by the Innovalab Team, for more information send a email to imt.innovlab@oficinas-upch.pe

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Examples

# \donttest{
# list all available malaria layers
all_layers <- l4h_layers_available_malaria()
head(all_layers)
#> # A tibble: 6 × 6
#>   workspace  year month species               measure         dataset_id        
#>   <chr>     <int> <int> <chr>                 <chr>           <chr>             
#> 1 malaria    2022     6 plasmodium falciparum mortality_count Malaria__202206_G…
#> 2 malaria    2024     6 plasmodium falciparum mortality_count Malaria__202406_G…
#> 3 malaria    2025     8 plasmodium falciparum mortality_count Malaria__202508_G…
#> 4 malaria    2022     6 plasmodium falciparum mortality_rate  Malaria__202206_G…
#> 5 malaria    2024     6 plasmodium falciparum mortality_rate  Malaria__202406_G…
#> 6 malaria    2025     8 plasmodium falciparum mortality_rate  Malaria__202508_G…

# filter for Plasmodium falciparum parasite rate in 2024
pf_pr_2024 <- l4h_layers_available_malaria(
  year    = 2024,
  species = "plasmodium falciparum",
  measure = "parasite_rate"
)
print(pf_pr_2024)
#> # A tibble: 1 × 6
#>   workspace  year month species               measure       dataset_id          
#>   <chr>     <int> <int> <chr>                 <chr>         <chr>               
#> 1 malaria    2024     6 plasmodium falciparum parasite_rate Malaria__202406_Glo…
# }