As part of an ongoing partnership with the Census Bureau, the National Center for Health Statistics (NCHS) recently added questions to assess the prevalence of post-COVID-19 conditions (long COVID), on the experimental Household Pulse Survey. This 20-minute online survey was designed to complement the ability of the federal statistical system to rapidly respond and provide relevant information about the impact of the coronavirus pandemic in the U.S. Data collection began on April 23, 2020. Beginning in Phase 3.5 (on June 1, 2022), NCHS included questions about the presence of symptoms of COVID that lasted three months or longer. Phase 3.5 will continue with a two-weeks on, two-weeks off collection and dissemination approach. Estimates on this page are derived from the Household Pulse Survey and show the percentage of adults aged 18 and over who a) as a proportion of the U.S. population, the percentage of adults who EVER experienced post-COVID conditions (long COVID). These adults had COVID and had some symptoms that lasted three months or longer; b) as a proportion of adults who said they ever had COVID, the percentage who EVER experienced post-COVID conditions; c) as a proportion of the U.S. population, the percentage of adults who are CURRENTLY experiencing post-COVID conditions. These adults had COVID, had long-term symptoms, and are still experiencing symptoms; d) as a proportion of adults who said they ever had COVID, the percentage who are CURRENTLY experiencing post-COVID conditions; and e) as a proportion of the U.S. population, the percentage of adults who said they ever had COVID.

From July 2021 to June 2022, American Indians or Alaska Natives were the ethnic group reporting the highest death rate from Long COVID per million population in the United States. Among this ethnic group, the mortality rate from COVID-19 was about 1,795 deaths per million population, while nearly 15 individuals per million died due to Long COVID. This statistic shows the death rates from COVID-19 and Long COVID per million population in the U.S. from July 2021 to June 2022, by race and ethnicity.

According to a survey conducted between August and September 2024, the number of adults in the U.S. previously diagnosed with COVID-19 and reporting Long COVID symptoms three months or longer after infection had reached over 38 million. By state, California registered the highest number of adults experiencing post-COVID symptoms, with more than 4.5 million people. This statistic shows the number of adults with Long COVID symptoms in the United States as of 2024, by state.

As of June 2022, death rates from COVID-19 and Long COVID per million people in the United States were both higher among individuals aged 85 and older. Within the analyzed period, approximately 117 people per million in this age group died due to Long COVID, and around 14,122 individuals per million died from COVID-19. This statistic shows the death rates from COVID-19 and Long COVID per million population in the United States from July 2021 to June 2022, by age group.

The dataset includes the complete, reproducible search strategies for all literature databases searched during this project.

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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

According to a survey conducted in the United Kingdom (UK) in April 2022, 4.13 percent of all people aged between 35 and 49 years reported to be suffering from long COVID symptoms, the highest share across all age groups. Furthermore, around 3.7 percent of the population aged 50 to 69 years were estimated to suffer from long COVID. Overall, around 863 thousand people in the UK reported their ability to undertake daily activities and routines was affected a little by long COVID symptoms.

Present state of COVID-19 As of May 2022, over 22 million COVID-19 cases had been reported in the UK. The largest surge of cases was noted over the winter period 2021/22. The incidence of cases in the county since the pandemic began stood at around 32,624 per 100,000 population. Cyprus had the highest incidence of COVID-19 cases among its population in Europe at 75,798 per 100,000 people, followed by a rate of 51,573 in Iceland. Over 175 thousand COVID-19 deaths have been reported in the UK. The deadliest day on record was January 20, 2021, when 1,820 deaths were recorded. In the UK, a COVID-19 death is defined as a person who died within 28 days of a positive test.

Preventing long COVID through vaccination According to the WHO, being fully vaccinated alongside a significant proportion of the population also vaccinated is the best way to avoid the spread of COVID-19 or serious symptoms associated with the virus. It is therefore regarded that receiving a vaccine course as well as subsequent booster vaccines limits the chance of developing long COVID symptoms. As of April 27, 2022, around 53.2 million first doses, 49.7 million second doses, and 39.2 booster doses had been administered in the UK.

A cohort study from a tertiary care hospital of Eastern India, which captures the incidence, characteristics and predictor of Long COVID among diagnosed cases of COVID-19, during four weeks and six months of follow-up.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

BackgroundCoronavirus disease 2019 (COVID-19) may lead to long-term sequelae. This study aimed to understand the acute and post-acute burden of SARS-CoV-2 infection and to identify high-risk groups for post-COVID-19 conditions (PCC).MethodsA retrospective observational study of the Bahraini population was conducted between 1 May 2021 and 30 April 2023, utilizing the national administrative database. PCC cases were defined according to WHO guidelines. All COVID-19 cases were confirmed using real-time polymerase chain reaction (PCR).ResultsOf 13,067 COVID-19 cases, 12,022 of them experienced acute COVID-19, and 1,045 of them developed PCC. Individuals with PCC tended to be older women with risk factors and instances of SARS-CoV-2 reinfection. The incidence rates per 100,000 individuals during the Alpha pandemic surge (2020), Delta pandemic surge (2021), and Omicron pandemic surge (2022) were 2.2, 137.2, and 222.5 for acute COVID-19, and 0.27, 10.5, and 19.3, respectively, for PCC cases. The death rates per 100,000 individuals during the Alpha, Delta, and Omicron pandemic surges were 3, 112, and 76, respectively, for acute COVID-19 and 1, 10, and 8, respectively, for PCC. The death rate was highest among those aged 65 and older during the Delta pandemic surge.ConclusionThese findings suggest the need for a timely national vaccination program prior to new COVID-19 surges to prevent complications related to SARS-CoV-2 infection, particularly in the older adult and in non-older adult individuals with risk factors.

