Fetching ERVISS Data

Introduction

The ervissexplore package provides easy access to all data published in the EU-ECDC Respiratory Viruses Weekly Data repository. Data is returned as data.table objects, ready for your own analysis.

library(ervissexplore)

Available data sources

The package supports 7 data sources from ERVISS:

Type	Function	CSV file	Description
`"positivity"`	`get_sentineltests_positivity()`	`sentinelTestsDetectionsPositivity.csv`	Sentinel test positivity rates by pathogen
`"variants"`	`get_erviss_variants()`	`variants.csv`	SARS-CoV-2 variant proportions
`"ili_ari_rates"`	`get_ili_ari_rates()`	`ILIARIRates.csv`	ILI/ARI consultation rates by age group
`"sari_rates"`	`get_sari_rates()`	`SARIRates.csv`	SARI rates by age group
`"sari_positivity"`	`get_sari_positivity()`	`SARITestsDetectionsPositivity.csv`	SARI virological data (tests, detections, positivity)
`"nonsentinel_severity"`	`get_nonsentinel_severity()`	`nonSentinelSeverity.csv`	Non-sentinel severity (deaths, hospitalisations, ICU)
`"nonsentinel_tests"`	`get_nonsentinel_tests()`	`nonSentinelTestsDetections.csv`	Non-sentinel tests and detections

Each source has a dedicated get_*() function, or you can use the generic get_erviss_data(type = ...) function.

Sentinel test positivity

Positivity rates for respiratory pathogens from sentinel surveillance.

data <- get_sentineltests_positivity(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31"),
  pathogen = "SARS-CoV-2",
  countries = c("France", "Germany", "Italy"),
  indicator = "positivity"
)

head(data)

You can filter on multiple pathogens at once:

data <- get_sentineltests_positivity(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-06-30"),
  pathogen = c("SARS-CoV-2", "Influenza", "RSV"),
  indicator = "detections"
)

SARS-CoV-2 variants

Variant proportions and detection counts.

data <- get_erviss_variants(
  date_min = as.Date("2025-06-01"),
  date_max = as.Date("2025-12-31"),
  variant = c("XFG", "LP.8.1"),
  countries = c("France", "Belgium"),
  indicator = "detections"
)

# Filter variants with a minimum proportion
data <- get_erviss_variants(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31"),
  min_value = 5,
  indicator = "proportion"
)

ILI/ARI consultation rates

ILI (Influenza-Like Illness) and ARI (Acute Respiratory Infection) consultation rates from primary care, stratified by age group.

# Get ILI consultation rates
data <- get_ili_ari_rates(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31"),
  indicator = "ILIconsultationrate",
  countries = "France"
)

# Get both ILI and ARI rates for specific age groups
data <- get_ili_ari_rates(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31"),
  age = c("0-4", "65+")
)

SARI rates

SARI (Severe Acute Respiratory Infection) hospitalisation rates, stratified by age group.

data <- get_sari_rates(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31"),
  age = c("0-4", "15-64", "65+"),
  countries = c("France", "Belgium")
)

SARI virological data

Tests, detections, and positivity rates from SARI virological surveillance.

# Get positivity for Influenza
data <- get_sari_positivity(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31"),
  pathogen = "Influenza",
  indicator = "positivity",
  countries = "Belgium"
)

# Get detections for all pathogens
data <- get_sari_positivity(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31"),
  indicator = "detections"
)

Non-sentinel severity

Hospital admissions, ICU admissions, ICU inpatients, hospital inpatients, and deaths from non-sentinel sources.

