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.

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

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

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.Revisions added on 4/23/2020 are highlighted.Revisions added on 4/30/2020 are highlighted.Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Correction on 6/1/2020Methodology 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.Reasons for undertaking this work: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 15-30 days + 5% from past 31-56 days - total deaths.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 used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. Back-casting revisions: In the Johns Hopkins’ data, the structure is to provide the cumulative number of cases per day, which presumes an ever-increasing sequence of numbers, e.g., 0,0,1,1,2,5,7,7,7, etc. However, revisions do occur and would look like, 0,0,1,1,2,5,7,7,6. To accommodate this, we revised the lists to eliminate decreases, which make this list look like, 0,0,1,1,2,5,6,6,6.Reporting Interval: In the early weeks, Johns Hopkins' data provided reporting every day regardless of change. In late April, this changed allowing for days to be skipped if no new data was available. The day was still included, but the value of total cases was set to Null. The processing therefore was updated to include tracking of the spacing between intervals with valid values.100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent fourteen days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 42 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 14 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 14 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 14 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 14 days and less than past 2 days indicates slight positive trend, but likely still within peak trend time frame.Past five days is less than the past 14 days. This means a downward trend. This would be an

Overview and Inspiration

I wanted to make some geospatial visualizations to convey the current severity of COVID19 in different parts of the U.S..

I liked the NYTimes COVID dataset, but it was lacking information on county boundary shape data, population per county, new cases / deaths per day, and per capita calculations, and county demographics.

After a lot of work tracking down the different data sources I wanted and doing all of the data wrangling and joins in python, I wanted to open-source the final enriched data set in order to give others a head start in their COVID-19 related analytic, modeling, and visualization efforts.

This dataset is enriched with county shapes, county center point coordinates, 2019 census population estimates, county population densities, cases and deaths per capita, and calculated per day cases / deaths metrics. It contains daily data per county back to January, allowing for analyizng changes over time.

UPDATE: I have also included demographic information per county, including ages, races, and gender breakdown. This could help determine which counties are most susceptible to an outbreak.

How this data can be used

Geospatial analysis and visualization - Which counties are currently getting hit the hardest (per capita and totals)? - What patterns are there in the spread of the virus across counties? (network based spread simulations using county center lat / lons) -county population densities play a role in how quickly the virus spreads? -how does a specific county/state cases and deaths compare to other counties/states? Join with other county level datasets easily (with fips code column)

Content Details

See the column descriptions for more details on the dataset

Visualizations and Analysis Examples

COVID-19 U.S. Time-lapse: Confirmed Cases per County (per capita)

https://github.com/ringhilterra/enriched-covid19-data/blob/master/example_viz/covid-cases-final-04-06.gif?raw=true" alt="">-

Other Data Notes

• Please review nytimes README for detailed notes on Covid-19 data - https://github.com/nytimes/covid-19-data/
• The only update I made in regards to 'Geographic Exceptions', is that I took 'New York City' county provided in the Covid-19 data, which has all cases for 'for the five boroughs of New York City (New York, Kings, Queens, Bronx and Richmond counties) and replaced the missing FIPS for those rows with the 'New York County' fips code 36061. That way I could join to a geometry, and then I used the sum of those five boroughs population estimates for the 'New York City' estimate, which allowed me calculate 'per capita' metrics for 'New York City' entries in the Covid-19 dataset

Acknowledgements

• Special thanks to NYTimes for all of their hard work gathering and consolidating all of the U.S. COVID19 related data on daily basis. Their git repo https://github.com/nytimes/covid-19-data/
• Also, thanks to ykzeng for the county population density estimates: https://github.com/ykzeng/covid-19/tree/master/data-

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

As of March 10, 2023, the death rate from COVID-19 in the state of New York was 397 per 100,000 people. New York is one of the states with the highest number of COVID-19 cases.

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

Context

This is the US Coronavirus data repository from The New York Times . This data includes COVID-19 cases and deaths reported by state and county. The New York Times compiled this data based on reports from state and local health agencies. More information on the data repository is available here . For additional reporting and data visualizations, see The New York Times’ U.S. coronavirus interactive site

Sample Queries

Query 1

Which US counties have the most confirmed cases per capita? This query determines which counties have the most cases per 100,000 residents. Note that this may differ from similar queries of other datasets because of differences in reporting lag, methodologies, or other dataset differences.

