About

Community collected, cleaned and organized COVID-19 datasets about India sourced from different government websites which are freely available to all. Here we have digitized them, so it can be used by all the researchers and students.

Infected Data

Column Discription

Main file in this dataset is COVID-19_India_Data.csv and the detailed descriptions are below.

Date_reported : Date of the observation in YYYY-MM-DD
cum_cases : Cumulative number of confirmed cases till that date
cum_death : Cumulative number of deaths till that date
cum_recovered : Cumulative number of recovered patients till that date
new_recovered : Daily new recovery
new_cases : New confirmed cases. Calculated by: current cum_cases - previous cum_case
new_death : New confirmed deaths. Calculated by: current cum_death - previous cum_death
cum_active_cases : Cumulative number of infected person till that date. Calculated by: cum_cases - cum_death - cum_recovered

Vaccination data

Column Discription

Main file in this dataset is Vaccination.csv and the detailed descriptions are below.

• date: date of the observation.
• total_vaccinations: total number of doses administered. For vaccines that require multiple doses, each individual dose is counted. If a person receives one dose of the vaccine, this metric goes up by 1. If they receive a second dose, it goes up by 1 again. If they receive a third/booster dose, it goes up by again.
• people_vaccinated: total number of people who received at least one vaccine dose. If a person receives the first dose of a 2-dose vaccine, this metric goes up by 1. If they receive the second dose, the metric stays the same.
• people_fully_vaccinated: total number of people who received all doses prescribed by the vaccination protocol. If a person receives the first dose of a 2-dose vaccine, this metric stays the same. If they receive the second dose, the metric goes up by 1.
• daily_vaccinations_raw: daily change in the total number of doses administered. It is only calculated for consecutive days. This is a raw measure provided for data checks and transparency, but we strongly recommend that any analysis on daily vaccination rates be conducted using daily_vaccinations instead.
• daily_vaccinations: new doses administered per day (7-day smoothed). For countries that don't report data on a daily basis, we assume that doses changed equally on a daily basis over any periods in which no data was reported. This produces a complete series of daily figures, which is then averaged over a rolling 7-day window. An example of how we perform this calculation can be found here.
• total_vaccinations_per_hundred: total_vaccinations per 100 people in the total population of the country.
• people_vaccinated_per_hundred: people_vaccinated per 100 people in the total population of the country.

Acknowledgements

• Johns Hopkins University for making the data available for educational and academic research purposes.
• World Health Organization (WHO): https://www.who.int/
• Government of India: https://www.mygov.in/covid-19, Ministry of Health and Family Welfare: https://www.mohfw.gov.in/
• Our World in Data: https://ourworldindata.org/covid-vaccinations?country=~IND
• https://data.covid19india.org/

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.

Context

Most of the datasets about covid19 vaccination data on Kaggle are not available citywise. So here I am to your rescue!! This version is just a starter with data for 9 cities. I plan to upload data for almost every city in India in the upcoming versions.

Vaccination planning has been a challenge in India. Earlier in the year, individual Indian citizens had to register on the Cowin or Aarogya Setu portal in order to receive a COVID-19 vaccination. The limited number of vaccination slots resulted in fewer administrations during the initial 5 months of the vaccination programme (phase 1–4). The Government of India has now amended the vaccination policy by waiving the preregistration requirement and offering free vaccinations to accelerate the programme. However, mass gatherings in healthcare settings might lead to a further surge in daily cases. Door-to-door vaccination might be a feasible and safe solution to avoid such assemblies.

Attribute Information

|: Date column. Contains date from 26 April,2020 to 31st Oct, 2021. || : Contains info about two variants of COVID: delta and delta7(delta7 is delta+ actually) ||_confirmed: Cases confirmed ||_deceased: Number of deaths reported ||_recovered: Cases recovered ||_tested: Number of people tested ||_vaccinated1: 1st dose of vaccine administered ||_vaccinated2: 2nd dose of vaccine administered |_total_confirmed: this column does not carry any information(did not remove it to maintain the originality of data) |_total_deceased: this column does not carry any information(did not remove it to maintain the originality of data) |_total_recovered: this column does not carry any information(did not remove it to maintain the originality of data)

More about the data

There are many NaN values in the data. They are not there because there is some error in the data. Vaccination dri drive started in India from Jan, 2021. So data for vaccination will be available from Jan,2021.

