This dataset talks about the rates of positive covid test done in all over India and compiled it according to the district. The dataset includes the positivity rates came on the Rapid Antigen Test (RAT) and Real Time Polymerase Chain Reaction (RT-PCR).

State - Name of the Indian States District - Name of the district % Contribution of Testing by RAT - % of test done through Rapid Antigen Test % Contribution of Testing by RTPCR - % of test done through RT-PCR Positivity - The percentage of test which shows positive result

Hello all, this notebook consists of the patients suffering from corona virus from various states of India. This pandemic started from Kerala and it spread all over. If you will try to analyze the dataset, you will come to know that Maharashtra state have large number of positive results, also the recovery rate is high over there. This notebook clearly categorizes the positive result, death rates and the recovery rates of different states. Data visualization is done here which makes the case study more attractive and informative.

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.

Context

Covid-19 Data collected from various sources on the internet. This dataset has daily level information on the number of affected cases, deaths, and recovery from the 2019 novel coronavirus. Please note that this is time-series data and so the number of cases on any given day is the cumulative number.

Content

The dataset includes 28 files scrapped from various data sources mainly the John Hopkins GitHub repository, the ministry of health affairs India, worldometer, and Our World in Data website. The details of the files are as follows

• countries-aggregated.csv A simple and cleaned data with 5 columns with self-explanatory names. -covid-19-daily-tests-vs-daily-new-confirmed-cases-per-million.csv A time-series data of daily test conducted v/s daily new confirmed case per million. Entity column represents Country name while code represents ISO code of the country. -covid-contact-tracing.csv Data depicting government policies adopted in case of contact tracing. 0 -> No tracing, 1-> limited tracing, 2-> Comprehensive tracing. -covid-stringency-index.csv The nine metrics used to calculate the Stringency Index are school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stay-at-home requirements; public information campaigns; restrictions on internal movements; and international travel controls. The index on any given day is calculated as the mean score of the nine metrics, each taking a value between 0 and 100. A higher score indicates a stricter response (i.e. 100 = strictest response). -covid-vaccination-doses-per-capita.csv A total number of vaccination doses administered per 100 people in the total population. This is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g. people receive multiple doses). -covid-vaccine-willingness-and-people-vaccinated-by-country.csv Survey who have not received a COVID vaccine and who are willing vs. unwilling vs. uncertain if they would get a vaccine this week if it was available to them. -covid_india.csv India specific data containing the total number of active cases, recovered and deaths statewide. -cumulative-deaths-and-cases-covid-19.csv A cumulative data containing death and daily confirmed cases in the world. -current-covid-patients-hospital.csv Time series data containing a count of covid patients hospitalized in a country -daily-tests-per-thousand-people-smoothed-7-day.csv Daily test conducted per 1000 people in a running week average. -face-covering-policies-covid.csv Countries are grouped into five categories: 1->No policy 2->Recommended 3->Required in some specified shared/public spaces outside the home with other people present, or some situations when social distancing not possible 4->Required in all shared/public spaces outside the home with other people present or all situations when social distancing not possible 5->Required outside the home at all times regardless of location or presence of other people -full-list-cumulative-total-tests-per-thousand-map.csv Full list of total tests conducted per 1000 people. -income-support-covid.csv Income support captures if the government is covering the salaries or providing direct cash payments, universal basic income, or similar, of people who lose their jobs or cannot work. 0->No income support, 1->covers less than 50% of lost salary, 2-> covers more than 50% of the lost salary. -internal-movement-covid.csv Showing government policies in restricting internal movements. Ranges from 0 to 2 where 2 represents the strictest. -international-travel-covid.csv Showing government policies in restricting international movements. Ranges from 0 to 2 where 2 represents the strictest. -people-fully-vaccinated-covid.csv Contains the count of fully vaccinated people in different countries. -people-vaccinated-covid.csv Contains the total count of vaccinated people in different countries. -positive-rate-daily-smoothed.csv Contains the positivity rate of various countries in a week running average. -public-gathering-rules-covid.csv Restrictions are given based on the size of public gatherings as follows: 0->No restrictions 1 ->Restrictions on very large gatherings (the limit is above 1000 people) 2 -> gatherings between 100-1000 people 3 -> gatherings between 10-100 people 4 -> gatherings of less than 10 people -school-closures-covid.csv School closure during Covid. -share-people-fully-vaccinated-covid.csv Share of people that are fully vaccinated. -stay-at-home-covid.csv Countries are grouped into four categories: 0->No measures 1->Recommended not to leave the house 2->Required to not leave the house with exceptions for daily exercise, grocery shopping, and ‘essent...

