Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET)
Purpose and Methods
About COVID-NET
Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance
Network (COVID-NET) is a population-based surveillance system that collects
data on laboratory-confirmed COVID-19-associated hospitalizations among
children and adults through a network of over 250 acute-care hospitals in 14
states.
Why COVID-NET Data Is Important
COVID-NET is CDC’s source for important data on hospitalization rates
associated with COVID-19. Hospitalization rates are updated weekly. COVID-NET
also provides important clinical information on COVID-19-associated
hospitalizations, including age group, sex, race/ethnicity and underlying
health conditions.
Hospitalization Rates
Patient Characteristics
COVID-NET Data Collection, Timeliness and Interpretation
Cases are identified in COVID-NET if they test positive for SARS-COV-2 (the
virus that causes COVID-19 disease) through a test ordered by a health care
professional and are hospitalized within 14 days of the positive test.
Clinical data are collected using a standardized case reporting form by
trained surveillance officers, so the data are collected in a standardized and
uniform way.
COVID-NET is expected to collect the following data for each case:

age
sex
surveillance site
date of hospital admission
evidence of positive SARS-CoV-2 test
demographic information (e.g., race, ethnicity)
clinical (medical) history (underlying health conditions)
clinical course (progression of the illness such as admission to an intensive care unit)
medical interventions (medical care for the illness such as need for mechanical ventilation)
outcomes (e.g., released from the hospital, death)

How COVID-NET Hospitalization Data Is Different from Hospitalizations
Reported in National and State Case Counts
COVID-NET differs from hospitalizations reported in national and state case
counts in two ways. First, state and national COVID-19 case reporting are
based on all people who test positive for COVID-19 in the United States.
COVID-NET is limited to COVID-19-associated hospitalizations captured in the
COVID-NET surveillance area. Second, COVID-NET reports rates and not just
counts. These rates show how many people are hospitalized with COVID-19 in the
surveillance area, compared to the entire number of people residing in that
area.
How COVID-NET Calculates Hospitalization Rates
Hospitalization rates are calculated by the number of residents of a defined
area who are hospitalized with a positive SARS-CoV-2 laboratory test divided
by the total population within that defined area.
COVID-NET coverage
COVID-NET comprises 99 counties in the 14 states participating in the
Emerging Infections Program (EIP) and the
Influenza Hospitalization Surveillance Project (IHSP). Participating states
include: California, Colorado, Connecticut, Georgia, Iowa, Maryland, Michigan,
Minnesota, New Mexico, New York, Ohio, Oregon, Tennessee, and Utah.
COVID-NET covers approximately 10 percent of the U.S. population. The counties
covered are located in all 10 Health and Human Services (HHS)
regions.
The designated COVID-NET surveillance area is generally similar to the U.S.
population by demographics; however, the information might not be
generalizable to the entire country.
Accessing COVID-NET Data
COVID-19-associated hospitalization rates are reported to CDC on a weekly
basis. COVID-NET data, including hospitalization rates for different age
groups and data on
patient characteristics,
are available on COVIDView 
COVID-NET hospitalization data are preliminary and may change as more data
is received. In particular, case counts and rates for recent hospital
admissions are subject to lag. As data are received each week, prior case
counts and rates are updated accordingly.
COVID-NET Publications

Study: Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019 – COVID-NET, 14 States, March 1-30, 2020. MMWR Morb Mortal Wklyl Rep 2020; 69:458-464