SELECT covid19.county, covid19.state_name, total_pop AS county_population, confirmed_cases, ROUND(confirmed_cases/total_pop *100000,2) AS confirmed_cases_per_100000, deaths, ROUND(deaths/total_pop *100000,2) AS deaths_per_100000 FROM bigquery-public-data.covid19_nyt.us_counties covid19 JOIN bigquery-public-data.census_bureau_acs.county_2017_5yr acs ON covid19.county_fips_code = acs.geo_id WHERE date = DATE_SUB(CURRENT_DATE(),INTERVAL 1 day) AND covid19.county_fips_code != "00000" ORDER BY confirmed_cases_per_100000 desc

Query 2

How do I calculate the number of new COVID-19 cases per day? This query determines the total number of new cases in each state for each day available in the dataset SELECT b.state_name, b.date, MAX(b.confirmed_cases - a.confirmed_cases) AS daily_confirmed_cases FROM (SELECT state_name AS state, state_fips_code , confirmed_cases, DATE_ADD(date, INTERVAL 1 day) AS date_shift FROM bigquery-public-data.covid19_nyt.us_states WHERE confirmed_cases + deaths > 0) a JOIN bigquery-public-data.covid19_nyt.us_states b ON a.state_fips_code = b.state_fips_code AND a.date_shift = b.date GROUP BY b.state_name, date ORDER BY date desc

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

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

Overview

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post, Mapping 2019-nCoV (https://systems.jhu.edu/research/public-health/ncov/), and included data sources are listed here: https://github.com/CSSEGISandData/COVID-19

Sample Query 1

How many confirmed COVID-19 cases were there in the US, by state? This query determines the total number of cases by province in February. A "province_state" can refer to any subset of the US in this particular dataset, including a county or state. SELECT province_state, confirmed AS feb_confirmed_cases, FROM bigquery-public-data.covid19_jhu_csse.summary WHERE country_region = "US" AND date = '2020-02-29' ORDER BY feb_confirmed_cases desc

Sample Query 2

Which countries with the highest number of confirmed cases have the most per capita? This query joins the Johns Hopkins dataset with the World Bank's global population data to determine which countries among those with the highest total number of confirmed cases have the most confirmed cases per capita.

with country_pop AS( SELECT IF(country = "United States","US",IF(country="Iran, Islamic Rep.","Iran",country)) AS country, year_2018 FROM bigquery-public-data.world_bank_global_population.population_by_country)

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "US" AND country_pop.country = "US" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "France" AND country_pop.country = "France" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, SUM(cases.confirmed) AS total_confirmed_cases, SUM(cases.confirmed)/AVG(country_pop.year_2018) * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region = "China" AND country_pop.country = "China" AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day)

GROUP BY country_region, date

UNION ALL

SELECT cases.date AS date, cases.country_region AS country_region, cases.confirmed AS total_confirmed_cases, cases.confirmed/country_pop.year_2018 * 100000 AS confirmed_cases_per_100000 FROM bigquery-public-data.covid19_jhu_csse.summary cases JOIN country_pop ON cases.country_region LIKE CONCAT('%',country_pop.country,'%') WHERE cases.country_region IN ("Italy", "Spain", "Germany", "Iran") AND cases.date = DATE_SUB(current_date(),INTERVAL 1 day) ORDER BY confirmed_cases_per_100000 desc

Dataset source

JHU CSSE

Update frequency

Daily

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had spread to almost every country in the world, and more than 6.86 million people had died after contracting the respiratory virus. Over 1.16 million of these deaths occurred in the United States.

Waves of infections Almost every country and territory worldwide have been affected by the COVID-19 disease. At the end of 2021 the virus was once again circulating at very high rates, even in countries with relatively high vaccination rates such as the United States and Germany. As rates of new infections increased, some countries in Europe, like Germany and Austria, tightened restrictions once again, specifically targeting those who were not yet vaccinated. However, by spring 2022, rates of new infections had decreased in many countries and restrictions were once again lifted.

What are the symptoms of the virus? It can take up to 14 days for symptoms of the illness to start being noticed. The most commonly reported symptoms are a fever and a dry cough, leading to shortness of breath. The early symptoms are similar to other common viruses such as the common cold and flu. These illnesses spread more during cold months, but there is no conclusive evidence to suggest that temperature impacts the spread of the SARS-CoV-2 virus. Medical advice should be sought if you are experiencing any of these symptoms.

