Covid-19 PA Case Data by County and Region with map and table selections.

Data from Johns Hopkins University (https://www.arcgis.com/home/item.html?id=628578697fb24d8ea4c32fa0c5ae1843)

COVID-19 Cases information is reported through the Pennsylvania State Department’s National Electronic Disease Surveillance System (PA-NEDSS). As new cases are passed to the Allegheny County Health Department they are investigated by case investigators. During investigation some cases which are initially determined by the State to be in the Allegheny County jurisdiction may change, which can account for differences between publication of the files on the number of cases, deaths and tests. Additionally, information is not always reported to the State in a timely manner, delays can range from days to weeks, which can also account for discrepancies between previous and current files. Test and Case information will be updated daily. This resource contains individuals who received a COVID-19 test and individuals whom are probable cases. Every day, these records are overwritten with updates. Each row in the data reflects a person that is tested, not tests that are conducted. People that are tested more than once will have their testing and case data updated using the following rules:

1. Positive tests overwrite negative tests.
2. Polymerase chain reaction (PCR) tests overwrite antibody or antigen (AG) tests.
3. The first positive PCR test is never overwritten. Data collected from additional tests do not replace the first positive PCR test.

Note: On April 4th 2022 the Pennsylvania Department of Health no longer required labs to report negative AG tests. Therefore aggregated counts that included AG tests have been removed from the Municipality/Neighborhood files going forward. Versions of this data up to this cut-off have been retained as archived files.

Individual Test information is also updated daily. This resource contains the details and results of individual tests along with demographic information of the individual tested. Only PCR and AG tests are included. Every day, these records are overwritten with updates. This resource should be used to determine positivity rates.

The remaining datasets provide statistics on death demographics. Demographic, municipality and neighborhood information for deaths are reported on a weekly schedule and are not included with individual cases or tests. This has been done to protect the privacy and security of individuals and their families in accordance with the Health Insurance Portability and Accountability Act (HIPAA). Municipality or City of Pittsburgh Neighborhood is based off the geocoded home address of the individual tested.

Individuals whose home address is incomplete may not be in Allegheny County but whose temporary residency, work or other mitigating circumstance are determined to be in Allegheny County by the Pennsylvania Department of Health are counted as "Undefined".

Since the start of the pandemic, the ACHD has mapped every day’s COVID tests, cases, and deaths to their Allegheny County municipality and neighborhood. Tests were mapped to patient address, and if this was not available, to the provider location. This has recently resulted in apparent testing rates that exceeded the populations of various municipalities -- mostly those with healthcare providers. As this was brought to our attention, the health department and our data partners began researching and comparing methods to most accurately display the data. This has led us to leave those with missing home addresses off the map. Although these data will still appear in test, case and death counts, there will be over 20,000 fewer tests and almost 1000 fewer cases on the map. In addition to these map changes, we have identified specific health systems and laboratories that had data uploading errors that resulted in missing locations, and are working with them to correct these errors.

Due to minor discrepancies in the Municipal boundary and the City of Pittsburgh Neighborhood files individuals whose City Neighborhood cannot be identified are be counted as “Undefined (Pittsburgh)”.

On May 19, 2023, with the rescinding of the COVID-19 public health emergency, changes in data and reporting mechanisms prompted a change to an annual data sharing schedule for tests, cases, hospitalizations, and deaths. Dates for annual release are TBD. The weekly municipal counts and individual data produced before this changed are maintained as archive files.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

