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.

Covid19Kerala.info-Data is a consolidated multi-source open dataset of metadata from the COVID-19 outbreak in the Indian state of Kerala. It is created and maintained by volunteers of ‘Collective for Open Data Distribution-Keralam’ (CODD-K), a nonprofit consortium of individuals formed for the distribution and longevity of open-datasets. Covid19Kerala.info-Data covers a set of correlated temporal and spatial metadata of SARS-CoV-2 infections and prevention measures in Kerala. Static releases of this dataset snapshots are manually produced from a live database maintained as a set of publicly accessible Google sheets. This dataset is made available under the Open Data Commons Attribution License v1.0 (ODC-BY 1.0).

Schema and data package Datapackage with schema definition is accessible at https://codd-k.github.io/covid19kerala.info-data/datapackage.json. Provided datapackage and schema are based on Frictionless data Data Package specification.

Temporal and Spatial Coverage

This dataset covers COVID-19 outbreak and related data from the state of Kerala, India, from January 31, 2020 till the date of the publication of this snapshot. The dataset shall be maintained throughout the entirety of the COVID-19 outbreak.

The spatial coverage of the data lies within the geographical boundaries of the Kerala state which includes its 14 administrative subdivisions. The state is further divided into Local Self Governing (LSG) Bodies. Reference to this spatial information is included on appropriate data facets. Available spatial information on regions outside Kerala was mentioned, but it is limited as a reference to the possible origins of the infection clusters or movement of the individuals.

Longevity and Provenance

The dataset snapshot releases are published and maintained in a designated GitHub repository maintained by CODD-K team. Periodic snapshots from the live database will be released at regular intervals. The GitHub commit logs for the repository will be maintained as a record of provenance, and archived repository will be maintained at the end of the project lifecycle for the longevity of the dataset.

Data Stewardship

CODD-K expects all administrators, managers, and users of its datasets to manage, access, and utilize them in a manner that is consistent with the consortium’s need for security and confidentiality and relevant legal frameworks within all geographies, especially Kerala and India. As a responsible steward to maintain and make this dataset accessible— CODD-K absolves from all liabilities of the damages, if any caused by inaccuracies in the dataset.

License

This dataset is made available by the CODD-K consortium under ODC-BY 1.0 license. The Open Data Commons Attribution License (ODC-By) v1.0 ensures that users of this dataset are free to copy, distribute and use the dataset to produce works and even to modify, transform and build upon the database, as long as they attribute the public use of the database or works produced from the same, as mentioned in the citation below.

Disclaimer

Covid19Kerala.info-Data is provided under the ODC-BY 1.0 license as-is. Though every attempt is taken to ensure that the data is error-free and up to date, the CODD-K consortium do not bear any responsibilities for inaccuracies in the dataset or any losses—monetary or otherwise—that users of this dataset may incur.

Context

This is a Covid 19 data set for India. The data set is updated frequently and is analysed using tableau. Click on the link to visit the tableau story. Click each of the caption in the story to unveil its content.

https://public.tableau.com/profile/ambili.nair#!/vizhome/COVID19Indiastory/Indiastory?publish=yes

The first Covid 19 case in India was reported on 30th January 2020 in South Indian state of Kerala on a medical student who was pursuing the studies at Wuhan University, China. Two more students were found to be infected in Kerala in the consecutive days. The Kerala government was successful in containing the disease with its proactive measures back then. The second outbreak of Covid 19 in India started in the first week of March from various parts of India in various people who visited the foreign countries and in some of the tourists from different countries.

