COVID cases and deaths for LA County and California State. Updated daily. Data source: Johns Hopkins University (https://coronavirus.jhu.edu/us-map), Johns Hopkins GitHub (https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv). Code available: https://github.com/CityOfLosAngeles/covid19-indicators.

COVID-19 data for LA County neighborhoods and communities. Updated daily.

Source: LA County Public Health (http://dashboard.publichealth.lacounty.gov/covid19_surveillance_dashboard/). Code available: https://github.com/CityOfLosAngeles/covid19-indicators.

The Mayor’s Office utilizes the most recent data to inform decisions about COVID-19 response and policies. The Los Angeles COVID-19 Neighborhood Map visualizes the cases and deaths across 139 neighborhoods in the city. It includes the same data used by the office to spot changes in infection trends in the city, and identify areas where testing resources should be deployed.Data Source:Data are provided on a weekly basis by the LA County Department of Public Health and prepared by the LA Mayor's Office Innovation Team. The data included in this map are on a one-week lag. That means the data shown here are reporting statistics gathered from one week ago. This map will be updated weekly on Mondays. Click on the maps to zoom in, get more details, and see the legends.

*****PLEASE NOTE: THIS SERVICE IS NOT CONSIDERED AUTHORITATIVE*****For authoritative case and death counts please see the data in the Department of Public Health's LA County COVID-19 Surveillance Dashboarddashboard.publichealth.lacounty.gov/covid19_surveillance_dashboard/Several tables of the data are made available to download, including the current daily count, by selecting a table from the menu on the left side of the dashboard and clicking the "Download his table" button at the top of the table's page.*********************************************************************************This is the hosted feature layer VIEW for Historic case counts that is being updated from the SDE data source through automated scripting.Additionally, this feature layer contains the Accumulated Cases and Death counts. To just view the accumulated totals, apply a filter for Community = County of Los Angeles.The script runs daily at 8pm and finishes around 8:15pm.This view layer replaces the older version. Please update your data source for historic or accumulated COVID-19 cases with this feature layer and remove the older version from your webmaps and applications. Please contact the GIS Unit with questions at gis@ceooem.lacounty.gov.

Daily updates on LA County COVID testing.

Source: LA County Department of Health (http://dashboard.publichealth.lacounty.gov/covid19_surveillance_dashboard/). Code available: https://github.com/CityOfLosAngeles/covid19-indicators.

Context

As an Angeleno passionate about statistics and data, I volunteered to work with the City of Los Angeles on a data science project focused on Covid-19 cases throughout the Los Angeles county. The focus of that project involved web scraping Covid-19 cases released by the County of Los Angeles Public Health. At the time of this project and to my knowledge still, an API has not been built to easily retrieve this data. I've built the web scraping code and have stored this data locally. To make your life easier, I'm sharing here. Enjoy!

Content

Data spans September 19, 2020 through the previous day (For updated continued data feed, please use the repo code). The data in published daily in the evenings by LA Public Health and contains data through the most recent complete date, so it's always 1 day behind. The cases and deaths data is a cumulated count for each point of interest for the given day. Folders after and including January 4, 2021 contain that day's published data, folders before this date contain data for the previous day.

For more details on how the raw data was gathered, visit the direct source @ City of Los Angeles Public Health

Details

Checkout the folder called "Example for Descriptions". It includes details about each csv file contained in each folder.

In Los Angeles County, methamphetamine accounted for the highest share of overdose deaths among people experiencing homelessness (PEH) in the 12 months before and after the COVID-19 pandemic onset, contributing to approximately three-quarters of all overdose deaths in both years. Fentanyl ranked as the second leading cause of overdose death in both periods, but showed the largest increase in its contribution over the analyzed timeframe. This statistic depicts the percentage of deaths among people experiencing homelessness by overdose pre- and post-COVID-19 pandemic in Los Angeles County, by drug type.

The dataset compiles COVID-19 cases, deaths, hospitalizations, tests and vaccination data for Los Angeles county and city from multiple sources in a frequently updated pdf format. It also contains Monkeypox case and vaccination data since August 2022.

