A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2016-2020 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents. On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021. Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset. Dataset is cumulative and covers cases going back to 3/2/2020 when testing began. Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents. C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time. D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000 Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology. A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website. Row included for Citywide case counts, incidence rate, and deaths A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongo

Demographic characteristics for Bay Area and in the study population overall–N (%) 1.

On 6/28/2023, data on cases by vaccination status will be archived and will no longer update.

A. SUMMARY This dataset represents San Francisco COVID-19 positive confirmed cases by vaccination status over time, starting January 1, 2021. Cases are included on the date the positive test was collected (the specimen collection date). Cases are counted in three categories: (1) all cases; (2) unvaccinated cases; and (3) completed primary series cases.

1. All cases: Includes cases among all San Francisco residents regardless of vaccination status.

2. Unvaccinated cases: Cases are considered unvaccinated if their positive COVID-19 test was before receiving any vaccine. Cases that are not matched to a COVID-19 vaccination record are considered unvaccinated.

3. Completed primary series cases: Cases are considered completed primary series if their positive COVID-19 test was 14 days or more after they received their 2nd dose in a 2-dose COVID-19 series or the single dose of a 1-dose vaccine. These are also called “breakthrough cases.”

On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021.

Data is lagged by eight days, meaning the most recent specimen collection date included is eight days prior to today. All data updates daily as more information becomes available.

B. HOW THE DATASET IS CREATED Case information is based on confirmed positive laboratory tests reported to the City. The City then completes quality assurance and other data verification processes. Vaccination data comes from the California Immunization Registry (CAIR2). The California Department of Public Health runs CAIR2. Individual-level case and vaccination data are matched to identify cases by vaccination status in this dataset. Case records are matched to vaccine records using first name, last name, date of birth, phone number, and email address.

We include vaccination records from all nine Bay Area counties in order to improve matching rates. This allows us to identify breakthrough cases among people who moved to the City from other Bay Area counties after completing their vaccine series. Only cases among San Francisco residents are included.

C. UPDATE PROCESS Updates automatically at 08:00 AM Pacific Time each day.

D. HOW TO USE THIS DATASET Total San Francisco population estimates can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). To identify total San Francisco population estimates, filter the view on “demographic_category_label” = “all ages”.

Population estimates by vaccination status are derived from our publicly reported vaccination counts, which can be found at COVID-19 Vaccinations Given to SF Residents Over Time.

The dataset includes new cases, 7-day average new cases, new case rates, 7-day average new case rates, percent of total cases, and 7-day average percent of total cases for each vaccination category.

New cases are the count of cases where the positive tests were collected on that specific specimen collection date. The 7-day rolling average shows the trend in new cases. The rolling average is calculated by averaging the new cases for a particular day with the prior 6 days.

New case rates are the count of new cases per 100,000 residents in each vaccination status group. The 7-day rolling average shows the trend in case rates. The rolling average is calculated by averaging the case rate for a part

Robustness check with marginal probabilities estimated from logit and probit models for respondents who were extremely worried about COVID-19 after versus before the San Francisco Bay Area shelter-in-place announcement 1.

*Authors contributed equally to work
**Corresponding authors

Changes in social distancing, difficulties, and concern after the shelter-in-place versus before in the Bay Area versus elsewhere in the U.S.

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

The data set summarizes the number of COVID-19 tests completed among Santa Clara County residents by major healthcare systems in the county. Each ‘test’ or ‘testing incident’ represents at least one specimen tested per person, per day. This does not represent the number of individuals tested, as some people are tested multiple times over time because of the risk of frequent exposure. Source: California Reportable Disease Information Exchange. Data notes: The daily average rate of tests is the daily average number of tests completed over the past 7 days per 100,000 people served by the individual healthcare system. The State of California has defined an initial goal of at least 150 tests per day per 100,000 people. Bay Area County Health Officers set a goal of 200 tests per day per 100,000 people.

This table was updated for the last time on May 20, 2021.

Context

Bay Area Rapid Transit or BART is a public rail system that connects much of California's San Francisco Bay Area. The transport system "connects the San Francisco Peninsula with Berkeley, Oakland, Fremont, Walnut Creek, Dublin/Pleasanton and other cities in the East Bay".

Content

This dataset is the most detailed information of trip information for BART and was provided by BART directly. Specifically, this data was pulled from the provided source http://64.111.127.166/origin-destination/. The data are automatically updated on the site and BART says they "are usually available by the 5th of the next month".

Acknowledgements

This obviously wouldn't be available without BART collecting and providing the data. It's great that the data is publicly available to this essential transportation to those living in the Bay Area!

Inspiration

This data was originally pulled in July 2020 during the COVID-19 pandemic. As counties in the Bay Area begin relaxing quarantine/lockdown restrictions yet an increase of COVID-19 cases continues, it could be important to see how public transportation has changed. It's possible to see the travel habits of different areas in the Bay.

A quick note

This is the maintained iteration from the first Kaggle dataset (which is no longer mainted): https://www.kaggle.com/mrgeislinger/bart-ridership

Alternative characterization of DID groups for analysis of experienced difficulties after versus before the San Francisco Bay Area shelter-in-place announcement.

