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.

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.

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.

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!

Data is from the California Department of Public Health (CDPH) Respiratory Virus Weekly Report.

The report is updated each Friday.

Laboratory surveillance data: California laboratories report SARS-CoV-2 test results to CDPH through electronic laboratory reporting. Los Angeles County SARS-CoV-2 lab data has a 7-day reporting lag. Test positivity is calculated using SARS-CoV-2 lab tests that has a specimen collection date reported during a given week.

Laboratory surveillance for influenza, respiratory syncytial virus (RSV), and other respiratory viruses (parainfluenza types 1-4, human metapneumovirus, non-SARS-CoV-2 coronaviruses, adenovirus, enterovirus/rhinovirus) involves the use of data from clinical sentinel laboratories (hospital, academic or private) located throughout California. Specimens for testing are collected from patients in healthcare settings and do not reflect all testing for influenza, respiratory syncytial virus, and other respiratory viruses in California. These laboratories report the number of laboratory-confirmed influenza, respiratory syncytial virus, and other respiratory virus detections and isolations, and the total number of specimens tested by virus type on a weekly basis.

Test positivity for a given week is calculated by dividing the number of positive COVID-19, influenza, RSV, or other respiratory virus results by the total number of specimens tested for that virus. Weekly laboratory surveillance data are defined as Sunday through Saturday.

Hospitalization data: Data on COVID-19 and influenza hospital admissions are from Centers for Disease Control and Prevention’s (CDC) National Healthcare Safety Network (NHSN) Hospitalization dataset. The requirement to report COVID-19 and influenza-associated hospitalizations was effective November 1, 2024. CDPH pulls NHSN data from the CDC on the Wednesday prior to the publication of the report. Results may differ depending on which day data are pulled. Admission rates are calculated using population estimates from the P-3: Complete State and County Projections Dataset provided by the State of California Department of Finance (https://dof.ca.gov/forecasting/demographics/projections/). Reported weekly admission rates for the entire season use the population estimates for the year the season started. For more information on NHSN data including the protocol and data collection information, see the CDC NHSN webpage (https://www.cdc.gov/nhsn/index.html).

CDPH collaborates with Northern California Kaiser Permanente (NCKP) to monitor trends in RSV admissions. The percentage of RSV admissions is calculated by dividing the number of RSV-related admissions by the total number of admissions during the same period. Admissions for pregnancy, labor and delivery, birth, and outpatient procedures are not included in total number of admissions. These admissions serve as a proxy for RSV activity and do not necessarily represent laboratory confirmed hospitalizations for RSV infections; NCKP members are not representative of all Californians.

Weekly hospitalization data are defined as Sunday through Saturday.

Death certificate data: CDPH receives weekly year-to-date dynamic data on deaths occurring in California from the CDPH Center for Health Statistics and Informatics. These data are limited to deaths occurring among California residents and are analyzed to identify influenza, respiratory syncytial virus, and COVID-19-coded deaths. These deaths are not necessarily laboratory-confirmed and are an underestimate of all influenza, respiratory syncytial virus, and COVID-19-associated deaths in California. Weekly death data are defined as Sunday through Saturday.

Wastewater data: This dataset represents statewide weekly SARS-CoV-2 wastewater summary values. SARS-CoV-2 wastewater concentrations from all sites in California are combined into a single, statewide, unit-less summary value for each week, using a method for data transformation and aggregation developed by the CDC National Wastewater Surveillance System (NWSS). Please see the CDC NWSS data methods page for a description of how these summary values are calculated. Weekly wastewater data are defined as Sunday through Saturday.

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.

*****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.

Copyright 2020 by The New York Times Company

Coronavirus (Covid-19) Data in the United States

[ U.S. Data (Raw CSV) | U.S. State-Level Data (Raw CSV) | U.S. County-Level Data (Raw CSV) ]

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.

United States Data Data on cumulative coronavirus cases and deaths can be found in three files, one for each of these geographic levels: U.S., states and counties.

Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information. If a county is not listed for a date, then there were zero reported confirmed cases and deaths.

State and county files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data.

Download all the data or clone this repository by clicking the green "Clone or download" button above.

U.S. National-Level Data The daily number of cases and deaths nationwide, including states, U.S. territories and the District of Columbia, can be found in the us.csv file. (Raw CSV file here.)

date,cases,deaths 2020-01-21,1,0 ... State-Level Data State-level data can be found in the states.csv file. (Raw CSV file here.)

date,state,fips,cases,deaths 2020-01-21,Washington,53,1,0 ... County-Level Data County-level data can be found in the counties.csv file. (Raw CSV file here.)

date,county,state,fips,cases,deaths 2020-01-21,Snohomish,Washington,53061,1,0 ... In some cases, the geographies where cases are reported do not map to standard county boundaries. See the list of geographic exceptions for more detail on these.

Methodology and Definitions The data is the product of dozens of journalists working across several time zones to monitor news conferences, analyze data releases and seek clarification from public officials on how they categorize cases.