Predict the risk score for each county in LA, California

The data is for the 2020 COVID-19 Computational Challenge hosted by the City of Los Angeles in partnership with the Global Association for Research Methods and Data Science (RMDS Lab). The data gathered from different sources like NYT open data GitHub repository.

Data sources

The data collected from: - NYtimes repo on Github. https://github.com/nytimes/covid-19-data) - CHHC open data portal -Asthma by age per county### Predict the risk score for each county in LA, California

The data needs cleaning and processing!

The Equity Explorer Tool allows users to explore census tracts throughout Los Angeles County to identify areas of the highest need based on populations disproportionately affected by COVID-19 prior to embarking on project design by either using the map or a series of filters.To use the Equity Explorer, users can leverage the following capabilities:Core COVID Filters: Apply the various COVID filters in the Core COVID Filters section of the far left pane. These filters include the COVID index scores and categories, the individual index components, HUD Qualified tract status, and other location attributes (like CSA). As filters in this section are applied, the map will update to reflect only tracts meeting the criteria and the summary statistics and table will update accordingly. To turn the filter on, toggle the radio button to the right of the filter. The filter is on when the button is blue. Thematic Filters: Apply any additional filters in the Thematic Filters section. Please note, these filters do not impact the summary statistics at the bottom of the application or the table of tracts. The corresponding layer(s) will need to be turned on using the map layer list to see the filter results. Map Selection: In addition to the above filters, tracts can also be selected directly on the map using the map select tool in the upper left corner of the map. Table Widget: Once the list of tracts has been narrowed down appropriately for the program, tracts can be exported by clicking the table widget in the upper right corner, next to the documentation button. Navigate to the COVID Index tab, click the 4 dot icon to the right of the table, and export records as a CSV. Summary Statistics: As the COVID filters are applied or a selection is made on the map, the statistics at the bottom of the screen will update. Map Layer List: To additional layers on or off the map, click the eye icon next to a layer name in the map layer list in the far right paneMap Legend: The map legend in the bottom right corner will update to show information about the layers currently being visualized on the map.For more information, please contact egis@isd.lacounty.gov or race-equity@ceo.lacounty.gov

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. This dataset will receive a final update on June 1, 2023, to reconcile historical data through May 10, 2023, and will remain publicly available.

This archived public use dataset contains historical case and percent positivity data updated weekly for all available counties and jurisdictions. Each week, the dataset was refreshed to capture any historical updates. Please note, percent positivity data may be incomplete for the most recent time period.

Related data CDC provides the public with two active versions of COVID-19 county-level community transmission level data: this dataset with historical case and percent positivity data for each county from January 22, 2020 (Weekly Historical Changes dataset) and a dataset with the levels as originally posted (Weekly Originally Posted dataset) since October 20, 2022. Please navigate to the Weekly Originally Posted dataset for the Community Transmission Levels published weekly on Thursdays.

Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making.

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2 Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00).

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests resulted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.

This dataset is created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access.

Archived data CDC has archived two prior versions of these datasets. Both versions contain the same 7 data elements reflecting community transmission levels for all available counties and jurisdictions; however, the datasets updated daily. The archived datasets can be found here:

Archived Originally Posted dataset

Archived Historical Changes dataset

Archived Data Notes:

October 27, 2022: Due to a processing issue this dataset will not be posted this week. CDC is currently working to address the issue and will publish the data when able.

November 10, 2022: As of 11/10/2022, this dataset will continue to incorporate historical updates made to case and percent positivity data; however, community transmission level will only be published in the corresponding Weekly COVID-19 County Level of Community Transmission as Originally Posted dataset (Weekly Originally Posted dataset).

Note:

October 20, 2022: Due to a data reporting error, the case rate for Philadelphia County, Pennsylvania is lower than expected in the COVID-19 Community Transmission Level data released on October 20, 2022. This could lead to the COVID-19 Community Transmission Level for Philadelphia County being underestimated; therefore, it should be interpreted with caution.

November 3, 2022: Due to a reporting cadence issue, case rates for Missouri counties are calculated based on 11 days’ worth of case count data in the COVID-19 Community Transmission Level data released on November 3, 2022, instead of the customary 7 days’ worth of data. This could lead to the COVID-19 Community Transmission Levels metrics for Missouri counties being overestimated; therefore, they should be interpreted with caution.

