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.

Abstract

The ongoing coronavirus pandemic, along with the preventive measures designed to slow its spread, are putting great stress on India's economy, and affecting the lives and livelihoods of millions of people, including refugees across the country. To determine the exact social and economic consequences of the crisis, UNDP and UNICEF, are working under the leadership of the UN Resident Coordinators, and in close collaboration with specialized UN agencies, to assess the socio-economic impacts of the COVID-19 pandemic on vulnerable communities. UNHCR led the socio economic impact assessment for refugee population in India. The assessment was conducted in collaboration with UNICEF and in partnership with BOSCO.

As of June 2020, 40,068 refugees and asylum seekers from different nationalities are registered with UNHCR in India (28,053 refugees and 12,015 asylum seekers). Approximately 51% of the population registered with UNHCR lives in Delhi NCR, the remaining population live throughout the country, with bigger groups in Hyderabad, Jammu and Mewat. Rohingya are the largest group of persons of concern to UNHCR in India with 17,772 persons, followed by Afghans (15,806 persons). Of the total population registered with UNHCR, 47% are women and girls while 16% are persons with specific needs.

The survival mechanism for most of the refugees and asylum seekers is mainly based on a daily income that is immensely challenged with the ongoing lockdown and restriction of movement introduced by the central and state governments. These restrictions make it impossible for asylum seekers and refugees to reach the location of their informal employment or daily income generating activities, or to receive customers for their goods and services. Their income and possible savings have dried up leaving them with no means to adequately provide for their families, including in the areas of food, shelter and medicine

Geographic coverage

National

Analysis unit

Individuals and households

Universe

All refugees registered by UNHCR in India.

Kind of data

Sample survey data [ssd]

Sampling procedure

Clustered random sampling, with clusters divided by region (Delhi, outside Delhi), and legal status (Asylum seekers and Refugees).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

Questionnaires included 9 modules: 1. General information 2. Awareness of COVID outbreak 3. Current work situation and impact on household income 4. Social protection at times of lockdown 5. Life at times of lockdown 6. Scenario of work during lockdown relaxation/after lockdown 7. Protection 8. Education/Children's Protection/SGBV 9. General questions

Cleaning operations

Data was cleaned and anonymized for licensed use.

India reported almost 45 million cases of the coronavirus (COVID-19) as of October 20, 2023, with more than 44 million recoveries and about 532 thousand fatalities. The number of cases in the country had a decreasing trend in the past months.

Burden on the healthcare system

With the world's second largest population in addition to an even worse second wave of the coronavirus pandemic seems to be crushing an already inadequate healthcare system. Despite vast numbers being vaccinated, a new variant seemed to be affecting younger age groups this time around. The lack of ICU beds, black market sales of oxygen cylinders and drugs needed to treat COVID-19, as well as overworked crematoriums resorting to mass burials added to the woes of the country. Foreign aid was promised from various countries including the United States, France, Germany and the United Kingdom. Additionally, funding from the central government was expected to boost vaccine production.

Situation overview
Even though days in April 2021 saw record-breaking numbers compared to any other country worldwide, a nation-wide lockdown has not been implemented. The largest religious gathering - the Kumbh Mela, sacred to the Hindus, along with election rallies in certain states continue to be held. Some states and union territories including Maharashtra, Delhi, and Karnataka had issued curfews and lockdowns to try to curb the spread of infections.

As of **********, in the state of Haryana, free meals were provided to more than **** million people who were stranded due to the COVID-19 pandemic. Most of these people were migrant laborers and poor people, who were badly affected due to the loss of livelihood. Of these **** million people, the state government provided the food for ***** percent, while the rest come from non-governmental organizations. Delhi with **** million was in second place. Delhi and Haryana together made approximately ** percent of all meals. As of **********, in all, **** million people were provided with free food across India since the start of the lockdown. The country went into lockdown on **************, the largest in the world, restricting *** billion people. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

A majority of the coronavirus (COVID-19) cases in India affected people between ages 31 and 40 years as of October 18, 2021. Of these, the highest share of deaths during the measured time period was observed in people under the age of 50 years.