I am planning to upload the data for more cities in upcoming versions. If you want data of some specific city in India, ask for it in the discussion.

The number of COVID-19 vaccination doses administered per 100 people in India rose to 156 as of Oct 27 2023. This dataset includes a chart with historical data for India Coronavirus Vaccination Rate.

Content

The table has data about total number of doses administered and number of people who received a single and both the doses.

Inspiration

To Answer the question if vaccination is helping in reducing the number of daily cases

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...

Context

The raw state-wise and district-wise covid19 vaccination data published by covid19india.org.

Content

The data is downloaded from Covid19India.org and consist of three CSV files. - cowin_vaccine_data_districtwise.csv : Key data points from CoWin database at a district level - cowin_vaccine_data_statewise.csv : Key data points from CoWin database at a state level - vaccine_doses_statewise.csv : Number of vaccine doses administered statewise

Acknowledgements

Special thanks 🙏 to the Covid19India.org team for their data-rich website and API.

Inspiration

• In each state of India, how many are vaccinated each day?
• How many are vaccinated in each district of a state as per the CoWin database?

Despite the significant success of India’s COVID-19 vaccination program, a sizeable proportion of the adult population remains unvaccinated or has received a single dose of the vaccine. Despite the recommendations of the Government of India for the two doses of the COVID-19 vaccine and the precautionary booster dose, many people were still hesitant towards the COVID-19 full vaccination. Hence, this study aimed to identify the primary behavioral and psychological factors contributing to vaccine hesitancy. Cross-sectional data was collected via a multi-stage sampling design by using a scheduled sample survey in the Gorakhpur district of Uttar Pradesh, India, between 15 July 2022 to 30 September 2022. This study has utilized three health behavior models—the Health Belief Model (HBM), the Theory of Planned Behavior (TPB), and the 5C Psychological Antecedents of vaccination, and employed bivariate and multivariable binary logistic regression model to assess the level of vaccine hesitancy and predictive health behavior of the respondents. Results indicate that among the constructs of the HBM and 5C Antecedents models, "perceived benefits", "confidence" and "collective responsibility" showed a lesser likelihood of COVID-19 vaccine hesitancy. However, in the TPB model constructs, a ‘negative attitude towards the vaccine’ showed a four times higher likelihood of COVID-19 vaccine hesitancy. From the future policy perspective, this study suggested that addressing the issue of ‘negative attitudes towards the vaccine’ and increasing the trust or confidence for the vaccine through increasing awareness about the benefits of the vaccination in India may reduce vaccine hesitancy.

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.

Context

As India recently Crossed the milestone of Administering 1 Billion Covid Doses. So here is the DataSet which provides the Figures of covid 19 doses state-wise.

Content

The DATA SET provides the information about the pace at which Covid - 19 Doses (both Dose 1 and 2) administered In India Statewise.

Acknowledgements

I would like to thank Goverment Of India for doing such a Fantastic Job of Driving a National Wide Campaign Of Administering Free Covid-19 Doses through out country.

Inspiration

Any Hidden Pattern

The current COVID-19 pandemic has urged the scientific community internationally to find answers in terms of therapeutics and vaccines to control SARS-CoV-2. The Indian government has opened Covid-19 vaccination. All citizens above the age of 18 can get vaccinated now. Citizens can go to CoWin Website to register themselves. Thanks to CoWin API for publicly providing the data.

Lets get vaccinated!