November 10, 2022: Due to a reporting cadence change, case rates for Alabama counties are calculated based on 13 days’ worth of case count data in the COVID-19 Community Transmission Level data released on November 10, 2022, instead of the customary 7 days’ worth of data. This could lead to the COVID-19 Community Transmission Levels metrics for Alabama counties being overestimated; therefore, they should be interpreted with caution.

November 10, 2022: Per the request of the jurisdiction, cases among non-residents have been removed from all Hawaii county totals throughout the entire time series. Cumulative case counts reported by CDC will no longer match Hawaii’s COVID-19 Dashboard, which still includes non-resident cases. 

November 10, 2022: In the COVID-19 Community Transmission Level data released on November 10, 2022, multiple municipalities in Puerto Rico are reporting higher than expected increases in case counts. CDC is working with territory officials to verify the data submitted. 

December 1, 2022: Due to cadence changes over the Thanksgiving holiday, case rates for all Ohio counties are reported as 0 in the COVID-19 Community Transmission Level data released on December 1, 2022. Therefore, the COVID-19 Community Transmission Levels may be underestimated and should be interpreted with caution. 

December 22, 2022: Due to an internal revision process, case rates for some Tennessee counties may appear higher than expected in the December 22, 2022, weekly release. Therefore, the COVID-19 Community Transmission Levels metrics for some Tennessee counties may be overestimated and should be interpreted with caution.

December 22, 2022: Due to reporting of a backlog of historic COVID-19 cases, case rates for some Louisiana counties will appear higher than expected in the December 22, 2022, weekly release. Therefore, the COVID-19 Community Transmission Levels metrics for some Louisiana counties may be overestimated and should be interpreted with caution.

December 29, 2022: Due to technical difficulties, county data from Alabama could not be incorporated via standard practices. As a result, case and death metrics will be reported as 0 in the December 29, 2022, weekly release. Therefore, the COVID-19 Community Transmission Levels metrics for Alabama counties will be underestimated and should be interpreted with caution.

January 5, 2023: Due to a reporting cadence issue, case rates for all Alabama counties will be calculated based on 14 days’ worth of case count data in the COVID-19 Community Transmission Level information released on January 5, 2023, instead of the customary 7 days’ worth of case count data. Therefore, the weekly case rates will be overestimated, which could affect counties’ COVID-19 Community Transmission Level classification and should be interpreted with caution.

January 5, 2023: Due to North Carolina’s holiday reporting cadence, aggregate case data will contain 14 days’ worth of data instead of the customary 7 days. As a result, case metrics will appear higher than expected in the January 5, 2023, weekly release. COVID-19 Community Transmission metrics may be overestimated and should be interpreted with caution.

January 12, 2023: Due to data processing delays, Mississippi’s aggregate case data will be reported as 0. As a result, case metrics will appear lower than expected in the January 12, 2023, weekly release. COVID-19 Community Transmission metrics may be underestimated and should be interpreted with caution. 

January 13, 2023: Aggregate case data released for Los Angeles County, California for the week of December 22nd, 2022, and December 29th, 2022, have been corrected for a data processing error.

January 19, 2023: Due to a reporting cadence issue, Mississippi’s aggregate case data will be calculated based on 14 days’ worth of data instead of the customary 7 days in the January 19, 2023, weekly release. Therefore, COVID-19 Community Transmission metrics may be overestimated and should be interpreted with caution.

January 26, 2023: Due to a reporting backlog of historic COVID-19 cases, case rates for two Michigan counties

Additional file 5: Table S5. Parameters for effective reproductive number estimation.

The COVID-19 Vulnerability and Recovery Index uses Tract and ZIP Code-level data* to identify California communities most in need of immediate and long-term pandemic and economic relief. Specifically, the Index is comprised of three components — Risk, Severity, and Recovery Need with the last scoring the ability to recover from the health, economic, and social costs of the pandemic. Communities with higher Index scores face a higher risk of COVID-19 infection and death and a longer uphill economic recovery. Conversely, those with lower scores are less vulnerable.