Florida COVID-19 Cases by Zip Code exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/.https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_Cases_Zips_COVID19/FeatureServerFor data 5/10/2020 or after: Archived data was exported directly from the live FDOH layer into the archive. For data prior to 5/10/2020: Data was exported by the University of South Florida - Digital Heritage and Humanities Collection using ArcGIS Pro Software. Data was then converted to shapefile and csv and uploaded into ArcGIS Online archive. For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from FDOH.Q. How is the zip code assigned to a person or case? Cases are counted in a zip code based on residential or mailing address, or by healthcare provider or lab address if other addresses are missing.Q. Why is the city data and the zip code data different? The zip code data is supplied to a healthcare worker, case manager, or lab technician by each individual during intake when a test is first recorded. When entering a zip code, the system we use automatically produces a list of cities within that zip code for the individual to further specify where they live. Sometimes the individual uses the postal city, which may be Miami, when in reality that person lives outside the City of Miami boundaries in the jurisdiction of Coral Gables. Many zip codes contain multiple city/town jurisdictions, and about 20% of zip codes overlap more than one county. Q: How is the Zip Code data calculated and/or shown? If a COUNTY has five or more cases (total): • In zip codes with fewer than 5 cases, the total number of cases is shown as “<5”. • Zip codes with 0 cases in these counties are “0" or "No cases.” • All values of 5 or greater are shown by the actual number of cases in that zip code. If a COUNTY has fewer than five total cases across all of its zip codes, then ALL of the zip codes within that county show the total number of cases as "Suppressed." Q: My zip code says "SUPPRESSED" under cases. What does that mean? IF Suppressed: This county currently has fewer than five cases across all zip codes in the county. In an effort to protect the privacy of our COVID-19-Positive residents, zip code data is only available in counties where five or more cases have been reported. Q: What about PO Box zip codes, or zip codes with letters, like 334MH? PO Box zip codes are not shown in the map. “Filler” zip codes with letters, like 334MH, are typically areas where no or very few people live – like the Florida Everglades, and are shown on the map like any other zip code. Key Data about Cases by Zip Code: ZIP = The zip code COUNTYNAME = The county for the zip code (multi-part counties have been split) ZIPX = The unique county-zip identifier used to pair the data during updates POName = The postal address name assigned to the zip code place_labels = A list of the municipalities intersecting the zip code boundary c_places = The list of cities cases self-reported as being residents of Cases_1 = The number of cases in each zip code, with conditions*LabelY = A calculated field for map display only. All questions regarding this dataset should be directed to the Florida Department of Health.

Florida COVID-19 Cases by Zip Code exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/.https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_Cases_Zips_COVID19/FeatureServerFor data 5/10/2020 or after: Archived data was exported directly from the live FDOH layer into the archive. For data prior to 5/10/2020: Data was exported by the University of South Florida - Digital Heritage and Humanities Collection using ArcGIS Pro Software. Data was then converted to shapefile and csv and uploaded into ArcGIS Online archive. For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from FDOH.Q. How is the zip code assigned to a person or case? Cases are counted in a zip code based on residential or mailing address, or by healthcare provider or lab address if other addresses are missing.Q. Why is the city data and the zip code data different? The zip code data is supplied to a healthcare worker, case manager, or lab technician by each individual during intake when a test is first recorded. When entering a zip code, the system we use automatically produces a list of cities within that zip code for the individual to further specify where they live. Sometimes the individual uses the postal city, which may be Miami, when in reality that person lives outside the City of Miami boundaries in the jurisdiction of Coral Gables. Many zip codes contain multiple city/town jurisdictions, and about 20% of zip codes overlap more than one county. Q: How is the Zip Code data calculated and/or shown? If a COUNTY has five or more cases (total): • In zip codes with fewer than 5 cases, the total number of cases is shown as “<5”. • Zip codes with 0 cases in these counties are “0" or "No cases.” • All values of 5 or greater are shown by the actual number of cases in that zip code. If a COUNTY has fewer than five total cases across all of its zip codes, then ALL of the zip codes within that county show the total number of cases as "Suppressed." Q: My zip code says "SUPPRESSED" under cases. What does that mean? IF Suppressed: This county currently has fewer than five cases across all zip codes in the county. In an effort to protect the privacy of our COVID-19-Positive residents, zip code data is only available in counties where five or more cases have been reported. Q: What about PO Box zip codes, or zip codes with letters, like 334MH? PO Box zip codes are not shown in the map. “Filler” zip codes with letters, like 334MH, are typically areas where no or very few people live – like the Florida Everglades, and are shown on the map like any other zip code. Key Data about Cases by Zip Code: ZIP = The zip code COUNTYNAME = The county for the zip code (multi-part counties have been split) ZIPX = The unique county-zip identifier used to pair the data during updates POName = The postal address name assigned to the zip code place_labels = A list of the municipalities intersecting the zip code boundary c_places = The list of cities cases self-reported as being residents of Cases_1 = The number of cases in each zip code, with conditions*LabelY = A calculated field for map display only. All questions regarding this dataset should be directed to the Florida Department of Health.