Seroprevalence of 67.6% is used with 765 million infectionsa from an age-adjusted population as of 14 Jun-6 Jul 2021 from the 4th nationwide serosurvey [6].

Since March 2020 , with the declaration of lockdown for 21 days , everything changed in India from the way we communicate , our education system and above all a downfall in the economy which we are facing from the past one and a half year with lockdown taking place in gaps , we have experience the first wave , the second wave which we are going through is worst than the first one , millions of people died across the world .In India during the second wave ,the country which was hit the hardest by the second wave saw the die of lakhs of people per day ,there was lack of basic medical facilitates Lakh of cases have been reported .only 30 crore people have been vaccinated as per June 2021 , where as the population stand in billions , everyday lakh of people are getting vaccinated .There has been an improvement in the natural environment and a Decline in the economy . The migrant and the poor have been hit the hard who are depended on daily wages .With every thing going online , the electronic industry has earned profit with huge demand for Laptop , smartphones , headphones , the companies sold produces 10 time more than the normal days since the last one year . COVID-19 is a catastrophe which brough in some positive changes also .

In a survey conducted on the impact of COVID-19 in India in March 2022, a majority of participants reported a net increase in spending across categories like groceries with a share of ** percent expecting to buy lesser quantity. However, a drop in spending was observed for categories related to leisure, travel, and dining in restaurants.

Spending models The COVID-19 pandemic has had a grave impact on the Indian economy which come with its own array of setbacks indicating a drastic change in the pattern of market dynamics. It was observed that during the pandemic, people’s spending models changed from one of indulging to hoarding. People spent less of their income on items that were perceived as non-essential such as clothing, make up, jewelry, toys and games and electronics. By inference, more money was spent on purchase of essential goods, particularly groceries and other food items. The second wave and the economy The nation’s battle with the coronavirus continues bringing in the second wave. This has prompted a reimposition of strict measures including partial lockdowns and curfews in certain states to keep the contagion under control. Experts have postulated a more virulent mutation of the virus could make the second wave even deadlier. While the economy has not yet fully recovered from the first wave of the pandemic following the lockdown imposed in March 2020, India’s recovery signals a slowdown. In the case of further lockdowns, it could lead to an economic recession. Some of the worst hit sectors during the pandemic have been tourism along with automotive and power.

The COVID-19 pandemic has globally jeopardized food security, with heightened threats for the most vulnerable including smallholder farmers as well as rural, indigenous populations. A serial cross-sectional study was conducted to document effect of COVID-19 pandemic on food environment, agricultural practices, diets and food security, along with potential determinants of food systems resilience, among vulnerable smallholder farmer households in indigenous communities of Santhal, Munda, and Sauria Paharia of Jharkhand state, India. Telephonic household surveys were conducted in two phases i.e., lockdown and unlock phase to assess the impact of the pandemic on their food systems and agricultural practices. Market surveys were conducted during the unlock phase, to understand the impact on local informal markets. Secondary data on state and district level food production and Government food security programs were also reviewed. For data analysis purpose, a conceptual framework was developed which delineated possible pathways of impact of COVID-19 pandemic on food environment, food security and food consumption patterns along with factors that may offer resilience. Our findings revealed adverse effects on food production and access among all three communities, due to restrictions in movement of farm labor and supplies, along with disruptions in food supply chains and other food-related logistics and services associated with the pandemic and mitigation measures. The pandemic significantly impacted the livelihoods and incomes among all three indigenous communities during both lockdown and unlock phases, which were attributed to a reduction in sale of agricultural produce, distress selling at lower prices and reduced opportunity for daily wage laboring. A significant proportion of respondents also experienced changes in dietary intake patterns. Key determinants of resilience were identified; these included accessibility to agricultural inputs like indigenous seeds, labor available at household level due to back migration and access to diverse food environments, specifically the wild food environment. There is a need for programs and interventions to conserve and revitalize the bio-cultural resources available within these vulnerable indigenous communities and build resilient food systems that depend on shorter food supply chains and utilize indigenous knowledge systems and associated resources, thereby supporting healthy, equitable and sustainable food systems for all.