The tableau story consists of the following data analysis : 1. State-wise number of infected and number of death count in India map. Hover the mouse on each state in the India map to know the count. 2. Click on the next caption to know the state-wise number of confirmed, active, recovered and deceased cases in the form of bar chart. 3. The next caption takes you to the bar chart which shows the number of cases getting confirmed in India each day starting from January 30, 2020. 4. Next caption takes us to an analysis of the Mortality rate and the Recovery rate (in percentage) of each of the Indian state. We get an idea how hard each of the state is hit by the pandemic. 5. Next caption gives a detailed analysis of the state Kerala which has the mortality rate of 0.806% and the recovery rate of 74.4% as of now. Hover the mouse to know the count in each district. Don't forget to have a look at the line graph of 'number of active cases' in Kerala. It looks almost flattened ! As everyday we hear the increasing number of cases and deaths across the country, this graph may make you feel better...! 6. Finally the caption takes you to the statistics from the topmost district of Kerala - Kasaragod. The total number of cases reported is 179 at Kasaragod. The active number of cases is just 12 as of now... !!! Have a look at the statistics from Kasaragod and the story of 'Kasaragod model' as some of the national media in India call it !!!

Content

This data set consists of the following data: 1. state-wise statistics - Confirmed, Active, Recovered, Deceased cases 2. day-wise count of infected and deceased from various states 3. Statistics from Kerala - day-wise count of confirmed, Active, Recovered, Deceased cases 4. Statistics from Kasaragod district, Kerala - day-wise count of confirmed, Active, Recovered, Deceased cases 5. Count of confirmed cases from various districts of India

Acknowledgements

Ministry of Health and Family Welfare - India covid19india.org Wikipedia page - Covid 19 Pandemic India Govt. of Kerala dashboard - official Kerala Covid 19 statistics

Inspiration

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

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.

Analysis of ‘COVID-19 India Time Series’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ravichaubey1506/covid19-india on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people who fall sick with COVID-19 will experience mild to moderate symptoms and recover without special treatment.

Content

COVID-19 cases at daily level is present in covid_time_series.csv COVID-19 cases for different states till 1 may 2020 is present in covid_india_states.csv

Acknowledgements

Thanks to Indian Ministry of Health & Family Welfare for making the data available to general public.

Thanks to covid19india.org for making the individual level details and testing details available to general public.

Thanks to Wikipedia for population information.

Inspiration

Forecast for next 15 days and some EDA on Spread of Corona Virus

--- Original source retains full ownership of the source dataset ---

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India’s 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

This dataset includes observations covering six states (Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh) and three survey rounds. The survey did not have a single, unified frame from which to sample phone numbers. The final sample was assembled from several different sample frames, and the choice of frame sample frames varied across states and survey rounds.

These frames comprise four prior IDinsight projects and from an impact evaluation of the National Rural Livelihoods project conducted by the Ministry of Rural Development. Each of these surveys sought to represent distinct populations, and employed idiosyncratic sample designs and weighting schemes.

A detailed note covering key features of each sample frame is available for download.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaires covered the following subjects:

1. Agriculture: COVID-19-related changes in price realisation, acreage decisions, input expenditure, access to credit, access to fertilisers, etc.

2. Income and consumption: Changes in wage rates, employment duration, consumption expenditure, prices of essential commodities, status of food security etc.

3. Migration: Rates of in-migration, migrant income and employment status, return migration plans etc.

4. Access to relief: Access to in-kind, cash and workfare relief, quantities of relief received, and constraints on the access to relief.

5. Health: Access to health facilities and rates of foregone healthcare, knowledge of COVID-19 related symptoms and protective behaviours.

While a number of indicators were consistent across all three rounds, questions were added and removed as and when necessary to account for seasonal changes (i.e: in the agricultural cycle).

Response rate

Round 1: ~55% Round 2: ~46% Round 3: ~55%

The COVID-19 outbreak was first declared an international public health, and it was later deemed a pandemic. In most countries, the COVID-19 incidence curve rises sharply over a short period of time, suggesting a transition from a disease-free (or low-burden disease) equilibrium state to a sustained infected (or high-burden disease) state. Such a transition is often known to exhibit characteristics of “critical slowing down.” Critical slowing down can be, in general, successfully detected using many statistical measures, such as variance, lag-1 autocorrelation, density ratio, and skewness. Here, we report an empirical test of this phenomena on the COVID-19 datasets of nine countries, including India, China, and the United States. For most of the datasets, increases in variance and autocorrelation predict the onset of a critical transition. Our analysis suggests two key features in predicting the COVID-19 incidence curve for a specific country: (a) the timing of strict social distancing and/or lockdown interventions implemented and (b) the fraction of a nation's population being affected by COVID-19 at that time. Furthermore, using satellite data of nitrogen dioxide as an indicator of lockdown efficacy, we found that countries where lockdown was implemented early and firmly have been successful in reducing COVID-19 spread. These results are essential for designing effective strategies to control the spread/resurgence of infectious pandemics.

