As of March 9, 2023, the total number of COVID-19 cases in Indonesia amounted to approximately 6.74 million. Up until now, the death toll in Indonesia has risen to more than 160.9 thousand.

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

Context

BangkitIndonesiaKu merupakan sebuah open-source yang bertujuan untuk mengkolaborasikan analisa data Covid-19, terutama di Indonesia. Di negara Indonesia yang tercinta inilah kita dilahirkan dan dibesarkan. Kini negeri ini sedang mengalami gejolak pandemi yang tak kunjung usai. Dahsyatnya hempasan pandemi ini menciderai akal sehat manusia. Perut yang kosong, oknum-oknum yang tidak bertanggung jawab, informasi yang simpang siur, dan ekonomi yang tak kunjung pulih membuat masyarakat mulai muak atas semua ini.

Mencari formula kebijakan yang tepat merupakan salah satu cara untuk negeri ini bisa kembali pulih. Project open-source ini merupakan salah satu cara kita untuk berkolaborasi menemukan formula kebijakan yang tepat, terukur, dan transparan. Berkontribusi untuk negeri ini tidaklah harus menggunakan tenaga lapangan, namun juga bisa dengan donasi, maupun mengontribusikan ilmu-ilmu yang telah dikuasai. Saatnya generasi muda Indonesia bergerak untuk negeri ini, memberikan apapun yang kita bisa. Tidak perlu pintar ataupun kaya melainkan seseorang yang ingin belajar dan memiliki empati yang bisa membangkitkan Indonesia ini.

Pandemi Covid-19 merupakan masalah global yang sekarang sedang marak-maraknya. Oleh karena itu, Indonesia tidak bisa tinggal diam mengikuti apa aturan WHO. Kita juga harus memiliki terobosan-terobosan yang konkrit. Ajaklah seluruh teman-temanmu untuk berkontribusi untuk negeri ini.

1. Bagi yang memiliki dana yang lebih, sumbangkan.
2. Bagi yang memiliki waktu yang lebih, kontribusikan.
3. Bagi yang memiliki intelek yang lebih, kolaborasikan.

Content

Data yang disediakan berupa .csv yang telah melewati tahap cleaning dan siap diproses.

Upcoming: 1. Data Kebijakan Pemerintah mulai dari karantina wilayah pertama hingga sekarang

Acknowledgements

Penggunaan data ini diharapkan selalu memuat seluruh referensi dari riset-riset sebelumnya.

Inspiration

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

References

• Github Crawler Anda juga bisa berkontribusi di crawler ini

• Covid19.go.id

• WHO CSV

As of March 9, 2023, Indonesia registered 160,941 deaths from the coronavirus. This week, Indonesia is experiencing an increase in cases caused by the highly-contagious Omicron variant.

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

In past 24 hours, Indonesia, Asia had N/A new cases, N/A deaths and N/A recoveries.

Indonesia recorded 6799760 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Indonesia reported 161646 Coronavirus Deaths. This dataset includes a chart with historical data for Indonesia Coronavirus Cases.

Abstract

The World Bank has launched a quick-deploying high-frequency phone-monitoring survey of households to generate near real-time insights on the socio-economic impact of COVID-19 on households which hence to be used to support evidence-based response to the crisis. At a moment when all conventional modes of data collection have had to be suspended, a phone-based rapid data collection/tracking tool can generate large payoffs by helping identify affected populations across the vast archipelago as the contagion spreads, identify with a high degree of granularity the mechanisms of socio-economic impact, identify gaps in public policy response as the Government responds, generating insight that could be useful in scaling up or redirecting resources as necessary as the affected population copes and eventually regains economic footing.

Analysis unit

Household-level; Individual-level: household primary breadwinners, respondent, student, primary caregivers, and under-5 years old kids

Sampling procedure

The sampling frame of the Indonesia high-frequency phone-based monitoring of socio-economic impacts of COVID-19 on households was the list of households enumerated in three recent World Bank surveys, namely Urban Survey (US), Rural Poverty Survey (RPS), and Digital Economy Household Survey (DEHS). The US was conducted in 2018 with 3,527 sampled households living in the urban areas of 10 cities and 2 districts in 6 provinces. The RPS was conducted in 2019 with the sample size of 2,404 households living in rural areas of 12 districts in 6 provinces. The DEHS was conducted in 2020 with 3,107 sampled households, of which 2,079 households lived in urban areas and 1,028 households lived in rural areas in 26 districts and 31 cities within 27 provinces. Overall, the sampled households drawn from the three surveys across 40 districts and 35 cities in 27 provinces (out of 34 provinces). For the final sampling frame, six survey areas of the DEHS which were overlapped with the survey areas in the UPS were dropped from the sampling frame. This was done in order to avoid potential bias later on when calculating the weights (detailed below). The UPS was chosen to be kept since it had much larger samples (2,016 households) than that of the DEHS (265 households). Three stages of sampling strategies were applied. For the first stage, districts (as primary sampling unit (PSU)) were selected based on probability proportional to size (PPS) systematic sampling in each stratum, with the probability of selection was proportional to the estimated number of households based on the National Household Survey of Socio-economic (SUSENAS) 2019 data. Prior to the selection, districts were sorted by provincial code.