BackgroundCanine transmitted rabies kills an estimated 59,000 people annually, despite proven methods for elimination through mass dog vaccination. Challenges in directing and monitoring numerous remote vaccination teams across large geographic areas remain a significant barrier to the up-scaling of focal vaccination programmes to sub-national and national level. Smartphone technology (mHealth) is increasingly being used to enhance the coordination and efficiency of public health initiatives in developing countries, however examples of successful scaling beyond pilot implementation are rare. This study describes a smartphone app and website platform, “Mission Rabies App”, used to co-ordinate rabies control activities at project sites in four continents to vaccinate over one million dogs.MethodsMission Rabies App made it possible to not only gather relevant campaign data from the field, but also to direct vaccination teams systematically in near real-time. The display of user-allocated boundaries on Google maps within data collection forms enabled a project manager to define each team’s region of work, assess their output and assign subsequent areas to progressively vaccinate across a geographic area. This ability to monitor work and react to a rapidly changing situation has the potential to improve efficiency and coverage achieved, compared to regular project management structures, as well as enhancing capacity for data review and analysis from remote areas. The ability to plot the location of every vaccine administered facilitated engagement with stakeholders through transparent reporting, and has the potential to motivate politicians to support such activities.ResultsSince the system launched in September 2014, over 1.5 million data entries have been made to record dog vaccinations, rabies education classes and field surveys in 16 countries. Use of the system has increased year-on-year with adoption for mass dog vaccination campaigns at the India state level in Goa and national level in Haiti.ConclusionsInnovative approaches to rapidly scale mass dog vaccination programmes in a sustained and systematic fashion are urgently needed to achieve the WHO, OIE and FAO goal to eliminate canine-transmitted human deaths by 2030. The Mission Rabies App is an mHealth innovation which greatly reduces the logistical and managerial barriers to implementing large scale rabies control activities. Free access to the platform aims to support pilot campaigns to better structure and report on proof-of-concept initiatives, clearly presenting outcomes and opportunities for expansion. The functionalities of the Mission Rabies App may also be beneficial to other infectious disease interventions.

This dataset corresponds to paper titled "COVID-19: Risks of Re-emergence, Re-infection, and Control Measures -- A Long Term Modeling Study". In this work we define a modified SEIR model that accounts for the spread of infection during the latent period, infections from asymptomatic or pauci-symptomatic infected individuals, potential loss of acquired immunity, people’s increasing awareness of social distancing and the use of vaccination as well as non-pharmaceutical interventions like social confinement. We estimate model parameters in three different scenarios - in Italy, where there is a growing number of cases and re-emergence of the epidemic, in India, where there are significant number of cases post confinement period and in Victoria, Australia where a re-emergence has been controlled with severe social confinement program. Our result shows the benefit of long term confinement of 50% or above population and extensive testing. With respect to loss of acquired immunity, our model suggests higher impact for Italy. We also show that a reasonably effective vaccine with mass vaccination program can be successful in significantly controlling the size of infected population. We show that for India, a reduction in contact rate by 50% compared to a reduction of 10% in the current stage can reduce death from 0.0268% to 0.0141% of population. Similarly, for Italy we show that reducing contact rate by half can reduce a potential peak infection of 15% population to less than 1.5% of population, and potential deaths from 0.48% to 0.04%. With respect to vaccination, we show that even a 75% efficient vaccine administered to 50% population can reduce the peak number of infected population by nearly 50% in Italy. Similarly, for India, a 0.056% of population would die without vaccination, while 93.75% efficient vaccine given to 30\% population would bring this down to 0.036% of population, and 93.75% efficient vaccine given to 70% population would bring this down to 0.034%.

This dataset corresponds to paper titled "A Mathematical Model for COVID-19 Considering Waning Immunity, Vaccination and Control Measures". In this work we define a modified SEIR model that accounts for the spread of infection during the latent period, infections from asymptomatic or pauci-symptomatic infected individuals, potential loss of acquired immunity, people’s increasing awareness of social distancing and the use of vaccination as well as non-pharmaceutical interventions like social confinement. We estimate model parameters in three different scenarios - in Italy, where there is a growing number of cases and re-emergence of the epidemic, in India, where there are significant number of cases post confinement period and in Victoria, Australia where a re-emergence has been controlled with severe social confinement program. Our result shows the benefit of long term confinement of 50% or above population and extensive testing. With respect to loss of acquired immunity, our model suggests higher impact for Italy. We also show that a reasonably effective vaccine with mass vaccination program can be successful in significantly controlling the size of infected population. We show that for India, a reduction in contact rate by 50% compared to a reduction of 10% in the current stage can reduce death from 0.0268% to 0.0141% of population. Similarly, for Italy we show that reducing contact rate by half can reduce a potential peak infection of 15% population to less than 1.5% of population, and potential deaths from 0.48% to 0.04%. With respect to vaccination, we show that even a 75% efficient vaccine administered to 50% population can reduce the peak number of infected population by nearly 50% in Italy. Similarly, for India, a 0.056% of population would die without vaccination, while 93.75% efficient vaccine given to 30\% population would bring this down to 0.036% of population, and 93.75% efficient vaccine given to 70% population would bring this down to 0.034%.