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India's 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, researchers from the World Bank, in collaboration with IDinsight, the Development Data Lab, and John Hopkins University sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The samples for these surveys were drawn from surveys and impact evaluations previously conducted by the World Bank, the Ministry of Rural Development, India and IDInsight. A detailed note on the sampling frames is available for download.

Sampling deviation

Details will be made available after all rounds of data collection and analysis is complete.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Response rate

Approximately 55%

India reported over 44 million confirmed cases of the coronavirus (COVID-19) as of October 20, 2023. The number of people infected with the virus was declining across the south Asian country.

What is the coronavirus?

COVID-19 is part of a large family of coronaviruses (CoV) that are transmitted from animals to people. The name COVID-19 is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged. Symptoms of COVID-19 resemble that of the common cold, with fever, coughing, and shortness of breath. However, serious infections can lead to pneumonia, multi-organ failure, severe acute respiratory syndrome, and even death, if appropriate medical help is not provided.

COVID-19 in India

India reported its first case of this coronavirus in late January 2020 in the southern state of Kerala. That led to a nation-wide lockdown between March and June that year to curb numbers from rising. After marginal success, the economy opened up leading to some recovery for the rest of 2020. In March 2021, however, the second wave hit the country causing record-breaking numbers of infections and deaths, crushing the healthcare system. The central government has been criticized for not taking action this time around, with "#ResignModi" trending on social media platforms in late April. The government's response was to block this line of content on the basis of fighting misinformation and reducing panic across the country.

Over the past several months, the world has been facing an unprecedented public health crisis in the form of Covid-19. States and union territories in India have been equal partners of the Central Government in managing the Covid-19 outbreak in the country. This document is a compendium of practices from states and union territories that details information about various initiatives implemented by states, districts, and cities in India for containing and managing the Covid-19 outbreak. The practices in the compendium have been dis-aggregated under six sections: (i) public health and clinical response (ii) governance mechanisms (iii) digital health (iv) integrated model (v) welfare of migrants and other vulnerable groups (vi) other practices. A summary of the relevant Government of India guidelines has been included for the aforementioned categories, wherever applicable.

Combating the spread of covid 19 through community participation India has a vast population with a weak public health system which is vulnerable to the COVID 19 pandemic Economically and physically India is in a state of considerable risk of the COVID 19 pandemic Community participation through various measures is the only way to limit the spread of the virus The present study investigates the possibility of social intervention and involvement in controlling the pandemics and its cascadi

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.

Globally, SARS-CoV-2 has moved from one tide to another with ebbs in between. Genomic surveillance has greatly aided the detection and tracking of the virus and the identification of the variants of concern (VOC). The knowledge and understanding from genomic surveillance is important for a populous country like India for public health and healthcare officials for advance planning. An integrative analysis of the publicly available datasets in GISAID from India reveals the differential distribution of clades, lineages, gender, and age over a year (Apr 2020–Mar 2021). The significant insights include the early evidence towards B.1.617 and B.1.1.7 lineages in the specific states of India. Pan-India longitudinal data highlighted that B.1.36* was the predominant clade in India until January–February 2021 after which it has gradually been replaced by the B.1.617.1 lineage, from December 2020 onward. Regional analysis of the spread of SARS-CoV-2 indicated that B.1.617.3 was first seen in India in the month of October in the state of Maharashtra, while the now most prevalent strain B.1.617.2 was first seen in Bihar and subsequently spread to the states of Maharashtra, Gujarat, and West Bengal. To enable a real time understanding of the transmission and evolution of the SARS-CoV-2 genomes, we built a transmission map available on https://covid19-indiana.soic.iupui.edu/India/EmergingLineages/April2020/to/March2021. Based on our analysis, the rate estimate for divergence in our dataset was 9.48 e-4 substitutions per site/year for SARS-CoV-2. This would enable pandemic preparedness with the addition of future sequencing data from India available in the public repositories for tracking and monitoring the VOCs and variants of interest (VOI). This would help aid decision making from the public health perspective.