This Dataset contains day-wise cumulative total positive cases, active cases, recoveries and death statistics due to COVID-19 in India up to 10 June 2024

Context

Covid-19 is spreading in India at a very high rate. Recently, India witnessed the most number of positive cases in a day. We must do what we can to understand and defeat this deadly virus. Here is the data set I gathered from official 'Indian Ministry of Health' website updated on 15 June, 2020. I hope you find it useful. I will keep updating the data set on a regular basis.

Acknowledgements

https://www.mohfw.gov.in/

PC: Photo by Fusion Medical Animation on Unsplash

Column Description

State - Name of the State/ Union Territory Active Cases - Number of active cases in the State Cured/Migrated - Number of Cases Cured/ Migrated from the State Deaths - Number of deaths in the State due to Covid19 Total Confirmed Cases - Total number of confirmed cases in the State (Active + Cured + Deaths)

-COVID-19 raw and derived epidemiological data -Updated daily, timeseries data since beginning of pandemic -National and statewise data

Following epidemiological indicators in the dataset: Effective reproduction number (Rt), Positivity rate (daily and cumulative), Crude CFR, Lag adjusted CFR, Outcome adjusted CFR, Mobility index (Google mobility).

Latest updated data and detailed information for use available at https://github.com/CovidToday/indicator-dataset

Live dashboard and visualisation at www.covidtoday.in (open sourced project) at https://github.com/CovidToday

Contact us at covidtodayindia@gmail.com

Context

Coronavirus is a family of viruses that can cause illness, which can vary from common cold and cough to sometimes more severe disease. SARS-CoV-2 (n-coronavirus) is the new virus of the coronavirus family, which first discovered in 2019, which has not been identified in humans before. It is a contiguous virus which started from Wuhan in December 2019. Which later declared as Pandemic by WHO due to high rate spreads throughout the world. Currently (on date 27 March 2020), this leads to a total of 24K+ Deaths across the globe, including 16K+ deaths alone in Europe.Pandemic is spreading all over the world; it becomes more important to understand about this spread.

The number of new cases are increasing day by day around the world. This dataset has information from the states and union territories of India at daily level.

State Wise data fetched from Ministry of Health & Family Welfare ICMR Testing Data comes from Indian Council of Medical Research

Content

COVID-19 cases at daily level is present in covid_19_india.csv file

COVID-19 State and Union Territory data with latitude and longitude is present in state_wise_data.csv

COVID-19 cases at daily level is present in data_wise_data.csv and perday_new_cases.csv file

Number of COVID-19 tests and positive cases at daily level in ICMR_Testing_Data.csv file

Acknowledgements

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

This work is highly inspired from few other kaggle kernels , github sources and other data science resources. Any traces of replications, which may appear , is purely co-incidental. Due respect & credit to all my fellow kagglers.

Inspiration

Together we can do this. Help the world to make a better place and with this fight against COVID-19.

Contact positivity among COVID-19 cases US Mission India, March 2020 - July 2021 (n = 627).

India experienced a second wave of COVID-19 infection with an unprecedented upsurge in the number of cases. We have analyzed the effect of different restrictive measures implemented in six Indian states. Further, based on available national and international data on disease transmission and clinical presentation, we have proposed a decision-making matrix for planning adequate resources to combat the future waves of COVID-19. We conclude that pragmatic and well calibrated localized restrictions, tailored as per specific needs may achieve a decline in disease transmission comparable to drastic steps like national lockdowns. Additionally, we have underscored the critical need for countries to generate local epidemiological, clinical and laboratory data alongwith community perception and uptake of various non-pharmaceutical interventions, for effective planning and policy making.

The southern Indian state of Kerala had almost 8,417 people under observation due to the coronavirus (COVID-19) as of April 10, 2022. Of these, over eight thousand were confined to home or institutions, while over 150 patients were quarantined in designated isolation facilities. India recorded over 62 thousand active cases of the virus as September 1, 2022. The regions of Kerala , Karnataka and Maharashtra had the highest number of confirmed cases in the same time period.

Kerala’s links to Wuhan

On February 7, 2020, three Indians from Kerala were tested positive for COVID-19 after returning to India from Wuhan- the epicenter of the virus that has infected over 90 thousand people. Wuhan has been a popular destination among Keralites for its quality and affordable medical education. After conducting test swabs on all returnees, the Kerala government swung into immediate action by advising home quarantines for the people suspected to have been in contact with this coronavirus.

A state known for its healthcare performance

Kerala’s last major health scare was the Nipah virus in 1998, that returned in 2018, killing 17 people, along with almost six million cases of acute respiratory infections in 2016. Even then, Kerala is known to be India’s leading state for healthcare and medical literacy compared to the rest of the country. The southern state’s health department was reported to have been strictly following the protocols given by the World Health Organization to combat COVID-19. This preparedness seems to have borne good results so far with a high rate of recovery and containment of the virus.

Content

District-wise Covid-19 data of Maharashtra, a state in India as on April 29, 2022. The data include number of positive cases, active cases, recovered, deceased cases, recovery rate and fatality rate.