India marks one COVID-19 death every 5 minutes

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Content

People across India scrambled for life-saving oxygen supplies on Friday and patients lay dying outside hospitals as the capital recorded the equivalent of one death from COVID-19 every five minutes.

For the second day running, the country’s overnight infection total was higher than ever recorded anywhere in the world since the pandemic began last year, at 332,730.

India’s second wave has hit with such ferocity that hospitals are running out of oxygen, beds, and anti-viral drugs. Many patients have been turned away because there was no space for them, doctors in Delhi said.

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Mass cremations have been taking place as the crematoriums have run out of space. Ambulance sirens sounded throughout the day in the deserted streets of the capital, one of India’s worst-hit cities, where a lockdown is in place to try and stem the transmission of the virus. source

Dataset

The dataset consists of the tweets made with the #IndiaWantsOxygen hashtag covering the tweets from the past week. The dataset totally consists of 25,440 tweets and will be updated on a daily basis.

The description of the features is given below | No |Columns | Descriptions | | -- | -- | -- | | 1 | user_name | The name of the user, as they’ve defined it. | | 2 | user_location | The user-defined location for this account’s profile. | | 3 | user_description | The user-defined UTF-8 string describing their account. | | 4 | user_created | Time and date, when the account was created. | | 5 | user_followers | The number of followers an account currently has. | | 6 | user_friends | The number of friends an account currently has. | | 7 | user_favourites | The number of favorites an account currently has | | 8 | user_verified | When true, indicates that the user has a verified account | | 9 | date | UTC time and date when the Tweet was created | | 10 | text | The actual UTF-8 text of the Tweet | | 11 | hashtags | All the other hashtags posted in the tweet along with #IndiaWantsOxygen | | 12 | source | Utility used to post the Tweet, Tweets from the Twitter website have a source value - web | | 13 | is_retweet | Indicates whether this Tweet has been Retweeted by the authenticating user. |

Acknowledgements

https://globalnews.ca/news/7785122/india-covid-19-hospitals-record/ Image courtesy: BBC and Reuters

Inspiration

The past few days have been really depressing after seeing these incidents. These tweets are the voice of the indians requesting help and people all over the globe asking their own countries to support India by providing oxygen tanks.

And I strongly believe that this is not just some data, but the pure emotions of people and their call for help. And I hope we as data scientists could contribute on this front by providing valuable information and insights.

BackgroundThe world witnessed a highly contagious and deadly disease, COVID-19, toward the end of 2019. India is one of the worst affected countries. We aimed to assess anxiety and depression levels among adult tobacco users and people who recently quit tobacco during COVID-19 lockdown in India.Materials and methodsThe study was conducted across two Indian cities, Delhi and Chennai (July-August, 2020) among adult tobacco users (n = 801). Telephonic interviews were conducted using validated mental health tools (Patient Health Questionnaire-PHQ-9 and Generalized Anxiety Disorder-GAD-7) to assess the anxiety and depression levels of the participants. Descriptive analysis and multiple logistic regression were used to study the prevalence and correlates of depression and anxiety.ResultsWe found that 20.6% of tobacco users had depression symptoms (3.9% moderate to severe); 20.7% had anxiety symptoms (3.8% moderate to severe). Risk factors associated with depression and anxiety included food, housing, and financial insecurity.ConclusionDuring COVID-19 lockdown, mental health of tobacco users (primarily women) was associated with food, housing and financial insecurity. The Indian Government rightly initiated several health, social and economic measures to shield the most vulnerable from COVID-19, including a ban on the sale of tobacco products. It is also necessary to prioritize universal health coverage, expanded social security net, tobacco cessation and mental health services to such vulnerable populations during pandemic situations.

