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.

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

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

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

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

The Indian capital of Delhi had the highest share of districts, at about 27 percent, in the red zone as of April 19, 2020. Red zones marked districts having more than 100 confirmed cases of the coronavirus COVID-19.

Infections in Indian states

Maharashtra confirmed around 13 thousand cases of the coronavirus (COVID-19) as of May 4, 2020, with 548 fatalities and 2,115 recoveries. It was the leading state in terms of number of infections, followed by the states of Gujarat and Delhi. The first case, however, was reported in late January in the southern state of Kerala. Since then the spread of the virus has been consistent and the country is yet to see a drop in the number of infections.

COVID-19 in India

India reported around 42.7 thousand cases of the coronavirus (COVID-19) as of May 4, 2020. The country went into lockdown on March 25, the largest in the world, restricting 1.3 billion people and extended until May 3, 2020. The lockdown had been until May 17, 2020.

Protest against containment policies in US states is fueled by two drivers: the stringency of containment policies and the partisan control over the governorship and legislatures in each state. In our analysis over the period from March 2020 to March 2022, we find more protest events in states fully controlled by Democrats than in Republican controlled states both in a sample consisting of all states and a balanced sample in which we constrain observations to those red and blue states with on average similarly stringent containment policies. Protest was therefore politicized and we find that partisanship exerts a roughly equal substantive effect on the number of protest events as the stringency of containment policies. If we assume no direct effect of partisanship on protest but allow for causal heterogeneity along partisan lines in the effect of containment policies, we find that the same increase in the stringency of policies evokes a stronger protest response in blue states than in red states.

This web map utilizes an outside feature layer created by Johns Hopkins University.This map is not affiliated with Johns Hopkins University, it's team of researchers or any other persons involved in the creation or maintenance of this source feature layer. Any any all rights to source content are retained by the creators and developers of said content.This web map visually depicts statewide range of COVID-19 cases and deaths (updated daily) with additional hospital capacity data and ACS socioeconomic, age and ethnicity indicators included.Description of original feature layer from source site included below: This feature layer contains the most up-to-date COVID-19 cases for the US. Data is pulled from the Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, the Red Cross, the Census American Community Survey, and the Bureau of Labor and Statistics, and aggregated at the US county level. Visit original feature layer page here.Visit the Johns Hopkins University COVID-19 United States Cases by County Dashboard here.We would like to formally thank Johns Hopkins University and it's researchers for all of the work they have contributed to analyzing and fighting the COVID pandemic and for graciously making their work publicly available online and through the ArcGIS platform. We appreciate their efforts more than we can fully express and would like to dedicate this map to them and everyone effected by the pandemic.

Timeline of COVID-19 policies and mandates that affect feeling anxious and depressed.

The Indian state of Punjab reported the highest number of active coronavirus (COVID-19) cases of over one thousand cases as of October 20, 2023. Kerala and Karnataka followed, with relatively lower casualties. That day, there were a total of over 44 million confirmed infections across India.

This web map utilizes an outside feature layer created by Johns Hopkins University.This map is not affiliated with Johns Hopkins University, it's team of researchers or any other persons involved in the creation or maintenance of this source feature layer. Any any all rights to source content are retained by the creators and developers of said content.This web map visually depicts statewide range of COVID-19 cases and deaths (updated daily) with additional hospital capacity data and ACS socioeconomic, age and ethnicity indicators included.Description of original feature layer from source site included below: This feature layer contains the most up-to-date COVID-19 cases for the US. Data is pulled from the Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, the Red Cross, the Census American Community Survey, and the Bureau of Labor and Statistics, and aggregated at the US county level. Visit original feature layer page here.Visit the Johns Hopkins University COVID-19 United States Cases by County Dashboard here.We would like to formally thank Johns Hopkins University and it's researchers for all of the work they have contributed to analyzing and fighting the COVID pandemic and for graciously making their work publicly available online and through the ArcGIS platform. We appreciate their efforts more than we can fully express and would like to dedicate this map to them and everyone effected by the pandemic.

Context

Indian Ministry of Home Affairs has released a list of Indian districts categorized into 3 zones. As of 30th April.