Attribute Information

Cumulative Cases by Districts

1. Districts - Name of districts in Maharashtra, India
2. Positive Cases - Number of positive cases
3. Active Cases - Number of active cases
4. Recovered - Number of recovered cases
5. Deceased - Number of deaths
6. Recovery Rate (%) - Ratio of number of recovered cases to positive cases
7. Fatality Rate (%) - Ratio of number of deaths to positive cases

Source

Link : https://www.covid19maharashtragov.in/mh-covid/dashboard

Other Updated Covid19 Datasets

Link : https://www.kaggle.com/anandhuh/datasets

If you find it useful, please support by upvoting 👍

Thank You

India's quarterly GDP was estimated to grow by 8.4 percent in the second quarter of financial year 2022 compared to the same quarter in the previous fiscal year. While continuing to be a positive change, it was a significant reduction from the performance during the first quarter of fiscal year 2022 when GDP growth peaked by 20 percent.

Cost of the pandemic

As a result of the various lockdowns enforced since the onset of the coronavirus pandemic in 2020, the Indian economy has been reeling from a multibillion dollar setback. The GDP contribution as well as the employment rate among most major sectors, especially services and trade, had taken a hit. The agriculture sector was an exception, having experienced positive changes on both these fronts.

A slowly recovering economy

With the outbreak of the second wave of the pandemic in March 2021, the government redirected financial support to boost India’s vaccination campaign. As of February 2022, over a billion vaccine doses had been administered across the country. Furthermore, inflation within the country was expected to decline 2021 onwards. However, the stagnation of employment continued to remain a matter of concern with protests erupting across different states in 2022.

As of January 1, 2025, the number of active coronavirus (COVID-19) infections in Italy was approximately 218,000. Among these, 42 infected individuals were being treated in intensive care units. Another 1,332 individuals infected with the coronavirus were hospitalized with symptoms, while approximately 217,000 thousand were in isolation at home. The total number of coronavirus cases in Italy reached over 26.9 million (including active cases, individuals who recovered, and individuals who died) as of the same date. The region mostly hit by the spread of the virus was Lombardy, which counted almost 4.4 million cases.For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

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

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

Hello all, this notebook consists of the patients suffering from corona virus from various states of India. This pandemic started from Kerala and it spread all over. If you will try to analyze the dataset, you will come to know that Maharashtra state have large number of positive results, also the recovery rate is high over there. This notebook clearly categorizes the positive result, death rates and the recovery rates of different states. Data visualization is done here which makes the case study more attractive and informative.

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

This Zenodo resource contains the data used to perform analysis in the article "Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India".

Data

The data is organized in the form of tables.

hypothesis-test-data

This table contains data used to perform the two tailed hypothesis test on gender mortality in different regions.

* Region
* Male_Deaths - Number of male COVID-19 deaths in region.  
* Female_Deaths - Number of female COVID-19 deaths in region.  
* Male_cases - Number of male COVID-19 positive in region.
* Female_cases - Number of female COVID-19 positive in region.

lasso-covid19India

This table contains data used for analysis on cases throughout India.

Columns from COVID-19 India data

* State_Code  
* State  
* District  
* Confirmed  
* Active  
* Recovered  
* Deceased

Columns taken from NFHS data

* Sex_ratio_of_the_total_population_females_per_1000_males  
* Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI_185_kgm214_  
* Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI_185_kgm2_  
* Women_who_are_overweight_or_obese_BMI_250_kgm214_  
* Men_who_are_overweight_or_obese_BMI_250_kgm2_  
* All_women_age_1549_years_who_are_anaemic_  
* Men_age_1549_years_who_are_anaemic_130_gdl_  
* Women_Blood_sugar_level_high_140_mgdl_  
* Men_Blood_sugar_level_high_140_mgdl_  
* Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_  
* Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_

lasso-KA+TN-bulletin

This table contains data used for analysis on the sub-cohort of Karnataka and Tamil Nadu.

Data from Media Bulletin

* District    
* Total_Positives  
* total_deaths
* male_deaths  
* female_deaths  
* Male_cases_in_data
* Female_cases_in_data

Calculated Data

* Estimated_Male_cases - Estimated male cases using total positives column and existing case data
* Estimated_Female_Cases - Estimated female cases using total positives column and existing case data  
* Male_Mortality - Estimated Male Cases / male_deaths
* Female_Mortality - Estimated Female Cases / female_deaths

Columns taken from NFHS data

* Sex_Ratio_females_every_1000_males
* State  Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI_185_kgm214_  
* Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI_185_kgm2_  
* Women_who_are_overweight_or_obese_BMI_250_kgm214_  
* Men_who_are_overweight_or_obese_BMI_250_kgm2_  
* All_women_age_1549_years_who_are_anaemic_  
* Men_age_1549_years_who_are_anaemic_130_gdl_  
* Women_Blood_sugar_level_high_140_mgdl_  
* Men_Blood_sugar_level_high_140_mgdl_  
* Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_  
* Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_

Code

The code is available at this Github Repository.

