This guide explains how to download and work with public health surveillance data from Brazil, Colombia, Peru, and the Dominican Republic. Each section walks you through the available methods, from R packages to direct downloads, so you can get to analysis as quickly as possible.
If you would like another country to be included, please let us know by opening an issue in our GitHub repository.
🇧🇷 Brasil
Brazil’s health surveillance data is managed by the Department of Informatics of the Unified Health System (DATASUS). Four methods are available for accessing anonymised, individual-level records. If you only need summary statistics rather than raw data, the DataSUS tabnet web interface lets you build and export custom tables without any programming.
The earliest year available depends on when a disease was introduced and when notification became mandatory. Disease could be available from 2000 onwards
If you need data from earlier years, nominal data, or data from SIVEP, you must request it from the Ministry of Health through the e-SIC portal. Nominal data require ethical committee approval in most cases.
Method 1: R package microdatasus
Microdatasus is an R package package is the most convenient way to download and preprocess DATASUS data directly in R.
Installing the package
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
library(devtools)
if (!requireNamespace("microdatasus", quietly = TRUE)) {
devtools::install_github("rfsaldanha/microdatasus")
}
library(microdatasus)Downloading data
Use fetch_datasus() to download data. You must specify:
- The temporal extent (
year_start,month_start,year_end,month_end) - The spatial extent (federative units)
- The disease (health information system)
- The variables (default downloads all)
chik <- fetch_datasus(year_start = 2017, year_end = 2019,
information_system = "SINAN-CHIKUNGUNYA")
mal <- fetch_datasus(year_start = 2004, year_end = 2006,
information_system = "SINAN-MALARIA")
den <- fetch_datasus(year_start = 2011, year_end = 2013,
information_system = "SINAN-DENGUE")
zika <- fetch_datasus(year_start = 2016, year_end = 2021,
information_system = "SINAN-ZIKA")Preprocessing data
Each disease has a dedicated preprocessing function that tidies the raw data for analysis. These functions:
- Assign human-readable labels to categorical fields (e.g., CS_RACA becomes Branca, Preta, Amarela instead of 1, 2, 3)
- Converts variables to the correct formats
- convert the age variable NU_IDADE_N into separate, easy-to-use columns: IDADEminutos, IDADEhoras, IDADEdias, IDADEmeses, and IDADEanos.
chik2 <- process_sinan_chikungunya(chik)
mal2 <- process_sinan_malaria(mal)
den2 <- process_sinan_dengue(den)
zika2 <- process_sinan_zika(zika)Your data is now ready for analysis.
Method 2: FTP access
DATASUS also shares its data via FTP, which you can access using any FTP client. FileZilla is a free and widely used option. Connect using these settings:
- Host: ftp.datasus.gov.br
- Remote site: /dissemin/publicos/SINAN/
Once connected, navigate into the DADOS folder. You will find two subfolders:
- FINAIS: revised, finalised historical data
- PRELIM: preliminary, unrevised data for the most recent year(s)
If a dataset appears in FINAIS, it will not appear in PRELIM. Navigate to the disease folder of interest, then download the file for the year you need.
Method 3: DATASUS website and command line
The Ministry of Health provides a web interface for browsing available files. Fill in the fields as follows:
- Fonte: SINAN - Sistema de Informações de Agravos de Notificação
- Modalidade: Dados
- Tipo de Arquivo: Select the disease of interest
- Ano: Select the desired years
- UF: BR
Click Enviar to generate the file list. To download a file, right-click it, copy the link address, and run the following in your terminal (substituting the correct URL and destination path):
Method 4: Python package PySUS
If you prefer Python, PySUS provides equivalent functionality for downloading DATASUS data.
Installing PySUS
Downloading data
Optionally, set a persistent cache directory so downloaded files are saved to a location of your choice:
Then, in Python:
from pysus.preprocessing.sinan import SINAN
SINAN.list_diseases()
SINAN.get_available_years('zika')
df = SINAN.download(2019, 'Zika')Downloaded files are saved as parquet files in the specified folder.
🇨🇴 Colombia
Colombia’s national public health surveillance system, Sistema Nacional de Vigilancia en Salud Pública (SIVIGILA) is coordinated by the Instituto Nacional de Salud in collaboration with the Ministerio de Salud y Protección Social. It collects, analyses, and disseminates information on health events that may affect the population, supporting public health planning and response across the country.
SIVIGILA’s reporting network includes thousands of healthcare providers who report on more than one hundred monitored conditions, enabling authorities to track disease trends and detect outbreaks.
The SIVIGILA microdata portal publishes aggregated data at municipality resolution, without georeferencing or sensitive clinical data. Publication is typically delayed by 6 to 10 months. A public dashboard is also available with a shorter delay, but it is less stable and harder to extract data from programmatically.
Two methods are available for accessing Colombia’s surveillance data.
Method 1: Manual download via SIVIGILA
Visit the Buscador de microdatos and follow these steps:
- In “Seleccione el evento de interés”, choose the disease or health event you want.
- In “Seleccione año”, choose the year.
- Click “Buscar” to generate the dataset.
If this is your first time using the portal, you may be asked for your name and the reason for the data request. Complete the form to proceed. Once the results appear, download the dataset as an Excel file.
Method 2: R package sivirep
The sivirep package makes it easy to download SIVIGILA surveillance data directly in R.
Installing sivirep
install.packages(
"sivirep",
repos = c("https://epiverse-trace.r-universe.dev", "https://cloud.r-project.org")
)
library(sivirep)Downloading data
List all available diseases and health events:
Download data for a specific event using import_data_event(). For example, to download Zika data for 2023:
🇵🇪 Peru
Peru’s public health surveillance system is managed by the Centro Nacional de Epidemiología, Prevención y Control de Enfermedades (CDC Peru). Data are collected through RENACE (Red Nacional de Epidemiología), a nationwide reporting network of over 10,000 health facilities. Cases of notifiable diseases are reported through this network, allowing national authorities to monitor trends and guide public health interventions. Data are made available through two main channels.
Method 1: Datos abiertos
Go to National Platform of Open Data (PNDA) and search for your disease of interest.
At the bottom of the page you will find the file available for download.
🇩🇴 Dominican Republic
Surveillance data in the Dominican Republic is managed by the Dirección de Epidemiología (DIEPI), the national epidemiology directorate within the Ministry of Public Health. DIEPI operates SINAVE (Sistema Nacional de Vigilancia Epidemiológica), the national surveillance system covering individual reporting of all notifiable diseases.
While microdata are not publicly available, DIEPI publishes weekly epidemiological bulletins with aggregated surveillance data, which may be sufficient for some purposes.
To request access to line-list data, you will need to contact DIEPI directly through their website or via the Ministry of Public Health (msp.gob.do).