COVID-19 Cases information is reported through the Pennsylvania State Department’s National Electronic Disease Surveillance System (PA-NEDSS). As new cases are passed to the Allegheny County Health Department they are investigated by case investigators. During investigation some cases which are initially determined by the State to be in the Allegheny County jurisdiction may change, which can account for differences between publication of the files on the number of cases, deaths and tests. Additionally, information is not always reported to the State in a timely manner, delays can range from days to weeks, which can also account for discrepancies between previous and current files. Test and Case information will be updated daily. This resource contains individuals who received a COVID-19 test and individuals whom are probable cases. Every day, these records are overwritten with updates. Each row in the data reflects a person that is tested, not tests that are conducted. People that are tested more than once will have their testing and case data updated using the following rules: Positive tests overwrite negative tests. Polymerase chain reaction (PCR) tests overwrite antibody or antigen (AG) tests. The first positive PCR test is never overwritten. Data collected from additional tests do not replace the first positive PCR test. Note: On April 4th 2022 the Pennsylvania Department of Health no longer required labs to report negative AG tests. Therefore aggregated counts that included AG tests have been removed from the Municipality/Neighborhood files going forward. Versions of this data up to this cut-off have been retained as archived files. Individual Test information is also updated daily. This resource contains the details and results of individual tests along with demographic information of the individual tested. Only PCR and AG tests are included. Every day, these records are overwritten with updates. This resource should be used to determine positivity rates. The remaining datasets provide statistics on death demographics. Demographic, municipality and neighborhood information for deaths are reported on a weekly schedule and are not included with individual cases or tests. This has been done to protect the privacy and security of individuals and their families in accordance with the Health Insurance Portability and Accountability Act (HIPAA). Municipality or City of Pittsburgh Neighborhood is based off the geocoded home address of the individual tested. Individuals whose home address is incomplete may not be in Allegheny County but whose temporary residency, work or other mitigating circumstance are determined to be in Allegheny County by the Pennsylvania Department of Health are counted as "Undefined". Since the start of the pandemic, the ACHD has mapped every day’s COVID tests, cases, and deaths to their Allegheny County municipality and neighborhood. Tests were mapped to patient address, and if this was not available, to the provider location. This has recently resulted in apparent testing rates that exceeded the populations of various municipalities -- mostly those with healthcare providers. As this was brought to our attention, the health department and our data partners began researching and comparing methods to most accurately display the data. This has led us to leave those with missing home addresses off the map. Although these data will still appear in test, case and death counts, there will be over 20,000 fewer tests and almost 1000 fewer cases on the map. In addition to these map changes, we have identified specific health systems and laboratories that had data uploading errors that resulted in missing locations, and are working with them to correct these errors. Due to minor discrepancies in the Municipal boundary and the City of Pittsburgh Neighborhood files individuals whose City Neighborhood cannot be identified are be counted as “Undefined (Pittsburgh)”.

Weekly updates have finished with the June 28th update. Some information may be found here: https://covid.cdc.gov/covid-data-tracker/#maps_new-admissions-rate-state
This dataset contains aggregate COVID-19 case counts and rates by date of first report for all counties in Pennsylvania and for the state as a whole. Counts include both confirmed and probable cases as defined by the Council of State and Territorial Epidemiologists (CSTE). At present, a person is counted as a case only once. Note that case counts by date of report are influenced by a variety of factors, including but not limited to testing availability, test ordering patterns (such as day of week patterns), labs reporting backlogged test results, and mass screenings in nursing homes, workplaces, schools, etc. Case reports received without a patient address are assigned to the county of the ordering provider or facility based on provider zip code. Cases reported with a residential address that does not match to a known postal address per the commonwealth geocoding service are assigned to a county based on the zip code of residence. Many zip codes cross county boundaries so there is some degree of misclassification of county. All counts may change on a daily basis due to reassignment of jurisdiction, removal of duplicate case reports, correction of errors, and other daily data cleaning activities. Downloaded data represents the best information available as of the previous day.
Data will be updated between 11:30 am to 1:30pm each Wednesday.

This dataset contains aggregate death data at the state and county level for Pennsylvania residents. The data are displayed by county, date, death counts, averages, rates based on population. Pennsylvania statewide numbers are listed with Pennsylvania named as the county for the statewide totals. Do not add up the entire file (all rows) or counts will be duplicated.

Time enabled view of Covid-19 Cases by county across PA from 3/16 to 4/20. Data Source: PA Dept of Health

Weekly updates have finished with the June 28th update. “Note: Beginning 7/13/2022, the hospitals are no longer reporting data on airborne isolation beds resulting in null values being displayed for the airborne isolation bed metrics.” This dataset contains aggregate hospitalization data related to COVID-19 patient which includes availability of ICU beds, patients on ventilators, ventilators in use, and total patients hospitalized data at the state and county level for Pennsylvania residents.
Data will be updated between 11:30 am to 1:30pm each Wednesday.

This dataset is now archived and purely historical. The state of Pennsylvania stopped updating the source data at the end of June, 2023.

Weekly archive of some State of Pennsylvania datasets found in this list: https://data.pa.gov/browse?q=vaccinations

For most of these datasets, the "date_saved" field is the date that the WPRDC pulled the data from the state data portal and the archive combines all the saved records into one table. The exception to this is the "COVID-19 Vaccinations by Day by County of Residence Current Health (archive)" which is already published by the state as an entire history.

The "date_updated" field is based on the date that the "updatedAt" field from the corresponding data.pa.gov dataset. Changes to this field have turned out to not be a good indicator of whether records have updated, which is why we are archiving this data on a weekly basis without regard to the "updatedAt" value. The "date_saved" field is the one you should sort on to see the variation in vaccinations over time.

Most of the source tables have gone through schema changes or expansions. In some cases, we've kept the old archives under a separate resource with something like "[Orphaned Schema]" added to the resource name. In other cases, we've adjusted our schema to accommodate new column names, but there will be a date range during which the new columns have null values because we did not start pulling them until we became aware of them.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

This dataset contains aggregate COVID-19 case counts by race by county of first report for all counties in Pennsylvania. Counts include both confirmed and probable cases as defined by the Council of State and Territorial Epidemiologists (CSTE). Suppression applies for quantities 1-4.