The rise and spread of Covid-19 pandemic affected all parts of the human society by creating massive socio-economic panic across all the sectors including agriculture, tourism, commerce, shipping, manufacturing and tertiary sectors across the world. The agricultural and food sector were considered as the most crucial part of the developing economics across the globe, which was completely exposed during the Covid-19 pandemic. It has an undesirable and prominent influence on agriculture and allied sectors in India. The pandemic lockdown has resulted in the agrarian crisis across the nation by influencing and disrupting the food demand, food supply and value chain of various agricultural goods and commodities. In the country like India where majority of the population, approximately 140 million, depends directly or indirectly on agriculture and food sectors as the primary source of their income the impact due to the Covid-19 pandemic created an imbalance and affected the economy of the nation. Containing an analysis and detailed review based on articles, scientific reports, publications, organizational statements, and press releases, this review article addresses an inclusive assessment and highlights the effects of Covid-19 pandemic on agriculture and food systems. An effort has been made to understand its impact on food supply, food demand, food prices, food security and national economy. The need of the hour is to promote effective solutions in order to control critical factors such as food production, food supply, food demand, price hikes, food security and supply chain resilience. Since the urbanization and population will have tremendous growth in the coming decades, epidemics may be more frequent and we need to ensure contingency plans and mitigation strategies, especially for agricultural and food systems.

India has witnessed a dramatic shift in the movement of workers within the nation. There are more than 100 million migrant workers in different Industries according to official employment estimation. Migrant workers employed in informal sectors mostly engage in temporary, unskilled work, characterized by low wages, job insecurity, and economic vulnerability, which are peculiar characteristics of informal work environments (Atnafu, 2014)They are exposed to experience multiple psychological disorders like anxiety and depression due to cultural differences in migrated states, loss of social network, identity crises and lack of access to public health care facilities in addition to other problems like malnutrition and acute poverty. COVID 19 has worsened the problem of migrant workers with increased rates of anxiety, stress, and distress psychiatric conditions. With the lockdown and travel restrictions, many groups of stranded laborers were struggling to reach their hometowns. Migrant workers were identified as the most vulnerable group to the risk of catching the COVID- 19 infections. This has added to their mental health issues. With the focus on migrant workers as the subject of investigation, this data set is aimed at enhancing the knowledge of the users of the data on the impact of the pandemic on the mental health issues of the internal migrant workers. Our survey probes into the psychiatric issues that migrant works face and trauma that they underwent during the pandemic using Corona Virus Anxiety Scale (CAS) which was originally developed by Lee in the year 2019. The scale uses four dimensions namely Cognitive, Emotional, Behavioural, and Psychological. The data was constructed with the help interview schedule which was developed by employing Corona Virus Anxiety Scale (CAS). We have developed this data out of 1350 valid responses elicited through the telephonic interview. The data were collected from June to August 2020 which is considered to be the peak pandemic period. We consider the period was crucial since it was characterized by the uncertain atmosphere with job markets badly hit and workers having the fear of reintroducing travel restriction being imposed by the government. Thus the data provides insights and guidance for the interested researchers to carry our related studies on the mental health of vulnerable groups like migrant workers. The data would also enable researchers and academicians to investigate the impact of such pandemic and uncertain events on the mental health of vulnerable groups. This data would enable the researcher to understand the possible adverse impacts on the anxiety levels of the dispersed population of migrant workers.

Small traders and laborers were most impacted by the coronavirus (COVID-19) lockdown in India with over ** million people losing their employment in April 2020. Over *** million Indians lost their jobs, including entrepreneurs and salaried workers. In contrast, agriculture saw an addition of **** percent from farmers as compared to the fiscal year 2020.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Excluded populations living in Northeast states and remote islands and Jammu and Kashmir. The excluded areas represent less than 10 percent of the total population.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for India is 3000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

BackgroundSince the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection.MethodsBiochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman’s rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score.ResultsThe parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count.ConclusionThe current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