Context

• First case of COVID-19 in India was announced on 27th January 2020 in Thrissur Kerala. After October 2020, there was some relief due to the subsiding of the curve of the covid case.
• In January 2020, the Government of India approved two vaccines i.e. Astra-Zeneca's Vaccine produced by Serum Institute - Covidsheild and Bharat Biotech's indigenous vaccine - Covaxin.
• This dataset deals with these vaccinations in India and provides insights into them. The two files provide information about daily COVID-19 vaccinations throughout the country and total vaccinations in each state.
• The data was collected from Our World in Data Github repo for covid-19 and Government of India website containing Vaccinations State Data, merged with relevant attributes, and uploaded.
• Government of India also approved Russia's Sputnik V for emergency use authorization on 12th April 2021.

Content

Covid-19_India_Daywise_Vaccinations.csv

Columns: - location- Location of the vaccination(country). - date- Date in format dd-mm-yyyy. - vaccine- Name of the vaccine(s) administered in the country on that day. - source_url- Source of the information for the vaccination. - total-vaccinations- Total number of doses administered till that day. If a person receives one dose of the vaccine, this metric goes up by 1. If they receive a second dose, it goes up by 1 again. - total_vaccinations_per_hundred- total_vaccinations per 100 people in the total population. - people_vaccinated- Total number of people who received at least one vaccine dose. If a person receives the first dose of a 2-dose vaccine, this metric goes up by 1. If they receive the second dose, the metric stays the same. - people_vaccinated_per_hundred- people_vaccinated per 100 people in the total population. - people_fully_vaccinated- Total number of people who received all doses prescribed by the vaccination protocol. If a person receives the first dose of a 2-dose vaccine, this metric stays the same. If they receive the second dose, the metric goes up by 1. - people_fully_vaccinated_per_hundred- people_fully_vaccinated per 100 people in the total population. - daily_vaccinations- New doses administered per day. - daily_vaccinations_per_million- daily_vaccinations per 1,000,000 people in the total population. - daily_change_in_vaccinations- Change in the number of doses administered (daily_vaccinations) from the previous day.

Covid-19_Statewise_Vaccination_India.csv

Columns: - State/Union Territory- Name of the State or Union Territory. - Population (2011 census)- Population of the State/UT based on 2011 census. - 1st dose- Number of first doses that were administered. - 2nd dose- Number of second doses that were administered. - Cumulative doses administered- Total number of doses administered till date. - Percentage of people given atleast one dose- Percent of the population of the state. - Percentage of people fully vaccinated- Percent of the population of the state.

Acknowledgements

I like to specify that I am only making available to Kagglers the data that is produced and maintained by Our World in Data through their Github repo, and also the Ministry of Health and Family Welfare Government of India which provide daily vaccine stats through their website. - Our World in Data Github Repo - Ministry of Health and Family Welfare Government of India

Inspiration

From this data, what you could do is: - Visualisations about the daily vaccination trends in the country. - Which state has the fastest pace in vaccination? - Prediction of future daily vaccinations in the country.

