Data documentation

This section provides detailed documentation of the data available for the Sprint, including table descriptions and other essential information for data analysis and modelling.

The data were uploaded to an FTP server. There are several ways to access the data on an FTP server. We will propose some here:

1- Using FileZilla

1. Download the FileZilla app from the official website: https://filezilla-project.org/.
2. Open the application, enter info.dengue.mat.br in the Host field, and click the button to connect.

If the message below appears to you, just click ok:

3. Open the data_sprint_2025 folder to visualize the available datasets.

4. To download all the datasets in the folder (described in detail in this document) right-click on the folder and click on the Download option:

2 - Using FTPWeb

1. Open the link: https://www.ftpweb.com.br/index2.php and fill server= info.dengue.mat.br, user = anonymous and password= anonymous@domain.com.

2. Open the data_sprint_2025 folder to visualize the available datasets and download them.

Inside the data_sprint_2025 folder, there are the following files:

• Population: datasus_population_2001_2024.csv.gz,
• Environmental: environ_vars.csv.gz,
• Ocean temperature indicators: enso.csv.gz, iod.csv.gz, pdo.csv.gz,
• Shapefile of the cities: shape_muni.gpkg,
• Link between each city and its regional and health region and macroregion: map_regional_health.csv
• Weekly time series of dengue cases: dengue.csv.gz
• Weekly time series of climatic variables: climate.csv.gz
• Monthly time series of climate variable forecasts: climate_forecast.csv.gz

Each of these datasets is described in detail below.

Disease data

Period: epiweek 201001 to epiweek 202517¹.

Aggregation: cases aggregated by the epidemiological week of dengue symptom onset and by municipality.

File: dengue.csv.gz.

Sources: from SINAN and IBGE, organized by Infodengue.

Note: Data for the state of ES are not available due to reporting issues.

Table 1. Description of the columns in dengue.csv.gz

Climate — reanalysis

Reanalysis of hourly data from ERA5, summarized by week by the Mosqlimate project.

Period: epiweek 201001 to epiweek 202517⁴.

Aggregation: temperature, humidity, and precipitation, originally by hour, were first aggregated by day (min, max, mean), and these daily measures were aggregated by epidemiological week (mean).

File: climate.csv.gz.

Sources: Copernicus ERA5, organized by Mosqlimate.

Table 2. Description of the columns of climate.csv.gz. The daily values of these variables are available in the mosqlimate API. *Atmospheric pressures are given as if the place were at sea level.

Climate Forecast

Seasonal forecasts (up to six months ahead) of climate variables from Copernicus, generated using System 51 by the ECMWF center.

Period: January 2010–April 2025.

File: climate_forecast.csv.gz.

Sources: Copernicus.

Table 3. Description of the columns of climate_forecast.csv.gz.

Ocean temperature and level oscillations

Period: 1993-01-04 — 2025-03-03 (weekly).

File: ocean_climate_oscillations.csv.gz.

Sources: https://sealevel.jpl.nasa.gov/.

Table 4. Description of the columns of ocean_climate_oscillations.csv.gz.

Environmental data

Environmental characteristics of the municipalities. Other variables can be aggregated as necessary.

Period: 2010 (koppen) and 2024 (biome).

File: environ_vars.csv.gz.

Sources: IBGE, Embrapa.

Table 5. Description of the columns of environ_vars.csv.gz.

Demographic data

Table 6. Geometry of cities in shape_muni.gpkg (source = IBGE).

Table 7. Link between each city and its regional and macroregional health center in map_regional_health.csv (source = IBGE).

Table 8. Population data (source: SVS). Files with population by city and year (2001 - 2024) in datasus_population_2001_2024.csv.gz

Additional datasets

• REGIC: The publication Regiões de Influência das Cidades 2018 presents the research conducted to identify the hierarchy and areas of influence of Brazilian cities, describes the general characteristics of the detected urban network, and includes thematic analyses to highlight specific features of cities within this network. Available here: https://www.ibge.gov.br/geociencias/cartas-e-mapas/redes-geograficas/15798-regioes-de-influencia-das-cidades.html.

• EPISCANNER: This dataset contains estimates of the epidemiological parameters of reproduction number, peak week, epidemic duration, and size of the epidemic for all the Brazilian cities between 2010 and 2025. The methodology to obtain these estimates is available here: https://arxiv.org/abs/2407.21286. There is a dashboard showing the estimates: https://info.dengue.mat.br/epi-scanner/, and this data can be downloaded using the mosqlimate API: https://api.mosqlimate.org/docs/datastore/GET/episcanner/.

• NVDI (Normalized Difference Vegetation Index) from BDC (Brazil Data Cube): The BDC is a research, development, and technological innovation project of the National Institute for Space Research (INPE), Brazil. It is producing data sets from big volumes of medium-resolution remote sensing images for the entire national territory and developing a computational platform to process and analyze these data sets using artificial intelligence, machine learning, and image time series analysis. It has your library to get the images. A tutorial to get an image in Python and process it (based on a bbox region) is available here: https://github.com/brazil-data-cube/code-gallery/blob/master/jupyter/Python/stac/stac-image-processing.ipynb. There is also this tutorial: https://github.com/brazil-data-cube/code-gallery/blob/master/jupyter/Python/wtss/wtss-introduction.ipynb explaining how to retrieve a time series of these indicators for a specific latitude and longitude coordinates.