Note: Starting April 27, 2023 updates change from daily to weekly.

Summary The cumulative number of positive COVID-19 cases among Maryland residents by gender: Female; Male; Unknown.

Description The MD COVID-19 - Cases by Gender Distribution data layer is a collection of positive COVID-19 test results that have been reported each day by the local health department via the ESSENCE system.

Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Note: Starting April 27, 2023 updates change from daily to weekly. Summary The cumulative number of positive COVID-19 cases among Maryland residents within a single Maryland jurisdiction. Description The MD COVID-19 - Cases by County data layer is a collection of positive COVID-19 test results that have been reported each day by the local health department via the ESSENCE system. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Actions of Public Health France

Public Health France’s mission is to improve and protect the health of populations. During the health crisis linked to the COVID-19 outbreak, Santé publique France is responsible for monitoring and understanding the dynamics of the epidemic, anticipating the various scenarios and putting in place actions to prevent and limit the transmission of this virus on national territory.

The Tracking Information System (SI-DEP)

The new screening information system (SI-DEP), which has been in operation since 13 May 2020, is a secure platform where the results of the laboratory tests carried out by all city and hospital laboratories for SARS-COV2 are systematically recorded.

The creation of this information system is authorised for a period of 6 months from the end of the state of health emergency by application of Decree No 2020-551 of 12 May 2020 on the information systems referred to in Article 11 of Law No 2020-546 of 11 May 2020 extending the state of health emergency and supplementing its provisions.

Description of data

This dataset provides information at the departmental and regional level: — the daily and weekly incidence rate per age group; — the daily and weekly standardised incidence rate; — the sliding standardised incidence rate.

This dataset provides information at the national level: — the daily and weekly incidence rate by age group and sex; — the daily and weekly standardised incidence rate; — the sliding standardised incidence rate.

The incidence rate corresponds to the number of positive tests per 100,000 inhabitants. It shall be calculated as follows: (100000 * number of positive cases)/Population

Accuracy: — From 29/08 onwards, laboratory data indicators (SI-DEP) show rates of incidence, positivity and screening adjusted for screenings conducted at airports upon arrival of international flights. — For more information, see the methodological note available in the resources. Limits: — Only the biological tests of persons for whom the residence department could be located are shown on the maps. Persons whose department could not be traced in the SIDEP data are counted only at the whole French level. As a result, the sum of the tests indicated in the departments or regions is less than the number of tests indicated in France. — The time limit for repeating tests may exceed 9 days in some cases. The indicators are adjusted daily according to the receipt of the results.

Notable changes

Since 8 December, after verifying the quality of the reported data, all results of RT-PCR or Antigenic tests have been included in the production of national and territorial epidemiological indicators (incidence rates, positivity rates and screening rates) relevant to the monitoring of the COVID-19 outbreak. On the other hand, the epidemic is prolonging in time and screening capacities have increased, leading to an increasing frequency of people tested several times. Thus, an adjustment of the methods of splitting for patients benefiting from repeated tests and therefore the definition of the persons tested was necessary. Public Health France, in its patient-centred epidemiological approach, has therefore adapted its methods to ensure that these indicators reflect, in particular, the proportion of infected people among the population tested. These developments have no impact on the trends and interpretation of the dynamics of the epidemic, which remain the same. More precise test data (impact and positivity) are also published by Santé publique France (SI-DEP data).

The likelihood of new-onset, self-reported long COVID after a second coronavirus (COVID-19) infection compared with a first infection, using data from the COVID-19 Infection Survey. Experimental Statistics.

According to a survey conducted from August 20 to September 16, 2024, roughly seven percent of all adults in the United States aged 40 to 49 years who previously had COVID-19 were currently experiencing Long COVID. Adults from this age group had the highest share of respondents suffering from Long COVID conditions. This statistic shows the percentage of all adults in the United States currently experiencing Long COVID from August 20 to September 16, 2024, by age group.

The dataset summarizes counts and rates of cumulative COVID-19 cases by cities in Santa Clara County. Source: California Reportable Disease Information Exchange

This dataset is updated every Thursday.

GIS Feature class polygon of Zip codes in Jefferson County joined with Latest Confirmed Cases by Zip code with Long Term Care and Population of 2019 ACS Demographic Data by Zip code. This feature is used in the Covid-19 Jefferson County Public Hub Site https://covid-19-in-jefferson-county-ky-lojic.hub.arcgis.com/Note: This data is preliminary, routinely updated, and is subject to change.For questions about this data please contact Angela Graham (Angela.Graham@louisvilleky.gov) or YuTing Chen (YuTing.Chen@louisvilleky.gov) or call (502) 574-8279.

Daily count of NYC residents who tested positive for SARS-CoV-2, who were hospitalized with COVID-19, and deaths among COVID-19 patients. Note that this dataset currently pulls from https://raw.githubusercontent.com/nychealth/coronavirus-data/master/trends/data-by-day.csv on a daily basis.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

The dataset provides information on cases among residents and staff at LTCFs, which are a critical part of the continuum of health care. LTCFs include skilled nursing, independent living, assisted living and board and care facilities. Source: California Reportable Disease Information Exchange. Data Notes: These data may represent ongoing investigations and as such may change as additional information are collected. Only LTCFs within Santa Clara County are listed. Residents and staff working at these facilities and who are residents of Santa Clara County are included in these counts.

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