Doctor Visits
This indicator estimates the percentage of outpatient doctor’s visits that are due to COVID-like symptoms, based on data provided to us by health system partners. Tele-medicine visits are included in these estimates.
The COVID-19 indicators visualized on our map are derived from the data sources described below. These are all publicly available on the COVIDcast endpoint of our public Epidata API. The API documentation includes full technical detail on how these indicators are calculated.
This indicator estimates the percentage of outpatient doctor’s visits that are due to COVID-like symptoms, based on data provided to us by health system partners. Tele-medicine visits are included in these estimates.
This indicator estimates the percentage of daily hospital admissions that have diagnostic codes related to COVID-19, based on medical claims summaries provided to us by health system partners.
This indicator estimates the percentage of people who have a COVID-like illness (fever, along with cough, or shortness of breath, or difficulty breathing), based on symptom surveys run by Carnegie Mellon. The surveys ask respondents how many people in their household are experiencing COVID-like symptoms, among other questions. Facebook directs a random sample of its users to these surveys, which are voluntary. Individual survey responses are held by CMU and are shareable with other health researchers under a data use agreement. No individual survey responses are shared back to Facebook. As of mid-June, about 70,000 such surveys were completed daily throughout the U.S.
This indicator estimates the percentage of people who know someone in their community with a COVID-like illness (fever, along with cough, or shortness of breath, or difficulty breathing). The data is based on the same CMU-run survey, advertised by Facebook, as is used for the Symptoms indicator. Note that more people tend to report knowing someone in their community with a COVID-like illness than having someone in their own household with a COVID-like illness, so these numbers are higher than the household symptoms survey.
These indicators estimate the proportion of people who spend time outside their homes during daytime hours, using mobile device location data provided by SafeGraph. “Away from Home 6hr+” is the proportion spending more than 6 hours outside their home, while “Away from Home 3-6hr” is the proportion spending between 3 and 6 hours outside their home. These estimates may be related to the spread of COVID-19, since they are related to the number of people interacting with others outside their homes, and also reveal the impact of the pandemic and movement restrictions.
This indicator estimates the percentage of COVID-19 antigen tests that come back positive, based on testing data provided to us by Quidel, Inc., a company that makes equipment and kits for medical tests. When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, doctors may order an antigen test. An antigen test can detect parts of the virus that are present during an active infection. Note that this data may differ from testing figures reported by state and local health authorities, as it only includes Quidel’s COVID-19 antigen tests, not those from other testing providers.
This indicator is based on the number of Google searches for COVID-related topics, relative to each area’s population, based on Google search statistics provided to us by Google’s Health Trends group. A larger number corresponds to more COVID-related searching.
The “Combined” map represents a combination of all the indicators currently featured on the public map. As of this writing, this includes Doctor Visits, Symptoms (Facebook), Symptoms in Community (Facebook), and Search Trends. It does not include official reports (cases and deaths), hospital admissions, or SafeGraph signals. We use a rank-1 approximation, from a nonnegative matrix factorization approach, to identify an underlying signal that best reconstructs the indicators. Higher values of the combined signal correspond to higher values of the other indicators, but the scale (units) of the combination is arbitrary.
These indicators show the number of new confirmed COVID-19 cases per day. The maps reflect only cases confirmed by state and local health authorities. They are based on confirmed case counts compiled and made public by a team at Johns Hopkins University and by USAFacts. We use Johns Hopkins data for Puerto Rico and report USAFacts data in all other locations.
These indicators shows the number of COVID-19 related deaths per day. The maps reflect official figures by state and local health authorities, and may not include excess deaths not confirmed as due to COVID-19 by health authorities. They are based on confirmed death counts compiled and made public by a team at Johns Hopkins University and by USAFacts. We use Johns Hopkins data for Puerto Rico and report USAFacts data in all other locations.
(Paused) This indicator estimates the percentage of people who know someone in their community with a COVID-like illness (fever, along with cough, or shortness of breath, or difficulty breathing). The data is based on Google-run symptom surveys, through publisher websites, Google’s Opinions Reward app, and similar applications. These surveys are voluntary. As of mid April, about 600,000 people answered the survey daily throughout the U.S. Note that these Google surveys are estimating a different quantity than the surveys given to Facebook users (percentage of people who know someone in their community who is sick, rather than percentage of people who are sick), so the estimates from the Google surveys tend to be larger.
(Archived) This indicator is based on data about influenza lab tests provided to us by Quidel, Inc., a company that makes equipment and kits for medical tests. When a patient (whether at a doctor’s office, clinic, or hospital) has COVID-like symptoms, standard practice currently is to perform a conventional influenza test to rule out seasonal influenza (flu), because these two diseases have similar symptoms. While the number of COVID tests performed depends on local capacity and testing policy, influenza testing is not influenced by these factors. Because a different number of labs may report on different days, we track the average number of flu tests performed per flu testing device (in a given location and on a given day).
Full technical documentation on the sources of our data, and how our estimates are constructed, is available in the COVIDcast API data source documentation.
The real-time COVID-19 indicators presented on the COVIDcast site represent our best estimates given all data that we have available up until now. For example, the estimates on our site for April 24, 2020 represent our current best estimate of the indicator values for that day. The first estimates for the indicator values for April 24 would typically be available on April 25 (one day later), but estimates for these April 24 values may be updated on later days as new data becomes available. This phenomenon is particularly prominent with the Doctor Visits indicator, which is based on doctor’s visits that do or do not involve COVID-like illnesses: there is generally a lag in how some of the data is made available to us, and a large fraction of doctor’s visits on any day is only reported to us several days later. For that reason, our Doctor Visits estimates that are just a few days old may be less reliable. When we deem them too unreliable, we do not post them, which is why this indicator is often available only up until a few days before the current day.
For each indicator, our estimates are formed using data smoothing techniques. The individual smoothing technique differs based on the indicator, but in all cases, we perform some kind of data smoothing (akin to averaging, or weighted averaging) across an approximately one week window.
Generally, we do not report estimates at locations with insufficient data (or insufficiently recent data). The Search Trends indicator is not available at the county level, as data is only available at a coarser geographic resolution in the first place. For the Doctor Visits and Facebook Surveys indicators, we lump together all counties in a given state that do not have sufficient data for their own individual estimate, and create a “rest of state” estimate that includes all of them.
The “Intensity” view presents a heat map of these estimates. For each indicator, we use a fixed range of values, from a “low” value to a “high” value, and assign a color to each value in between, as shown to the left of the map. These “low” and “high” values are different for each indicator, but for a given indicator, they are constant across time and geographic hierarchy, meaning that the heat maps are comparable across days. At the county level, the “rest of state” estimates are plotted in semi-transparent colors, to make the individual counties where estimates are made more easily visually distinguishable.