It is also a response to a fragmented American public health system in which overwhelmed public servants at the state, county and territorial level have sometimes struggled to report information accurately, consistently and speedily. On several occasions, officials have corrected information hours or days after first reporting it. At times, cases have disappeared from a local government database, or officials have moved a patient first identified in one state or county to another, often with no explanation. In those instances, which have become more common as the number of cases has grown, our team has made every effort to update the data to reflect the most current, accurate information while ensuring that every known case is counted.

When the information is available, we count patients where they are being treated, not necessarily where they live.

In most instances, the process of recording cases has been straightforward. But because of the patchwork of reporting methods for this data across more than 50 state and territorial governments and hundreds of local health departments, our journalists sometimes had to make difficult interpretations about how to count and record cases.

For those reasons, our data will in some cases not exactly match with the information reported by states and counties. Those differences include these cases: When the federal government arranged flights to the United States for Americans exposed to the coronavirus in China and Japan, our team recorded those cases in the states where the patients subsequently were treated, even though local health departments generally did not. When a resident of Florida died in Los Angeles, we recorded her death as having occurred in California rather than Florida, though officials in Florida counted her case in their own records. And when officials in some states reported new cases without immediately identifying where the patients were being treated, we attempted to add informati...

Context

COVID-19 is on a rise worldwide. It was first identified in the city of Wuhan in China in 2019 and has now spread into a global pandemic. California is currently the fourth largest affected state in USA. The state's confirmed cases have been on a rise since early March 2020 due to more testing capabilities. In this dire time, it is extremely important to understand the factors affecting the spread of the virus in California, identify susceptible population and predict the trajectory of the infected and dead cases on a daily basis.

Content

Update: 4 April 2020, 7:27 PM Pacific Time (PT)

This data contains information about confirmed cases (13927) and fatalities (321) due to COVID-19 in 58 California counties along with instructions provided by health agencies in all counties. A breakdown of confirmed cases in the cities of California is also provided. The information has been sourced from Los Angeles Times.

As mentioned by LA Times, "The tallies here are mostly limited to residents of California, which is the standard method used to count patients by the state’s health authorities. Those totals do not include people from other states who are quarantined here, such as the passengers and crew of the Grand Princess cruise ship that docked in Oakland."

Acknowledgements

LA Times - https://www.latimes.com/projects/california-coronavirus-cases-tracking-outbreak/

Inspiration

1. This dataset will be useful in understanding and predicting the trajectory of the infected and dead cases in California in the coming days.
2. It might also be useful for COVID19 Local US-CA Forecasting (Week 1) competition
3. The dataset can also highlight any need to update any health agency instructions to take further precautionary measures and save lives.

Please consider upvoting if the data is found useful in any way. If there are any improvement suggestions, do let me know.

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.

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

COVID-19 cases by community. Data Source: Los Angeles County Department of Public Health

Objectives: Studies of household transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) focused on households with children are limited. We investigated household secondary attack rate (SAR), transmission dynamics, and contributing factors in households with children.Materials and Methods: In this prospective case-ascertained study in Los Angeles County, California, all households members were enrolled if ≥1 member tested positive for SARS-CoV-2 by polymerase chain reaction (PCR). Nasopharyngeal PCRs, serology, and symptom data were obtained over multiple visits.Results: A total of 489 individuals in 105 households were enrolled from June to December 2020. The majority (77.3%) reported a household annual income of

The data and files support the analysis of accessibility to nearest healthcare facilities across 44 locations in Los Angeles city using an MCDA [1] approach. In this dataset, the raster files are layers used to compute a cost layer using a linear sum approach. The backlink raster and cost distance are used to generate least cost paths to the healthcare facilites which are then combinied with the social vulnerability index dataset from the Center of Disease Control [2] to compute an accessiblity index.

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.

The coronavirus has already significantly impacted box office markets in North America, with recent data showing that box offices in Montreal generated just *** thousand U.S. dollars in revenue on the weekend ending March 15, 2020, down by ** percent from the previous weekend. New York and Boston were also among the hardest hit with a revenue downturn of over ** percent each from the weekend ending March 8, and number one box office market Los Angeles suffered losses of ** percent in the same time period. It is worth nothing that some markets were more heavily affected due to the number of coronavirus cases logged within each state, and also that in between these two weekends the World Health Organization declared the coronavirus to be a global pandemic which sent many industries into shock and saw the introduction of increased measures to contain the virus, including movie theater closures and film release cancellations.