The Index includes one overarching Index score as well as a score for each of the individual components. Each component includes a set of indicators we found to be associated with COVID-19 risk, severity, or recovery in our review of existing indices and independent analysis. The Risk component includes indicators related to the risk of COVID-19 infection. The Severity component includes indicators designed to measure the risk of severe illness or death from COVID-19. The Recovery Need component includes indicators that measure community needs related to economic and social recovery. The overarching Index score is designed to show level of need from Highest to Lowest with ZIP Codes in the Highest or High need categories, or top 20th or 40th percentiles of the Index, having the greatest need for support.

The Index was originally developed as a statewide tool but has been adapted to LA County for the purposes of the Board motion. To distinguish between the LA County Index and the original Statewide Index, we refer to the revised Index for LA County as the LA County ARPA Index.

*Zip Code data has been crosswalked to Census Tract using HUD methodology

Indicators within each component of the LA County ARPA Index are:Risk: Individuals without U.S. citizenship; Population Below 200% of the Federal Poverty Level (FPL); Overcrowded Housing Units; Essential Workers Severity: Asthma Hospitalizations (per 10,000); Population Below 200% FPL; Seniors 75 and over in Poverty; Uninsured Population; Heart Disease Hospitalizations (per 10,000); Diabetes Hospitalizations (per 10,000)Recovery Need: Single-Parent Households; Gun Injuries (per 10,000); Population Below 200% FPL; Essential Workers; Unemployment; Uninsured PopulationData are sourced from US Census American Communities Survey (ACS) and the OSHPD Patient Discharge Database. For ACS indicators, the tables and variables used are as follows:

Indicator

ACS Table/Years

Numerator

Denominator

Non-US Citizen

B05001, 2019-2023

b05001_006e

b05001_001e

Below 200% FPL

S1701, 2019-2023

s1701_c01_042e

s1701_c01_001e

Overcrowded Housing Units

B25014, 2019-2023

b25014_006e + b25014_007e + b25014_012e + b25014_013e

b25014_001e

Essential Workers

S2401, 2019-2023

s2401_c01_005e + s2401_c01_011e + s2401_c01_013e + s2401_c01_015e + s2401_c01_019e + s2401_c01_020e + s2401_c01_023e + s2401_c01_024e + s2401_c01_029e + s2401_c01_033e

s2401_c01_001

Seniors 75+ in Poverty

B17020, 2019-2023

b17020_008e + b17020_009e

b17020_008e + b17020_009e + b17020_016e + b17020_017e

Uninsured

S2701, 2019-2023

s2701_c05_001e

NA, rate published in source table

Single-Parent Households

S1101, 2019-2023

s1101_c03_005e + s1101_c04_005e

s1101_c01_001e

Unemployment

S2301, 2019-2023

s2301_c04_001e

NA, rate published in source table

The remaining indicators are based data requested and received by Advancement Project CA from the OSHPD Patient Discharge database. Data are based on records aggregated at the ZIP Code level:

Indicator

Years

Definition

Denominator

Asthma Hospitalizations

2017-2019

All ICD 10 codes under J45 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Gun Injuries

2017-2019

Principal/Other External Cause Code "Gun Injury" with a Disposition not "Died/Expired". ICD 10 Code Y38.4 and all codes under X94, W32, W33, W34, X72, X73, X74, X93, X95, Y22, Y23, Y35 [All listed codes with 7th digit "A" for initial encounter]

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Heart Disease Hospitalizations

2017-2019

ICD 10 Code I46.2 and all ICD 10 codes under I21, I22, I24, I25, I42, I50 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Diabetes (Type 2) Hospitalizations