Content

The zones are: 1. Green Zone: Least impacted zone, A district will be considered under green zone if there has been no confirmed cases of COVID-19 so far or there is no reported case since last 21 days in the district. 2. Orange Zone: Districts that do not have enough confirmed cases to meet the ‘red zone’, but are being seen as potential hotspots, are part of the ‘orange zone’. A Red Zone can be categorised as a Orange Zone if no new confirmed case is reported there for 14 consecutive days. 3. Red Zone: Districts reporting a large number of cases or high growth rates. Inclusion criteria for Red Zone: - Highest case-load districts contributing to over 80 percent of cases in India, or - Highest case-load districts contributing to more than 80 percent of cases for each state in the country, or - Districts with doubling rate at less than four days (calculated every Monday for last seven days, to be determined by the state government).

Acknowledgements

This data is fetched from this news website and converted into CSV format: https://www.news18.com/news/india/centre-marks-all-metro-city-as-red-zones-for-covid-19-curbs-post-may-3-heres-the-full-list-2600595.html

And the Ministry of Home Affair's advisory is downloaded from: https://www.moneycontrol.com/news/india/coronavirus-crisis-hotspots-red-zone-orange-zone-and-green-zone-heres-all-you-need-to-know-which-districts-areas-5196691.html

ObjectivesDue to political polarization, adherence to public health measures varied across US states during the COVID-19 pandemic. Although social media posts have been shown effective in anticipating COVID-19 surges, the impact of political leaning on the effectiveness of different topics for early warning remains mostly unexplored. Our study examines the spatio-temporal early warning potential of different geo-social media topics across republican, democrat, and swing states.MethodsUsing keyword filtering, we identified eight COVID-19-related geo-social media topics. We then utilized Chatterjee's rank correlation to assess their early warning capability for COVID-19 cases 7 to 42 days in advance across six infection waves. A mixed-effect model was used to evaluate the impact of timeframe and political leaning on the early warning capabilities of these topics.ResultsMany topics exhibited significant spatial clustering over time, with quarantine and vaccination-related posts occurring in opposing spatial regimes in the second timeframe. We also found significant variation in the early warning capabilities of geo-social media topics over time and across political clusters. In detail, quarantine related geo-social media post were significantly less correlated to COVID-19 cases in republican states than in democrat states. Further, preventive measure and quarantine-related posts exhibited declining correlations to COVID-19 cases over time, while the correlations of vaccine and virus-related posts with COVID-19 infections.ConclusionOur results highlight the need for a dynamic spatially targeted approach that accounts for both how regional geosocial media topics of interest change over time and the impact of local political ideology on their epidemiological early warning capabilities.

Timeline of COVID-19 policies and mandates that affect finances.

Context

The Red, Orange and Green Zone classification is based on factors such as the number of novel coronavirus cases, the doubling rate of Covid-19 cases, and the extent of testing and surveillance. Red Zones have a high number of cases and a high doubling rate, Orange Zones have comparatively fewer cases and Green Zones have not had any cases in the last 21 days.

Content

Here is the full list of districts and their zone classification. This classification comes into effect from May 4 and will last for around a week after which it will be revised. This list is based on the classification of the central government; states and Union Territories may make some modifications.

Inspiration

https://www.indiatoday.in/india/story/red-orange-green-zones-full-current-update-list-districts-states-india-coronavirus-1673358-2020-05-01

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases for the US. Data is pulled from the Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, the Red Cross, the Census American Community Survey, and the Bureau of Labor and Statistics, and aggregated at the US county level. This web map created and maintained by the Centers for Civic Impact at the Johns Hopkins University, and is supported by the Esri Living Atlas team and JHU Data Services. It is used in the COVID-19 United States Cases by County dashboard. For more information on Johns Hopkins University’s response to COVID-19, visit the Johns Hopkins Coronavirus Resource Center where our experts help to advance understanding of the virus, inform the public, and brief policymakers in order to guide a response, improve care, and save lives.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases for the US. Data is pulled from the Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, the Red Cross, the Census American Community Survey, and the Bureau of Labor and Statistics, and aggregated at the US county level. This web map created and maintained by the Centers for Civic Impact at the Johns Hopkins University, and is supported by the Esri Living Atlas team and JHU Data Services. It is used in the COVID-19 United States Cases by County dashboard. For more information on Johns Hopkins University’s response to COVID-19, visit the Johns Hopkins Coronavirus Resource Center where our experts help to advance understanding of the virus, inform the public, and brief policymakers in order to guide a response, improve care, and save lives.