People experiencing homelessness (PEH) were among the most likely to contract the novel coronavirus disease 2019 (COVID-19). Many PEH utilized high-density public places to satisfy their basic needs (e.g., soup kitchens for sustenance, public libraries for restrooms). This made it difficult for them to limit close contact with others and put them at increased risk of contracting and transmitting COVID-19. Furthermore, it was difficult to follow recommended protective measures--such as handwashing and social distancing--when living in shelters or on the streets. PEH were at higher risk of COVID-19 related hospitalization and death than the rest of the population. The poor living conditions of PEH accelerated aging, leading them to experience geriatric conditions and medical complications more typical of individuals 10-20 years older. They were also at increased risk of cardiovascular and respiratory disease, HIV/AIDS, and diabetes, all conditions that increase vulnerability to serious COVID-19-related complications and death. These risks were compounded by the fact that PEH also faced significant barriers to accessing quality health care. In the absence of protective action, it was estimated that more than 21,000 PEH would require hospitalization due to COVID-19, more than 7,000 would require critical care, and nearly 3,500 would die. Consequently, the COVID-19 pandemic made housing and health care for PEH one of the top priorities for the U.S. health care and public health systems. State and local governments across the country used federal relief funds to allocate private hotel rooms as protective shelter for vulnerable PEH. In Los Angeles County (LAC), which contains the largest unsheltered homeless population in the nation, 2,400 PEH were placed in hotels. COVID-19 response plans included accommodating up to 15,000 PEH in hotels who would then be moved to permanent housing in 90 days. This rapid push into housing amid a pandemic necessitated a delicate balance between social distancing and maintaining patients' basic needs, continuity of existing care, and personal and social well-being. Permanent supportive housing (PSH)--programs that provide immediate access to independent living situations coupled with support services--is the most effective approach for serving PEH. Numerous studies have demonstrated PSH's effectiveness in improving housing retention, quality of life, and HIV outcomes. Though evidence concerning its impact on other health outcomes, health behaviors, and health care utilization is limited, the National Academies of Sciences, Engineering, and Medicine has nonetheless recognized PSH as extremely beneficial for PEH's health. COVID-19 was what this organization termed a "housing-sensitive condition"--one whose transmissibility, course, and medical management are particularly influenced by homelessness. Consequently, the National Alliance to End Homelessness recommended the use of PSH as part of its framework to address COVID-19 and homelessness. However, significant questions remain about what types of PSH programs can best address COVID-19-related risk and promote patient-centered outcomes at a time of social and community disruption. There are two distinct approaches to implementing PSH: place-based (PB) PSH, or single-site housing placement in a congregate residence with on-site services, and scattered-site (SS) PSH, which uses apartments rented from a private landlord to house clients while providing mobile case management services. The strengths and weaknesses of these two approaches remain largely unknown but may have direct implications for adherence to COVID-19 prevention protocols and other health-related outcomes.

Case investigation (CI) and contact tracing (CT) are key to containing the COVID-19 pandemic. Widespread community transmission necessitates a large, diverse workforce with specialized knowledge and skills. The University of California, San Francisco and Los Angeles partnered with the California Department of Public Health to rapidly mobilize and train a CI/CT workforce. In April through August 2020, a team of public health practitioners and health educators constructed a training program to enable learners from diverse backgrounds to quickly acquire the competencies necessary to function effectively as CIs and CTs. Between April 27 and May 5, the team undertook a curriculum design sprint by performing a needs assessment, determining relevant goals and objectives, and developing content. The initial four-day curriculum consisted of 13 hours of synchronous live web meetings and 7 hours of asynchronous, self-directed study. Educational content emphasized the principles of COVID-19 exposure, infectious period, isolation and quarantine guidelines and the importance of prevention and control interventions. A priority was equipping learners with skills in rapport building and health coaching through facilitated web-based small group skill development sessions. The training was piloted among 31 learners and subsequently expanded to an average weekly audience of 520 persons statewide starting May 7, reaching 7,499 unique enrollees by August 31. Capacity to scale and sustain the training program was afforded by the UCLA Extension Canvas learning management system. Repeated iteration of content and format was undertaken based on feedback from learners, facilitators, and public health and community-based partners. It is feasible to rapidly train and deploy a large workforce to perform CI and CT. Interactive skills-based training with opportunity for practice and feedback are essential to develop independent, high-performing CIs and CTs. Rigorous evaluation will continue to monitor quality measures to improve the training experience and outcomes.

On April 22, 2020, 549 U.S. resident users of Prolific.co completed (1) the Authoritarianism-Conservatism-Traditionalism Scales (Duckitt, Bizumic, Kruauss, and Heled, 2010), (2) the 22-item short form of the Left-Wing Authoritarianism Index (Costello and Lilienfeld, 2019), (3) their level of endorsement of 19 policies that could possibly mitigate the impact of the COVID-19 pandemic, and (4) a set of demographic questions (age, gender, ethnicity, pre-pandemic household income, and highest educational attainment). They were also asked to provide their current ZIP code, because county-level COVID-19 prevalence was a control variable in the analyses. To protect participants' confidentiality, neither ZIP code nor county name is included in this data set. Instead, the data set includes the relevant control variable (COVID-19 cases per 100,000 county residents on Apr. 22, 2020). Also provided are the STATA commands used in the analyses.