2017-2019

All ICD 10 codes under E11 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

For more information about this dataset, please contact egis@isd.lacounty.gov.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global disruption to human health and activity. Being able to trace the early outbreak of SARS-CoV-2 within a locality will inform public health measures and provide insights to contain or prevent the viral transmission to save lives. Investigation of the transmission history requires efficient sequencing methods and analytic strategy, which can be generally useful in the study of viral outbreaks. Los Angeles (LA) County has sustained a large outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To learn about the transmission history, we carried out surveillance viral genome sequencing to determine 142 viral genomes from unique patients seeking care at UCLA Health System. 86 of these genomes are from samples collected before April 19, 2020. We found that the early outbreak in LA, as in other international air travel hubs, was seeded by multiple introductions of strains from Asia and Europe. We identified a US-specific strain, B.1.43, which has been found predominantly in California and Washington State. While samples from LA County carry the ancestral B.1.43 genome, viral genomes from neighboring counties in California and from counties in Washington State carry additional mutations, suggesting a potential origin of B.1.43 in Southern California. We quantified the transmission rate of SARS-CoV-2 over time, and found evidence that the public health measures put in place in LA County to control the virus were effective at preventing transmission, but may have been undermined by the many introductions of SARS-CoV-2 into the region. Our work demonstrates that genome sequencing can be a powerful tool for investigating outbreaks and informing the public health response. Our results reinforce the critical need for the U.S. to have coordinated inter-state responses to the pandemic.

Deaths were determined to be COVID-associated if they met the Department of Public Health's surveillance definition at the time of death.The cumulative COVID-19 mortality rate can be used to measure the most severe impacts of COVID-19 in a community. There have been documented inequities in COVID-19 mortality rates by demographic and geographic factors. Black and Brown residents, seniors, and those living in areas with higher rates of poverty have all been disproportionally impacted.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Additional file 1: Table S1. Collection dates and quality control for 260 patient samples.

Note: On April 30, 2024, the Federal mandate for COVID-19 and influenza associated hospitalization data to be reported to CDC’s National Healthcare Safety Network (NHSN) expired. Hospitalization data beyond April 30, 2024, will not be updated on the Open Data Portal. Hospitalization and ICU admission data collected from summer 2020 to May 10, 2023, are sourced from the California Hospital Association (CHA) Survey. Data collected on or after May 11, 2023, are sourced from CDC's National Healthcare Safety Network (NHSN).

Data is from the California Department of Public Health (CDPH) Respiratory Virus State Dashboard at https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Respiratory-Viruses/RespiratoryDashboard.aspx.

Data are updated each Friday around 2 pm.

For COVID-19 death data: As of January 1, 2023, data was sourced from the California Department of Public Health, California Comprehensive Death File (Dynamic), 2023–Present. Prior to January 1, 2023, death data was sourced from the COVID-19 case registry. The change in data source occurred in July 2023 and was applied retroactively to all 2023 data to provide a consistent source of death data for the year of 2023. Influenza death data was sourced from the California Department of Public Health, California Comprehensive Death File (Dynamic), 2020–Present.

COVID-19 testing data represent data received by CDPH through electronic laboratory reporting of test results for COVID-19 among residents of California. Testing date is the date the test was administered, and tests have a 1-day lag (except for the Los Angeles County, which has an additional 7-day lag). Influenza testing data represent data received by CDPH from clinical sentinel laboratories in California. These laboratories report the aggregate number of laboratory-confirmed influenza virus detections and total tests performed on a weekly basis. These data do not represent all influenza testing occurring in California and are available only at the state level.

The COVID-19 pandemic has undoubtedly impacted everyone around the globe. In 2020, many countries entered into a lockdown, transforming daily lifestyles into isolation. The SARS-CoV-2 virus that causes the disease COVID-19 slowly spread to different regions of the world, and the first cases of COVID-19 infection in Los Angeles County, California, were documented in mid-January 2020. In March 2020, Governor Gavin Newsom of California declared a state of emergency and implemented a stay-at-home order (1). Therefore,

people were quarantined at home, and many “non- essential” businesses were closed, including schools.

With no cure available and hospitals reaching maximum capacity, scientists raced to develop vaccines to immunize individuals against the virus. Meanwhile, wastewater technicians began collecting wastewater samples to monitor the presence of the SARS-CoV-2 virus shed from infected residents. We hypothesized that the presence of SARS-CoV-2 RNA in LA County wastewater would decrease as localized vaccination rates increased. Here, we describe a meta-analysis comparing two data sets; the vaccination progression data in Los Angeles County, and the wastewater surveillance PCR

While most construction sites have been closed down in the greater San Francisco Bay Area, many construction works in Los Angeles County appear to be moving forward. Read More