Data only includes information reported to PA-NEDSS, Pennsylvania National Electronic Disease Surveillance System.

Weekly archive of some State of Pennsylvania datasets found in this list: https://data.pa.gov/browse?q=vaccinations

For most of these datasets, the "date_saved" field is the date that the WPRDC pulled the data from the state data portal and the archive combines all the saved records into one table. The exception to this is the "COVID-19 Vaccinations by Day by County of Residence Current Health (archive)" which is already published by the state as an entire history.

The "date_updated" field is based on the date that the "updatedAt" field from the corresponding data.pa.gov dataset. Changes to this field have turned out to not be a good indicator of whether records have updated, which is why we are archiving this data on a weekly basis without regard to the "updatedAt" value. The "date_saved" field is the one you should sort on to see the variation in vaccinations over time.

Most of the source tables have gone through schema changes or expansions. In some cases, we've kept the old archives under a separate resource with something like "[Orphaned Schema]" added to the resource name. In other cases, we've adjusted our schema to accommodate new column names, but there will be a date range during which the new columns have null values because we did not start pulling them until we became aware of them.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Weekly updates have finished.

This dataset contains aggregate data by county for the age groups of individuals that received a COVID vaccination. Data includes counts of individuals who received a vaccine dose that provides partial coverage against the disease and counts of individuals that received a vaccine dose that provides full coverage against the disease. Age groups are presented in 5-year brackets. Suppression applies for quantities less than 5 and records under review.

Data only includes information reported to PA-SIIS, the Pennsylvania Statewide Immunization Information System.

Fully Vaccinated means that the person has received the necessary number of COVID-19 vaccines at the recommended time intervals.

First Booster Doses (Administered Since August 13, 2021)

First Booster doses administered since August 13, 2021 includes vaccinations beyond the primary series administered to the suggested groups at the recommended intervals on or after 08-13-2021 following CDC guidelines. Such vaccinations are commonly called booster doses. This category also includes additional does of COVID vaccine administered on or after 08-13-2021 to immunocompromised individual at the CDC recommended interval.

Second Booster Doses (Administered Since March 29, 2022)

Second booster doses administered since March 29, 2022 includes mRNA vaccinations beyond the primary series and one additional COVIDS vaccine as a second booster dose administered on or after 03-29-2022 to individuals ages 50+ at the recommended intervals per CDC guidelines. This category also includes additional doses of mRNA COVID vaccines administered on or after 03-29-2022 to immunocompromised individual at the CDC recommended internal.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Day of week and county demographic coefficients and 95% confidence intervals based on robust standard errors.

Weekly updates have finished with the June 28th update. This dataset contains aggregate data by zip code of residence for the age groups of individuals that received a COVID vaccination. Data includes counts of individuals who received a vaccine dose that provides partial coverage against the disease and counts of individuals that received a vaccine dose that provides full coverage against the disease. Age groups are presented in 5-year brackets. Suppression applies for quantities less than 5 and records under review. Data only includes information reported to PA-SIIS, the Pennsylvania Statewide Immunization Information System.
Effective 7/9/2021, the COVID-19 Vaccine Dashboard is updated to more accurately reflect the number of people who are partially and fully vaccinated in each county outside of Philadelphia, along with the demographics of those receiving vaccine. For state-to-state comparisons refer to the CDC vaccine data tracker located here: https://covid.cdc.gov/covid-data-tracker/#county-view

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

Attendance at pa-i-porta and Queen’s gala dinner MGEs and number of contacts (k) to obtain expected cases of COVID-19 compared with observed cases from the formula of Tupper and co-authors [63].

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.

The SEIR (susceptible-exposed-infected-recovered) model has become a valuable tool for studying infectious disease dynamics and predicting the spread of diseases, particularly concerning the COVID pandemic. However, existing models often oversimplify population characteristics and fail to account for differences in disease sensitivity and social contact rates that can vary significantly among individuals. To address these limitations, we have developed a new multi-feature SEIR model that considers the heterogeneity of health conditions (disease sensitivity) and social activity levels (contact rates) among populations affected by infectious diseases. Our model has been validated using the data of the confirmed COVID cases in Allegheny County (Pennsylvania, USA) and Hamilton County (Ohio, USA). The results demonstrate that our model outperforms traditional SEIR models regarding predictive accuracy. In addition, we have used our multi-feature SEIR model to propose and evaluate different vaccine prioritization strategies tailored to the characteristics of heterogeneous populations. We have formulated optimization problems to determine effective vaccine distribution strategies. We have designed extensive numerical simulations to compare vaccine distribution strategies in different scenarios. Overall, our multi-feature SEIR model enhances the existing models and provides a more accurate picture of disease dynamics. It can help to inform public health interventions during pandemics/epidemics.