Despite COVID-19 vaccines being available to pregnant women in India since summer 2021, little is known about vaccine uptake among this high-need population. We conducted mixed methods research with pregnant and recently delivered rural women in northern India, consisting of 300 phone surveys and 15 in-depth interviews, in November 2021. Only about a third of respondents were vaccinated, however, about half of unvaccinated respondents reported that they would get vaccinated now if they could. Fears of harm to the unborn baby or young infant were common (22% of unvaccinated women). However, among unvaccinated women who wanted to get vaccinated, the most common barrier reported was that their healthcare provider refused to provide them with the vaccine. Gender barriers and social norms also played a role, with family members restricting women’s access. Trust in the health system was high, however, women were most often getting information about COVID-19 vaccines from sources that they did not trust, and they knew they were getting potentially poor-quality information. Qualitative data shed light on the barriers women faced from their family and healthcare providers but described how as more people got the vaccine, that norms were changing. These findings highlight how pregnant women in India have lower vaccination rates than the general population, and while vaccine hesitancy does play a role, structural barriers from the healthcare system also limit access to vaccines. Interventions must be developed that target household decision-makers and health providers at the community level, and that take advantage of the trust that rural women already have in their healthcare providers and the government. It is essential to think beyond vaccine hesitancy and think at the system level when addressing this missed opportunity to vaccinate high-risk pregnant women in this setting.

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

On 6/16/2023 CDPH replaced the booster measures with a new “Up to Date” measure based on CDC’s new recommendations, replacing the primary series, boosted, and bivalent booster metrics The definition of “primary series complete” has not changed and is based on previous recommendations that CDC has since simplified. A person cannot complete their primary series with a single dose of an updated vaccine. Whereas the booster measures were calculated using the eligible population as the denominator, the new up to date measure uses the total estimated population. Please note that the rates for some groups may change since the up to date measure is calculated differently than the previous booster and bivalent measures.

This data is from the same source as the Vaccine Progress Dashboard at https://covid19.ca.gov/vaccination-progress-data/ which summarizes vaccination data at the county level by county of residence. Where county of residence was not reported in a vaccination record, the county of provider that vaccinated the resident is included. This applies to less than 1% of vaccination records. The sum of county-level vaccinations does not equal statewide total vaccinations due to out-of-state residents vaccinated in California.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

Totals for the Vaccine Progress Dashboard and this dataset may not match, as the Dashboard totals doses by Report Date and this dataset totals doses by Administration Date. Dose numbers may also change for a particular Administration Date as data is updated.

Previous updates:

• On March 3, 2023, with the release of HPI 3.0 in 2022, the previous equity scores have been updated to reflect more recent community survey information. This change represents an improvement to the way CDPH monitors health equity by using the latest and most accurate community data available. The HPI uses a collection of data sources and indicators to calculate a measure of community conditions ranging from the most to the least healthy based on economic, housing, and environmental measures.

• Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 16+ and age 5+ denominators have been uploaded as archived tables.

• Starting on May 29, 2021 the methodology for calculating on-hand inventory in the shipped/delivered/on-hand dataset has changed. Please see the accompanying data dictionary for details. In addition, this dataset is now down to the ZIP code level.

Just like any country, India has struggled against the coronavirus (COVID-19) pandemic. Various factors like financial inequality, inadequate healthcare, and a huge population have made the matter even worse. In April 2020, after conducting the maximum number of tests in the country, the state of Maharashtra was able to detect over 1,900 cases. For the same period, the state of Sikkim carried out the minimum number of tests with zero cases detected.

What do people think about COVID-19 in India?

According to an online survey in February 2020, when the respondents were asked about their opinion on the issue of coronavirus, over 70 percent of the participants showed belief in staying alert and taking precautions. However, 16 percent of participants believed that the virus will not have a major influence on the country. To learn how people from different age groups are dealing with the fear of catching the virus, another survey was conducted in March 2020. It was discovered that the millennials were the most scared of contracting the virus. On the other hand, baby boomers showed minimum fear.

Impact of COVID-19 on India’s economy

The coronavirus has influenced the Indian economy in many ways. China has a major share in India’s import and export market. Therefore, any strain on the Chinese economy directly shows its effect on India too. This is true for all major economies of the world. Apart from this, the internal trade in the country has also taken a huge hit due to a series of lockdowns. In April 2020, the overall cost of a complete lockdown in India was estimated at around 26 billion U.S. dollars. India’s gross domestic product (GDP) growth in the second quarter of 2020 was estimated to show a negative growth of nine percent. This was a huge decline as compared to the last quarter in which positive growth was observed.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.