NOTE: As of 2/16/2023, This page is not being updated, for data on COVID-19 Updated (Bivalent) Booster Coverage go to https://data.ct.gov/Health-and-Human-Services/COVID-19-Updated-Bivalent-Booster-Coverage-By-Town/bqd5-4jgh. Important change as of June 1, 2022 As of June 1, 2022, we will be using 2020 DPH provisional census estimates* to calculate vaccine coverage percentages at the county level. 2020 estimates will replace the 2019 estimates that have been used. Caution should be taken when making comparisons of percentages calculated using the 2019 and 2020 census estimates since observed difference may result from the shift in the denominator. DPH Provisional State and County Characteristics Estimates April 1, 2020. Hayes L, Abdellatif E, Jiang Y, Backus K (2022) Connecticut DPH Provisional April 1, 2020, State Population Estimates by 18 age groups, sex, and 6 combined race and ethnicity groups. Connecticut Department of Public Health, Health Statistics & Surveillance, SAR, Hartford, CT. This tables shows the number and percent of people that have initiated COVID-19 vaccination, are fully vaccinated and had additional dose 1 by county of residence. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Population size estimates are based on 2019 DPH census estimates until 5/26/2022. From 6/1/2022, 2020 DPH provisional census estimates are used. A person who has received at least one dose of any COVID-19 vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if they have completed a primary series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the number who have initiated vaccination. Percentages are calculated using 2019 census data (https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Annual-Town-and-County-Population-for-Connecticut). A person who completed a Pfizer, Moderna, Novavax or Johnson & Johnson primary series (as defined above) and then had an additional monovalent dose of COVID-19 vaccine is considered to have had additional dose 1. The additional dose may be Pfizer, Moderna, Novavax or Johnson & Johnson and may be a different type from the primary series. For people who had a primary Pfizer or Moderna series, additional dose 1 was counted starting August 18th, 2021. For people with a Johnson & Johnson primary series additional dose 1 was counted starting October 22nd, 2021. For most people, additional dose 1 is a booster. However, additional dose 1 may represent a supplement to the primary series for a people who is moderately or severely immunosuppressed. Bivalent booster administrations are not included in the additional dose 1 calculations. The percent with at least one dose may be over-estimated and the percent fully and additional dose 1 vaccinated may be under-estimated because of vaccine administration records for individuals that cannot be linked because of differences in how names or date of birth are reported. County of residence is determined based on the town of residence. Town of residence is verified by geocoding the reported address and then mapping it to a town using municipal boundaries. If an address cannot be geocoded, the reported town is used, if available. Out-of-state residents vaccinated by CT providers excluded from this table. People for whom an address is not currently available are shown in this table as “Address pending validation”. Connecticut COVID-19 Vaccine Program providers are required to report information on all COVID-19 vaccine doses administered to CT WiZ, the Connecticut Immunization Information System. Data on doses administered to CT residents out-of-state are being added to CT WiZ jurisdiction-by-jurisdiction.

Nairobi has been the Kenyan county most affected by the coronavirus (COVID-19) pandemic. As of March 31, 2022, the capital registered most of the confirmed COVID-19 cases in the country, around 129 thousand. The amount corresponded to nearly 40 percent of the total cases in Kenya. In Kiambu, within the Nairobi Metropolitan Region, 19,778 infected people were registered, whereas Mombasa, Kenya's oldest and second largest city, had 17,794 cases. As of March 2021, Kenya started the vaccination campaign against the coronavirus with doses received through the COVAX initiative.