# Get hospital admissions for SARS-CoV-2
data <- get_nonsentinel_severity(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31"),
  pathogen = "SARS-CoV-2",
  indicator = "hospitaladmissions",
  countries = "France"
)

# Get multiple severity indicators
data <- get_nonsentinel_severity(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31"),
  pathogen = "SARS-CoV-2",
  indicator = c("hospitaladmissions", "ICUadmissions", "deaths")
)

Non-sentinel tests and detections

Tests and detections from non-sentinel virological surveillance.

data <- get_nonsentinel_tests(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31"),
  pathogen = "Influenza",
  indicator = "detections",
  countries = c("France", "Germany")
)

Using the generic function

Instead of remembering each specific function name, you can use get_erviss_data() with the type parameter:

# These two calls are equivalent:
data <- get_sentineltests_positivity(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31"),
  pathogen = "SARS-CoV-2"
)

data <- get_erviss_data(
  type = "positivity",
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31"),
  pathogen = "SARS-CoV-2"
)

This makes it easy to switch between data sources while keeping the same workflow:

types <- c("positivity", "sari_positivity", "nonsentinel_tests")

results <- lapply(types, function(t) {
  get_erviss_data(
    type = t,
    date_min = as.Date("2024-01-01"),
    date_max = as.Date("2024-12-31"),
    pathogen = "Influenza",
    countries = "Belgium"
  )
})

names(results) <- types

Historical snapshots

All functions support fetching historical snapshots for reproducible analyses. The ERVISS repository stores weekly snapshots of the data, so you can retrieve the exact data that was available at a given date.

# Fetch a specific snapshot
data <- get_sentineltests_positivity(
  date_min = as.Date("2023-01-01"),
  date_max = as.Date("2023-12-31"),
  use_snapshot = TRUE,
  snapshot_date = as.Date("2024-02-23")
)

This works with all data sources:

data <- get_erviss_data(
  type = "nonsentinel_severity",
  date_min = as.Date("2023-01-01"),
  date_max = as.Date("2023-12-31"),
  pathogen = "SARS-CoV-2",
  indicator = "hospitaladmissions",
  use_snapshot = TRUE,
  snapshot_date = as.Date("2024-02-23")
)

To see available snapshot dates, visit the EU-ECDC snapshots directory.

URL helpers

If you prefer to download the data files yourself, you can retrieve the URLs directly:

# Latest data URLs
get_erviss_url("positivity")
#> [1] "https://raw.githubusercontent.com/EU-ECDC/Respiratory_viruses_weekly_data/refs/heads/main/data/sentinelTestsDetectionsPositivity.csv"
get_erviss_url("ili_ari_rates")
#> [1] "https://raw.githubusercontent.com/EU-ECDC/Respiratory_viruses_weekly_data/refs/heads/main/data/ILIARIRates.csv"
get_erviss_url("nonsentinel_severity")
#> [1] "https://raw.githubusercontent.com/EU-ECDC/Respiratory_viruses_weekly_data/refs/heads/main/data/nonSentinelSeverity.csv"

# Snapshot URL
get_erviss_url(
  "variants",
  use_snapshot = TRUE,
  snapshot_date = as.Date("2023-11-24")
)
#> [1] "https://raw.githubusercontent.com/EU-ECDC/Respiratory_viruses_weekly_data/refs/heads/main/data/snapshots/2023-11-24_variants.csv"

Each source also has a dedicated URL function (e.g., get_sentineltests_positivity_url(), get_ili_ari_rates_url(), etc.).

Using a local CSV file

If you have already downloaded the data locally, you can pass the file path directly:

data <- get_sentineltests_positivity(
  csv_file = "path/to/sentinelTestsDetectionsPositivity.csv",
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-12-31")
)

Analyzing the data

All functions return data.table objects. You can use data.table syntax or convert to a data.frame / tibble for your preferred workflow:

data <- get_sentineltests_positivity(
  date_min = as.Date("2024-01-01"),
  date_max = as.Date("2024-06-30"),
  pathogen = c("SARS-CoV-2", "Influenza")
)

# data.table syntax
data[,
  .(
    mean_positivity = mean(value, na.rm = TRUE),
    max_positivity = max(value, na.rm = TRUE),
    n_weeks = .N
  ),
  by = .(countryname, pathogen)
]

# Or convert to tibble for dplyr
# tibble::as_tibble(data)