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.

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 12+ and age 5+ denominators have been uploaded as archived tables.

Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021.

This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data.

This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score.

This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4.

The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting.

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.

For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

Context

Coronaviruses are a large family of viruses which may cause illness in animals or humans. In humans, several coronaviruses are known to cause respiratory infections ranging from the common cold to more severe diseases such as MERS and SARS. The most recently discovered coronavirus causes COVID-19 - World Health Organization (WHO).

The number of new cases is increasing day by day around the world. This dataset has information for states of India at a daily level.

Content

COVID-19 cases at a daily level is present in COVID_19_INDIA.csv file

Acknowledgements

Thanks to the Indian Ministry of Health & Family Welfare for making the data available to the general public.

Thanks to covid19india.org for making the individual level details and testing details available to the general public.

Objectives: From the beginning of the COVID-19 pandemic, clinical practice and research, globally, have centered on the prevention of transmission and treatment of the disease. The pandemic has had a huge impact on the economy and stressed the healthcare systems worldwide. The present study estimates Disability-Adjusted Life Years (DALYs), Years of Potential Productive Life Lost (YPPLL), and Cost of Productivity Lost (CPL) due to premature mortality and absenteeism, secondary to COVID-19 in Kerala state, India.

Setting: Details on sociodemography, incidence, death, quarantine, recovery time, etc were derived from public sources and CODD-K for Kerala. The working proportion for 5-year age-gender cohorts and corresponding life expectancy were obtained from the Census of India 2011.

Primary and secondary outcome measures: The impact of disease was computed through model based analysis on various age-gender cohorts. Sensitivity Analysis has been conducted by adjusting six variables across 21 scenarios. We present two estimates, one till November 15, 2020, and later updated till June 10, 2021.

Results: Severity of infection and mortality were higher among the older cohorts, with males being more susceptible than females in most sub-groups. The DALYs for males and females were 15954.5 and 8638.4 till November 15, 2020, and 83853.0 and 56628.3 till June 10, 2021. The corresponding YPPLL were 1323.57 and 612.31 till November 15, 2020, and 6993.04 and 3811.57 till June 10, 2021 and CPL (premature mortality) were 263780579.94 and 41836001.82 till November 15, 2020, and 1419557903.76 and 278275495.29 till June 10, 2021.

Conclusions: Most of the COVID-19 disease burden was contributed by YLL. Losses due to YPPLL were reduced as the impact of COVID-19 infection was lesser among productive cohorts. CPL values for 40-49 year-olds were the highest. These estimates provide the data necessary for policymakers to work on, to reduce the economic burden of COVID-19 in Kerala.

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. 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.

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. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

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.

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.

Analysis of ‘Unemployment in India’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gokulrajkmv/unemployment-in-india on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

The story behind this datasets is how lock-down affects employment opportunities and how the unemployment rate increases during the Covid-19.

Content

This dataset contains the unemployment rate of all the states in India

Region = states in India Date = date which the unemployment rate observed Frequency = measuring frequency (Monthly)
Estimated Unemployment Rate (%) = percentage of people unemployed in each States of India Estimated Employed = percentage of people employed Estimated Labour Participation Rate (%) = labour force participation rate by dividing the number of people actively participating in the labour force by the total number of people eligible to participate in the labor force force

Acknowledgements

I wouldn't be here without the help of my friends. I owe you thanks !!

Inspiration

questions? 1. How Covid-19 affects the employment 2. how far the unemployment rate will go

source of datasets https://unemploymentinindia.cmie.com/

--- Original source retains full ownership of the source dataset ---