CDC is collaborating with Vitalant Research Institute, American Red Cross, and Westat Inc. to conduct a nationwide COVID-19 seroprevalence survey of blood donors. De-identified blood samples are tested for antibodies to SARS-CoV-2 to better understand the percentage of people in the United States who have antibodies against SARS-CoV-2 (the virus that causes COVID-19) and to track how this percentage changes over time. Both SARS-CoV-2 infection and COVID-19 vaccines currently used in the United States result in production of anti-spike (anti-S) antibodies but only infection results in production of anti-nucleocapsid (anti-N) antibodies. Infection-induced seroprevalence estimates the proportion of the population with antibody evidence of previous SARS-CoV-2 infection and refers to the percent of the population with anti-nucleocapsid antibodies. Combined infection-Induced and Vaccination-Induced seroprevalence estimates the proportion of the population with antibody evidence of previous SARS-CoV-2 infection, COVID-19 vaccination, or both, and refers to the percent of the population that has anti-spike antibodies, anti-nucleocapsid antibodies, or both. This link connects to a webpage that displays the data from the Nationwide Blood Donor Seroprevalence Survey. It offers an interactive visualization available at https://covid.cdc.gov/covid-data-tracker/#nationwide-blood-donor-seroprevalence-2022

In this analysis, we highlight red counties that have combinations of a high percentage of vulnerable populations and high rates of COVID-19, and that are also adjacent to counties with similarly high values. We calculate and map a Local Indicator of Spatial Association (LISA) for pairs of variables in counties in the contiguous United States.

There was a large spike in gun purchases and gun violence during the first year of the COVID-19 pandemic in the United States. We used an online U.S. national survey (N = 1036) to examine the characteristics of people who purchased a gun between March 2020 and October 2021 (n = 103) and compared them to non-gun owners (n = 763) and people who own a gun but did not purchase a gun during the COVID-19 pandemic (n = 170). Compared to non-gun owners, pandemic gun buyers were younger and more likely to be male, White race, and to affiliate with the Republican party. Compared to non-gun owners and pre-pandemic gun owners, pandemic gun buyers exhibited extreme elevations on a constellation of political (QAnon beliefs, pro-gun attitudes, Christian Nationalism, approval of former President Donald Trump, anti-vax beliefs, COVID-19 skepticism; mean Cohen’s d = 1.15), behavioral (intimate partner violence, antisocial behavior; mean d = 1.38), mental health (suicidality, depression, anxiety, substance use; mean d = 1.21), and personality (desire for power, belief in a dangerous world, low agreeableness, low conscientiousness; mean d = 0.95) characteristics. In contrast, pre-pandemic gun owners only endorsed more pro-gun attitudes (d = 0.67), lower approval of President Joe Biden (d = -0.41) and were more likely to be male and affiliate with the Republican party relative to non-gun owners. Pandemic gun buyers represent an extreme group in terms of political and psychological characteristics including several risk-factors for violence and self-harm.

How to Read the map.This map allows you to visualize the trends over time and cases, recoveries, deaths and testing at the regional health unit. The Map shows the relative state of the COVID-19 outbreak in each region. Colour (red to green) shows the time since a new reported case.

7 Day Hot Spots

The map highlights regions with an active outbreak with a "glowing ball". The size of the ball reflects the average number of new cases in the past 7 days as a rate per 100K population.

High

Low

Important InformationNot all data is reported for all regional health units. Data sources are consulted every 24 hours, however not all organizations report on a daily bases. As this data is cumulative, values carry-forward if updates are not provided. Values can go down due to corrected errors as reported. Data SourcesThe source of the data for each regional health unit is listed in the "SourceURL" field.

Looking for the raw data? You can find it here.

In 2019, there were 27 Republican governors and 23 Democratic governors in office. The number of both Republican and Democratic governors has been fluctuating since 1990, with Democratic governors seeing a surge from 2018 to 2019.

Opinions of governors

The top 10 most popular governorsas of December 2019, were all Republicans, while six out of the top 10 least popular governors were Democrats, which includes New York governor Andrew Cuomo. However, amid the COVID-19 pandemic, Cuomo received favorable opinions from both political parties in his state. Across all states, governors have received overall positive opinions from residents about their responses to the George Floyd protests, while members of Congress and the President have received negative reactions.

Women in politics

Women have been consistently underrepresented in politics since the creation of the United States, women were not allowed to vote until the ratification of the 19th Amendment in 1920, and all women did not get the right to vote until 1965 with the passage of the Voting Rights Act. However, the number of women in politics has been increasing since then. Arizona has had the most female governors, but states like California, Florida, and New York have yet to see a female governor. In Congress, the Democratic Party that had three times the number of female nominees for congressional and gubernatorial positions compared to the Republican Party.

The following layer shows hotspot areas as delineated by NY State government. The layer shows red, orange, and yellow zones and provides activity guidance via attributes.