In 2023, the number of deaths per 1,000 inhabitants in India was ****. Between 1960 and 2023, the figure dropped by *****, though the decline followed an uneven course rather than a steady trajectory.

Actual value and historical data chart for India Death Rate Crude Per 1 000 People

The statistic shows the adult mortality rate in India from 2013 to 2023, by gender. According to the source, the adult mortality rate is the probability of dying between the ages of 15 and 60 - that is, the probability of a 15-year-old dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages. In 2023, the mortality rate for women was at ****** per 1,000 female adults, while the mortality rate for men was at ****** per 1,000 male adults in India.

Vital Statistics: Death Rate: per 1000 Population: Delhi: Rural data was reported at 4.100 NA in 2020. This records an increase from the previous number of 3.800 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Delhi: Rural data is updated yearly, averaging 4.700 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 5.500 NA in 2005 and a record low of 3.700 NA in 2018. Vital Statistics: Death Rate: per 1000 Population: Delhi: Rural data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.

In 2020, the state of Kerala, with 7.1 deaths per 1,000 inhabitants, had the highest urban death rate. It was followed by Odisha and Chhattisgarh . On the contrary, the region of Delhi had the lowest urban deaths during the same period. Death rates for India between 2004 and 2020 can be found here.

Vital Statistics: Death Rate: per 1000 Population: Karnataka data was reported at 6.200 NA in 2020. This stayed constant from the previous number of 6.200 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Karnataka data is updated yearly, averaging 7.100 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 7.900 NA in 1998 and a record low of 6.200 NA in 2020. Vital Statistics: Death Rate: per 1000 Population: Karnataka data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.

UNICEF's country profile for India, including under-five mortality rates, child health, education and sanitation data.

Almost ** percent of all daily deaths in Delhi between 2008 and 2019 were linked to fine particulate matter (PM2.5) levels higher than World Health Organization (WHO) guidelines for safe exposure. Annual average exposure to PM2.5 in Delhi exceeded 100 micrograms per cubic meter of air in 2023. On average, *** per cent of all daily deaths in these ** major Indian cities were linked to PM2.5 air pollution during this period.

Vital Statistics: Death Rate: per 1000 Population: Uttar Pradesh data was reported at 6.500 NA in 2020. This stayed constant from the previous number of 6.500 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Uttar Pradesh data is updated yearly, averaging 8.200 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 10.500 NA in 1999 and a record low of 6.500 NA in 2020. Vital Statistics: Death Rate: per 1000 Population: Uttar Pradesh data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.

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

Burden on the healthcare system

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

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

Female child mortality rate of India declined by 4.79% from 29.2 deaths per thousand live births in 2022 to 27.8 deaths per thousand live births in 2023. Since the 5.90% slump in 2013, female child mortality rate plummeted by 45.49% in 2023. Child mortality rate is the probability of dying between the exact ages of one and five, if subject to current age-specific mortality rates. The probability is expressed as a rate per 1,000.

Vital Statistics: Death Rate: per 1000 Population: West Bengal: Rural data was reported at 5.300 NA in 2020. This records an increase from the previous number of 5.200 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: West Bengal: Rural data is updated yearly, averaging 6.200 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 7.700 NA in 1998 and a record low of 5.200 NA in 2019. Vital Statistics: Death Rate: per 1000 Population: West Bengal: Rural data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.

In 2020, 108 out of 1,000 elderly people living in rural India, died at an age between 80 and 84 years old. The highest death rate was among elderly people who were 85 years or older in the country.

Vital Statistics: Death Rate: per 1000 Population: Andhra Pradesh: Urban data was reported at 4.900 NA in 2020. This records an increase from the previous number of 4.800 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Andhra Pradesh: Urban data is updated yearly, averaging 5.400 NA from Dec 1997 (Median) to 2020, with 23 observations. The data reached an all-time high of 6.100 NA in 1998 and a record low of 4.800 NA in 2019. Vital Statistics: Death Rate: per 1000 Population: Andhra Pradesh: Urban data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.

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.

Vital Statistics: Death Rate: per 1000 Population: Telangana: Urban data was reported at 4.200 NA in 2020. This records a decrease from the previous number of 4.300 NA for 2019. Vital Statistics: Death Rate: per 1000 Population: Telangana: Urban data is updated yearly, averaging 4.500 NA from Dec 2014 (Median) to 2020, with 7 observations. The data reached an all-time high of 4.900 NA in 2015 and a record low of 4.200 NA in 2020. Vital Statistics: Death Rate: per 1000 Population: Telangana: Urban data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAH003: Vital Statistics: Death Rate: by States.

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

Dataset Description: Infected and Death Cases of Covid-19 in Bangladesh This dataset contains detailed information on Covid-19 cases in Bangladesh, focusing on the number of new cases and deaths reported. The data spans from September 27, 2020, to November 19, 2021. The dataset is structured with three primary columns:

Date: The date when the data was recorded, formatted as YYYY-MM-DD. New Cases: The number of new Covid-19 cases reported on the corresponding date. Deaths: The number of deaths attributed to Covid-19 on the corresponding date. Key Features: Time Range: Covers over a year of data, capturing various waves of the pandemic. Granularity: Daily records, providing detailed insights into the daily progression of the pandemic. Size: The dataset is compact, with a file size of 7.91 KB, making it easy to handle and analyze. Cite this paper

@InProceedings{10.1007/978-981-19-2445-3_38, author="Rahman, Ashifur and Hossain, Md. Akbar and Moon, Mohasina Jannat", editor="Hossain, Sazzad and Hossain, Md. Shahadat and Kaiser, M. Shamim and Majumder, Satya Prasad and Ray, Kanad", title="An LSTM-Based Forecast Of COVID-19 For Bangladesh", booktitle="Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021 ", year="2022", publisher="Springer Nature Singapore", address="Singapore", pages="551--561", abstract="Preoperative events can be predicted using deep learning-based forecasting techniques. It can help to improve future decision-making. Deep learning has traditionally been used to identify and evaluate adverse risks in a variety of major applications. Numerous prediction approaches are commonly applied to deal with forecasting challenges. The number of infected people, as well as the mortality rate of COVID-19, is increasing every day. Many countries, including India, Brazil, and the United States, were severely affected; however, since the very first case was identified, the transmission rate has decreased dramatically after a set time period. Bangladesh, on the other hand, was unable to keep the rate of infection low. In this situation, several methods have been developed to forecast the number of affected, time to recover, and the number of deaths. This research illustrates the ability of DL models to forecast the number of affected and dead people as a result of COVID-19, which is now regarded as a possible threat to humanity. As part of this study, we developed an LSTM based method to predict the next 100 days of death and newly identified COVID-19 cases in Bangladesh. To do this experiment we collect data on death and newly detected COVID-19 cases through Bangladesh's national COVID-19 help desk website. After collecting data we processed it to make a dataset for training our LSTM model. After completing the training, we predict our model with the test dataset. The result of our model is very robust on the basis of the training and testing dataset. Finally, we forecast the subsequent 100 days of deaths and newly infected COVID-19 cases in Bangladesh.", isbn="978-981-19-2445-3" }

Dataset

This dataset was created by Advik Maniar

Contents

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

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