Kenya's economy rebounds amid vaccination campaign

The coronavirus outbreak had a significant negative impact on Kenya's economy. In the second quarter of 2020, the quarterly country’s GDP decreased by 5.5 percent, the first contraction in recent years. Around one year later, in the third quarter of 2021, Kenya already registered an improved economic performance, with the quarterly GDP growth rate measured at 9.9 percent. The educational sector pushed the result, with an expansion of 65 percent. Mining and quarrying, and accommodation and food services followed, each with a 25 percent growth rate.

Signs of recovery in the tourism sector

Extensively known for its rich nature and wildlife, Kenya felt dramatically the impacts of the COVID-19 pandemic in the tourism industry. The sector's contribution to the country’s GDP roughly halved in 2020, compared to 2019. By the end of 2021, however, signals of recovery were already spotted. The monthly number of arrivals in both Jomo Kenyatta and Moi international airports in December that year corresponded to roughly 70 percent of that registered in December 2019. Additionally, as of March 2022, the bed occupancy rate in Kenyan hotels amounted to 57 percent, against 23 percent in March 2021.

Florida COVID-19 Cases by Zip Code exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/.https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_Cases_Zips_COVID19/FeatureServerFor data 5/10/2020 or after: Archived data was exported directly from the live FDOH layer into the archive. For data prior to 5/10/2020: Data was exported by the University of South Florida - Digital Heritage and Humanities Collection using ArcGIS Pro Software. Data was then converted to shapefile and csv and uploaded into ArcGIS Online archive. For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from FDOH.Q. How is the zip code assigned to a person or case? Cases are counted in a zip code based on residential or mailing address, or by healthcare provider or lab address if other addresses are missing.Q. Why is the city data and the zip code data different? The zip code data is supplied to a healthcare worker, case manager, or lab technician by each individual during intake when a test is first recorded. When entering a zip code, the system we use automatically produces a list of cities within that zip code for the individual to further specify where they live. Sometimes the individual uses the postal city, which may be Miami, when in reality that person lives outside the City of Miami boundaries in the jurisdiction of Coral Gables. Many zip codes contain multiple city/town jurisdictions, and about 20% of zip codes overlap more than one county. Q: How is the Zip Code data calculated and/or shown? If a COUNTY has five or more cases (total): • In zip codes with fewer than 5 cases, the total number of cases is shown as “<5”. • Zip codes with 0 cases in these counties are “0" or "No cases.” • All values of 5 or greater are shown by the actual number of cases in that zip code. If a COUNTY has fewer than five total cases across all of its zip codes, then ALL of the zip codes within that county show the total number of cases as "Suppressed." Q: My zip code says "SUPPRESSED" under cases. What does that mean? IF Suppressed: This county currently has fewer than five cases across all zip codes in the county. In an effort to protect the privacy of our COVID-19-Positive residents, zip code data is only available in counties where five or more cases have been reported. Q: What about PO Box zip codes, or zip codes with letters, like 334MH? PO Box zip codes are not shown in the map. “Filler” zip codes with letters, like 334MH, are typically areas where no or very few people live – like the Florida Everglades, and are shown on the map like any other zip code. Key Data about Cases by Zip Code: ZIP = The zip code COUNTYNAME = The county for the zip code (multi-part counties have been split) ZIPX = The unique county-zip identifier used to pair the data during updates POName = The postal address name assigned to the zip code place_labels = A list of the municipalities intersecting the zip code boundary c_places = The list of cities cases self-reported as being residents of Cases_1 = The number of cases in each zip code, with conditions*LabelY = A calculated field for map display only. All questions regarding this dataset should be directed to the Florida Department of Health.