Progress in understanding long COVID ÂÂÂand developing effective therapeutics is hampered in part by the lack of suitable animal models. Here we used ACE2-transgenic mice recovered from Omicron (BA.1) infection to test for pulmonary and behavioral post-acute sequelae. Through in-depth phenotyping by CyTOF, we demonstrate that naïve mice experiencing a first Omicron infection exhibit profound immune perturbations in the lung after resolving acute infection. This is not observed if mice were first vaccinated with spike-encoding mRNA. The protective effects of vaccination against post-acute sequelae were associated with a highly polyfunctional SARS-CoV-2-specific T cell response that was recalled upon BA.1 breakthrough infection but not seen with BA.1 infection alone. Without vaccination, the chemokine receptor CXCR4 was uniquely upregulated on multiple pulmonary immune subsets in the BA.1 convalescent mice, a process previously connected to severe COVID-19. Taking advantage of recent deve..., , , READ ME FILE

General Information

Title: Vaccination protects against persistence of pulmonary immunological perturbations in mouse model of long COVID

Contact:

Nadia Roan, PhD

Gladstone Institutes

University of California, San Francisco

Dates of collection:

4/1/2022 6/27/2022

Information about geographic location of data collection:

San Francisco Bay Area

Key Words:

COVID-19, SARS-CoV-2, Mouse Model, Long Covid, Post-Acute Sequelae of COVID-19 (PASC), T cells, CyTOF, CXCR4

Data and File Overview

Included are total 40 FCS files corresponding to CyTOF data generated from the murine lung. Data were generated from the following five groups of mice:

1. Mock-treated (Mock)
2. Vaccinated (Vac)
3. BA.1 convalescent (BA1)
4. Vaccinated, BA.1 convalescent (Vac_BA1)

Each experimental group had 5 mice. Each murine sample was analyzed at baseline, or following a 6 hour stimulation with SARS-CoV-2 peptides to characterize SARS-CoV-2-specific T cells.

Each FCS file name b...

The average weekday ridership of the San Francisco Bay Area rapid transit system (BART) fluctuated year by year during the given period. The number of passengers who exit the BART system on a weekday dropped from 410,774 in 2019 to 288,271 in 2020 as a result of the COVID-19 pandemic.

The annual operating revenue of the public transportation authority of the San Francisco Bay Area (BART) amounted to 90.5 million U.S. dollars in the financial year 2021. This represented a significant decrease amid the COVID-19 pandemic of around 77.1 percent compared to the previous year.

Data used for analysis in paper. Includes concentrations of SARS-CoV-2 N and PMMoV genes in wastewater solids in three wastewater treatment plants in the greater Bay Area of California, USA

The annual ridership of the San Francisco Bay Area rapid transit system (BART) fluctuated year by year during the given period. The number of passengers who exit the BART system dropped from 118.1 million in the fiscal year 2019 to 83.7 million in the fiscal year 2020 as a result of the COVID-19 pandemic.

See tables for region and stratum population-adjusted estimates for COVID-19 outcomes and mitigation analyses. (XLSX)

This dataset contains pre- and post-service responses from 175 participants in the Encore Intergenerational Vaccine Corps, a health outreach program within Federally Qualified Health Centers (FQHCs) that was run in the San Francisco Bay Area. The program, sponsored by AmeriCorps Seniors—a federal agency that promotes national and community service for Americans aged 55 and older—ran from May 2021 to April 2022. Participants ranged in age from 18 to 81 years. The volunteers, who aimed to increase awareness and administration of COVID-19 vaccines, were evenly divided between those with medical skills (50%, such as current or retired nurses or doctors) and those without medical skills (50%). The survey questions focused on participants’ perceptions of individuals from different generations (i.e., respondents aged 50+ were asked about younger people, and vice versa) and public issues (e.g., the adequacy of resource allocation to FQHCs), along with their experiences in the program. All responses were collected online, with survey outreach conducted by CoGenerate (formerly Encore.org), the lead operator of the Vaccine Corps. The complete dataset, which includes 145 variables (three of which are string/text variables from open-ended responses), is available in both Stata .do and CSV file formats. A codebook is also provided.

IntroductionThe psychological health of healthcare workers (HCWs) has become a significant concern, particularly during the initial stage of a pandemic. This study compared the depressive symptoms among HCWs in high-risk areas (HRAs) and low-risk areas (LRAs) with matching demographics.MethodsA cross-sectional study was employed to compare the depressive symptoms (Patient Health Questionnaire score ≥ 10), workplace environment characteristics, the Health Belief Model (HBM) and socio-demographics of the HCWs working in HRAs and LRAs in several accessible regions (mainly Hubei Province and Guangdong–Hong Kong–Macao Greater–Bay–Area) in China. Eight hundred eighty-five HCWs were recruited for unmatched analysis between March 6 and April 2, 2020. After matching with occupation and years of service using a 1:2 ratio, 146 HCWs in HRAs and 290 HCWs in LRAs were selected for matched analysis. Subgroup analyzes were performed using two individual logistic regressions to delineate the associated factors in LRAs and HRAs, respectively.ResultsHCWs in LRAs (Prevalence = 23.7%) had 1.96 times higher odds of depressive symptoms than those in HRAs (Prevalence = 15.1%) after adjusting for occupation and years of service (p 

This statistic is based on a survey conducted in January 2023. It displays U.S. adults' opinions on which country would be the number one world leader in science and technology in the year 2030. Some 20 percent of respondents believed that China would be the leader in science and technology in 2030.

World leaders in science

Among American adults, 52 percent believe that the United States would be considered the world leader in science and technology in 2030. Back in 2010, the United States was viewed by many as the leader in life science technologies. For medical implants, 81 percent considered the country at the top, while 10 percent of people considered Germany to be the leader in pharmaceutical developments. In 2021, the United States had a total employment in bioscience sectors of roughly 2.1 million, of which the largest single part worked in research, testing, and laboratories.

Biotech shows its potentials

Biotechnology is a good example for a very science and technology driven sector, as was seen recently during the COVID-19 pandemic, when biotech companies like BioNTech and Moderna managed to develop and manufacture vaccines in record-time. The U.S. is still the global leader in life sciences, especially driven by its strong science and technology hubs in the San Francisco Bay Area and the Greater Boston Area.

Prevalence of SARS-CoV-2 related outcomes among participants per study round.