Context

The research shows that the U.S. had secured 1.01 billion doses from six different companies up to November 20 which represents the highest quantity of any government apart from India which has made agreements for 1.6 billion. Pfizer/BioNTech and Moderna both account for 100 million U.S. doses each while the U.S. is also set for 500 million doses of the vaccine being developed by the University of Oxford and AstraZeneca.

https://www.statista.com/chart/23660/umber-of-doses-of-covid-19-vaccines-secured-by-the-us/

Content

This chart shows the number of doses of Covid-19 vaccines secured by the U.S. as of November 20, 2020.

Acknowledgements

Niall McCarthy, Data Journalist.

https://www.statista.com/chart/23660/umber-of-doses-of-covid-19-vaccines-secured-by-the-us/

Inspiration

Covid-19 Pandemic.

Context

What Is COVID-19?

A coronavirus is a kind of common virus that causes an infection in your nose, sinuses, or upper throat. Most coronaviruses aren't dangerous.

COVID-19 is a disease that can cause what doctors call a respiratory tract infection. It can affect your upper respiratory tract (sinuses, nose, and throat) or lower respiratory tract (windpipe and lungs). It's caused by a coronavirus named SARS-CoV-2.

It spreads the same way other coronaviruses do, mainly through person-to-person contact. Infections range from mild to serious.

SARS-CoV-2 is one of seven types of coronavirus, including the ones that cause severe diseases like Middle East respiratory syndrome (MERS) and sudden acute respiratory syndrome (SARS). The other coronaviruses cause most of the colds that affect us during the year but aren’t a serious threat for otherwise healthy people.

In early 2020, after a December 2019 outbreak in China, the World Health Organization identified SARS-CoV-2 as a new type of coronavirus. The outbreak quickly spread around the world.

Is there more than one strain of SARS-CoV-2?

It’s normal for a virus to change, or mutate, as it infects people. A Chinese study of 103 COVID-19 cases suggests the virus that causes it has done just that. They found two strains, which they named L and S. The S type is older, but the L type was more common in early stages of the outbreak. They think one may cause more cases of the disease than the other, but they’re still working on what it all means.

How long will the coronavirus last?

It’s too soon to tell how long the pandemic will continue. It depends on many things, including researchers’ work to learn more about the virus, their search for a treatment and a vaccine, and the public’s efforts to slow the spread.

Dozens of vaccine candidates are in various stages of development and testing. This process usually takes years. Researchers are speeding it up as much as they can, but it still might take 12 to 18 months to find a vaccine that works and is safe.

Symptoms of COVID-19

The main symptoms include:

• Fever
• Coughing
• Shortness of breath
• Fatigue
• Chills, sometimes with shaking
• Body aches
• Headache
• Sore throat
• Loss of smell or taste
• Nausea
• Diarrhea

The virus can lead to pneumonia, respiratory failure, septic shock, and death. Many COVID-19 complications may be caused by a condition known as cytokine release syndrome or a cytokine storm. This is when an infection triggers your immune system to flood your bloodstream with inflammatory proteins called cytokines. They can kill tissue and damage your organs.

STAY HOME. STAY SAFE !

Content

ALL DATASETS HAVE BEEN CLEANED FOR DIRECT USE.

Total_World_covid-19.csv : This dataset contains the worldwide data country-wise such as total cases , total active, deaths, etc. along with testing data.

Total_India_covid-19.csv : This dataset contains India level data statewise such as confirmed cases , active cases, deaths, etc.

Total_US_covid-19.csv : This dataset contains India level data statewise such as confirmed cases , active cases, deaths, etc.

Daily_States_India.csv : This dataset contains daily statewise data of India such as daily confirmed , daily active , daily deaths and daily recovered.

Total_Maharshtra_covid-19.csv : This dataset contains Maharashtra's district wise data such as confirmed cases , active cases, deaths, etc.

Acknowledgements

1. World and US data has been collected from Worldometer . Thanks a lot.

2. India and State level along with Maharashtra district data has been collected from Covid19India. Special thanks to them for providing updated and such wonderful data .

Inspiration

1) What has been the Covid-19 trend across the world, Is it declining? Is it increasing? 2) Which countries have been able to sustain and control the virus spread? 3) How is India coping up with the virus? Have they been able to control it at the given cost of 2 months nationwide lockdown?