Petroleum products were the most affected commodities in terms of exports from India, with a decline of about ** percent in ************, compared to the same month in the previous year. Other cereals and oil meals witnessed a highly positive change rate.

Global economic impact The outbreak of COVID-19 caused a massive economic recession, with *** out of the ***** largest economies showing a massive GDP loss in the third quarter of 2020. A slump in demand and changing consumption patterns shook international trade worldwide. Since **********, lockdowns became a global necessity, and the Indian subcontinent was no exception, announcing its first nation-wide lockdown by the end of March. Aimed at getting hold of the infectious chains, the lockdown resulted in a massive decrease in mobility, but also meant that livelihoods were disproportionately impacted. This was especially true for those with daily or hourly wages across the country.

COVID-19 impact on different sectors Reduced mobility and the unavailability of resources, due to restricted borders caused significant challenges to traditional retailers. The automotive industry, in particular, emerged as one of the worst impacted industries. Simultaneously, petroleum consumption decreased. Other industries such as healthcare or fast-moving consumer goods, were less affected due to their indispensability and local shopper clientele. E-commerce experienced a long-lasting benefit from the pandemic, as most online purchasers consider e-retail as a post-pandemic option.

Aims and objectives: The purpose of this study is to detect the presence of SAR-CoV-2 viral RNA in conjunctival secretions of COVID-19 patients and to compare the RT-PCR positivity rate for SARS-CoV-2 in conjunctival and nasopharyngeal swabs. Materials and method: Eighty hospitalised COVID-19 patients whose nasopharyngeal swab tested positive for SARS-CoV-2 by RT-PCR were included in the study. Conjunctival swab was collected from the eyes of these patients and sent for detection of SARS-CoV-2 by RT-PCR method. Results: Among the eighty patients, 51 (63.7%) were males and 29 (36.3%) were females. The mean age of the patients was 55.93 ± 16.59. Six patients had ocular manifestations. Eleven (13.75%) patients tested positive on conjunctival swab for SARS-CoV-2 viral RNA, and only one of them had ocular manifestations out of the eleven. Conclusion: In our study, the presence of SARS-CoV-2 in conjunctival secretions of COVID-19 patients was detected and this was not dependent on the presenc...

Studies on host-pathogen interaction have identified human ACE2 as a host cell receptor responsible for mediating infection by coronavirus (COVID-19). Subsequent studies have shown striking difference of allele frequency among Europeans and Asians for a polymorphism rs2285666, present in ACE2. It has been revealed that the alternate allele (TT-plus strand or AA-minus strand) of rs2285666 elevate the expression level of this gene upto 50%, hence may play a significant role in SARS-CoV-2 susceptibility. Therefore, we have first looked the phylogenetic structure of rs2285666 derived haplotypes in worldwide populations and compared the spatial frequency of this particular allele with respect to the COVID-19 infection as well as case-fatality rate in India. For the first time, we ascertained a significant positive correlation for alternate allele (T or A) of rs2285666, with the lower infection as well as case-fatality rate among Indian populations. We trust that this information will be useful to understand the role of ACE2 in COVID-19 susceptibility.

Relaxation of mass quarantine (lockdown) measures has led to an increase in the daily positivity rate of SARS-CoV-2. However, the pattern and dynamics of its spread has been heterogenous across and within the states of India. This sero-epidemiological prevalence survey conducted in the city of Ujjain is one of the earliest such studies conducted to understand the determinants and dynamics of spread of COVID-19 in Ujjain and similar cities of Madhya Pradesh. A total of 4,883 individuals chosen through a stratified multi-stage random sampling method, were approached to capture socio-demographic information and any history of symptoms associated with COVID-19. A sample of venous blood was also collected and assessed for the presence of SARS-CoV-2 specific antibodies using the Electrochemiluminescence Immunoassay (ECLIA) technique. Overall unadjusted sero-prevalence for anti-SARS-CoV-2 antibody was found to be 14.2% (95% CI: 13.2% - 15.2%), and adjusted sero-prevalence was found to be 13.9% (95% CI: 10.4% - 18%). The adjusted sero-prevalence was highest in the age-group between 30 and 45 years (17.1%) and was lowest in children <15 years (9.5%). Sero-positivity was significantly higher in males (p=0.006) and in the 30-45 years age group (p=0.009). Adjusted titre values for anti-SARS-CoV-2 antibody were found to be 10.4 COI (SE = 3.38 COI) for the High Burden tertile; 4.8 (SE = 1.34 COI) for the Intermediate Burden tertile; and 6.1 COI (SE = 2.19 COI) for the Low Burden tertile.