Florida COVID-19 Cases by Zip Code exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/.https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_Cases_Zips_COVID19/FeatureServerFor data 5/10/2020 or after: Archived data was exported directly from the live FDOH layer into the archive. For data prior to 5/10/2020: Data was exported by the University of South Florida - Digital Heritage and Humanities Collection using ArcGIS Pro Software. Data was then converted to shapefile and csv and uploaded into ArcGIS Online archive. For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from FDOH.Q. How is the zip code assigned to a person or case? Cases are counted in a zip code based on residential or mailing address, or by healthcare provider or lab address if other addresses are missing.Q. Why is the city data and the zip code data different? The zip code data is supplied to a healthcare worker, case manager, or lab technician by each individual during intake when a test is first recorded. When entering a zip code, the system we use automatically produces a list of cities within that zip code for the individual to further specify where they live. Sometimes the individual uses the postal city, which may be Miami, when in reality that person lives outside the City of Miami boundaries in the jurisdiction of Coral Gables. Many zip codes contain multiple city/town jurisdictions, and about 20% of zip codes overlap more than one county. Q: How is the Zip Code data calculated and/or shown? If a COUNTY has five or more cases (total): • In zip codes with fewer than 5 cases, the total number of cases is shown as “<5”. • Zip codes with 0 cases in these counties are “0" or "No cases.” • All values of 5 or greater are shown by the actual number of cases in that zip code. If a COUNTY has fewer than five total cases across all of its zip codes, then ALL of the zip codes within that county show the total number of cases as "Suppressed." Q: My zip code says "SUPPRESSED" under cases. What does that mean? IF Suppressed: This county currently has fewer than five cases across all zip codes in the county. In an effort to protect the privacy of our COVID-19-Positive residents, zip code data is only available in counties where five or more cases have been reported. Q: What about PO Box zip codes, or zip codes with letters, like 334MH? PO Box zip codes are not shown in the map. “Filler” zip codes with letters, like 334MH, are typically areas where no or very few people live – like the Florida Everglades, and are shown on the map like any other zip code. Key Data about Cases by Zip Code: ZIP = The zip code COUNTYNAME = The county for the zip code (multi-part counties have been split) ZIPX = The unique county-zip identifier used to pair the data during updates POName = The postal address name assigned to the zip code place_labels = A list of the municipalities intersecting the zip code boundary c_places = The list of cities cases self-reported as being residents of Cases_1 = The number of cases in each zip code, with conditions*LabelY = A calculated field for map display only. All questions regarding this dataset should be directed to the Florida Department of Health.

Florida COVID-19 Cases by Zip Code exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2020. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/.https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_Cases_Zips_COVID19/FeatureServerFor data 5/10/2020 or after: Archived data was exported directly from the live FDOH layer into the archive. For data prior to 5/10/2020: Data was exported by the University of South Florida - Digital Heritage and Humanities Collection using ArcGIS Pro Software. Data was then converted to shapefile and csv and uploaded into ArcGIS Online archive. For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from FDOH.Q. How is the zip code assigned to a person or case? Cases are counted in a zip code based on residential or mailing address, or by healthcare provider or lab address if other addresses are missing.Q. Why is the city data and the zip code data different? The zip code data is supplied to a healthcare worker, case manager, or lab technician by each individual during intake when a test is first recorded. When entering a zip code, the system we use automatically produces a list of cities within that zip code for the individual to further specify where they live. Sometimes the individual uses the postal city, which may be Miami, when in reality that person lives outside the City of Miami boundaries in the jurisdiction of Coral Gables. Many zip codes contain multiple city/town jurisdictions, and about 20% of zip codes overlap more than one county. Q: How is the Zip Code data calculated and/or shown? If a COUNTY has five or more cases (total): • In zip codes with fewer than 5 cases, the total number of cases is shown as “<5”. • Zip codes with 0 cases in these counties are “0" or "No cases.” • All values of 5 or greater are shown by the actual number of cases in that zip code. If a COUNTY has fewer than five total cases across all of its zip codes, then ALL of the zip codes within that county show the total number of cases as "Suppressed." Q: My zip code says "SUPPRESSED" under cases. What does that mean? IF Suppressed: This county currently has fewer than five cases across all zip codes in the county. In an effort to protect the privacy of our COVID-19-Positive residents, zip code data is only available in counties where five or more cases have been reported. Q: What about PO Box zip codes, or zip codes with letters, like 334MH? PO Box zip codes are not shown in the map. “Filler” zip codes with letters, like 334MH, are typically areas where no or very few people live – like the Florida Everglades, and are shown on the map like any other zip code. Key Data about Cases by Zip Code: ZIP = The zip code COUNTYNAME = The county for the zip code (multi-part counties have been split) ZIPX = The unique county-zip identifier used to pair the data during updates POName = The postal address name assigned to the zip code place_labels = A list of the municipalities intersecting the zip code boundary c_places = The list of cities cases self-reported as being residents of Cases_1 = The number of cases in each zip code, with conditions*LabelY = A calculated field for map display only. All questions regarding this dataset should be directed to the Florida Department of Health.