Respiratory Virus Hospitalization Surveillance Network (RESP-NET)

Interactive Dashboard

Purpose

The Respiratory Virus Hospitalization Surveillance Network (RESP-NET) monitors laboratory-confirmed hospitalizations associated with influenza, COVID-19, and respiratory syncytial virus (RSV) among children and adults.
Respiratory Virus Hospitalization Surveillance Network

Overview

RESP-NET is made up of three networks that monitor laboratory-confirmed hospitalizations associated with influenza (FluSurv-NET), COVID-19 (COVID-NET), and RSV (RSV-NET).

RESP-NET Interactive Dashboard

The rates presented on the RESP-NET interactive dashboard can be used to follow trends and comparisons of influenza, COVID-19, and RSV-associated hospitalizations in different demographic groups and over time.

How to use the RESP-NET interactive dashboard‎

1. Select a topic of interest
To use the RESP-NET interactive dashboard, select a topic to see specific data trends. Topics include rates by season, site, age group, sex, or race and ethnicity. Hospitalizations can be viewed as weekly, monthly, or cumulative rates.


2. Select a filter of interest
Topics of interest can be filtered by season, site, age group, sex, and race and ethnicity. Filters vary by topic, as not all topics have filters available.


3. Select other ways to view
The data can be displayed in a graph or as a table. Right click anywhere in the graph for a tabular view. Hovering your mouse over or selecting a data point or bar in the graph will display detailed information. Some graphs allow you to hide or show data from the legend for detailed analysis.

This dashboard is updated weekly. Data are preliminary and subject to change as more data become available. Rates for recent hospital admissions are subject to reporting delays that might increase around holidays or during periods of increased hospital utilization. As new data are received each week, previous rates are updated accordingly.

Why RESP-NET data are important

COVID-19, RSV, and influenza-associated hospitalization rates are used to understand trends in virus circulation, estimate disease burden, and respond to outbreaks. Demographic and detailed clinical information, including underlying conditions, allow CDC to better understand hospitalization trends and determine who is most at risk.

Key Concept‎

Population-based surveillance is the active collection, analysis, and interpretation of data on a population in a specified geographic area.

RESP-NET does not collect data on all hospitalizations caused by respiratory illnesses, but it can describe hospitalizations caused by three viruses that account for a large proportion of these hospitalizations. Surveillance is conducted through a network of acute care hospitals in select counties or county equivalents in:

  • 14 states for influenza surveillance.
  • 13 states for COVID-19 surveillance.
  • 13 states for RSV surveillance.

These surveillance platforms cover more than 30 million people and include an estimated 10% of the U.S. population.

The surveillance platforms for these viruses are FluSurv-NET, COVID-NET, and RSV-NET.

Rates presented on the RESP-NET interactive dashboard can be used to follow trends and compare COVID-19, influenza, and RSV-associated hospitalization rates in different demographic groups. This includes data by age, sex, and race and ethnicity, and across seasons and states.

Surveillance in RESP-NET for these viruses relies on clinical testing ordered by a healthcare provider. Hospitalization rates are unadjusted and do not account for undertesting, differing provider or facility testing practices, and diagnostic test sensitivity. The true burden of hospitalizations associated with these viruses in the United States may be greater than what is shown by these numbers.

Additional resources

To see further hospitalization data, visit:

See Emergency Department Visits for Viral Respiratory Illness for a combined view of emergency department visit data for multiple respiratory conditions tracked by the National Syndromic Surveillance Program.