In the second stage, villages (as secondary sampling unit (SSU)) were selected systematically in each district, with probability of selection was proportional to the estimated number of households based on the Village Potential Census (PODES) 2018 data. Prior to the selection, villages were sorted by sub-district code. In the third stage, the number of households was selected systematically in each selected village. Prior to the selection, all households were sorted by implicit stratification, that is gender and education level of the head of households. If the primary selected households could not be contacted or refused to participate in the survey, these households were replaced by households from the same area where the non-response households were located and with the same gender and level of education of households’ head, in order to maintain the same distribution and representativeness of sampled households as in the initial design.

In the Round 8 survey where we focused on early nutrition knowledge and early child development, we introduced an additional respondent who is the primary caregiver of under 5 years old in the household. We prioritized the mother as the target of caregiver respondents. In households with multiple caregivers, one is randomly selected. Furthermore, only the under 5 children who were taken care of by the selected respondent will be listed in the early child development module.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire in English is provided for download under the Documentation section.

Response rate

The HiFy survey was initially designed as a 5-round panel survey. By end of the fifth round, it is expected that the survey can maintain around 3,000 panel households. Based on the experience of phone-based, panel survey conducted previously in other study in Indonesia, the response rates were expected to be around 60 percent to 80 percent. However, learned from other similar surveys globally, response rates of phone-based survey, moreover phone-based panel survey, are generally below 50 percent. Meanwhile, in the case of the HiFy, information on some of households’ phone numbers was from about 2 years prior the survey with a potential risk that the targeted respondents might not be contactable through that provided numbers (already inactive or the targeted respondents had changed their phone numbers). With these considerations, the estimated response rate of the first survey was set at 60 percent, while the response rates of the following rounds were expected to be 80 percent. Having these assumptions and target, the first round of the survey was expected to target 5,100 households, with 8,500 households in the lists. The actual sample of households in the first round was 4,338 households or 85 percent of the 5,100 target households. However, the response rates in the following rounds are higher than expected, making the sampled households successfully interviewed in Round 2 were 4,119 (95% of Round 1 samples), and in Rounds 3, 4, 5, 6, 7, and 8 were 4,067 (94%), 3,953 (91%), 3,686 (85%), 3,471 (80%), 3,435 (79%), 3,383 (78%) respectively. The number of balanced panel households up to Rounds 3, 4, 5, 6, 7, and 8 are 3,981 (92%), 3,794 (87%), 3,601 (83%), 3,320 (77%), 3,116 (72%), and 2,856 (66%) respectively.

Context

This is an early version of my dataset. I'm planing to expand this dataset with the addition of every province in Indonesia and vaccination data.

Content

This dataset contains the latest Covid-19 in Indonesia, from the first confirmed case in Indonesia, 02 March 2021 until 30 June 2021.

Acknowledgements

Thanks to covid.go.id/peta-sebaran where's this data is collected

Context

Global update on coronavirus disease As of 17 March 2020, a total of 179.112 confirmed cases have been reported for coronavirus disease (COVID-19) globally. Among these, there have been 7.426 deaths reported related to COVID-19.

Update on coronavirus disease in Indonesia As of 17 March 2020, the Government of the Republic of Indonesia has reported 172 confirmed cases and five deaths related to COVID-19. WHO is working with the Indonesian Government to monitor the situation and prevent further spread of disease.

Acknowledgements

• https://kawalcovid19.id/
• https://corona.jakarta.go.id/
• https://pikobar.jabarprov.go.id/

Analysis of ‘COVID-19 in Indonesia’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hanifnoerrofiq/covid19-in-indonesia on 30 September 2021.

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

Context

This is an early version of my dataset. I'm planing to expand this dataset with the addition of every province in Indonesia and vaccination data.

Content

This dataset contains the latest Covid-19 in Indonesia, from the first confirmed case in Indonesia, 02 March 2021 until 30 June 2021.

Acknowledgements

Thanks to covid.go.id/peta-sebaran where's this data is collected

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

Dataset

This dataset was created by Masayu Anandita

Released under Data files © Original Authors

Contents

Survey data from construction workers in West Java conducted in November and December 2020. Data on employment, wages, benefits, project type and value, mode of transport, supplementary incomes, work hours, health protocols, workers' preference, and impact of COVID on the workers.

Indonesia recorded 1880413 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, Indonesia reported 143969 Coronavirus Deaths. This dataset includes a chart with historical data for Indonesia Coronavirus Recovered.

Context:

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited:

Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Here’s a polished version suitable for a professional Kaggle dataset description:

Dataset Description

This dataset contains time-series and case-level records of the COVID-19 pandemic. The primary file is covid_19_data.csv, with supporting files for earlier records and individual-level line list data.

Files and Columns

1. covid_19_data.csv (Main File)

This is the primary dataset and contains aggregated COVID-19 statistics by location and date.

• Sno – Serial number of the record
• ObservationDate – Date of the observation (MM/DD/YYYY)
• Province/State – Province or state of the observation (may be missing for some entries)
• Country/Region – Country of the observation
• Last Update – Timestamp (UTC) when the record was last updated (not standardized, requires cleaning before use)
• Confirmed – Cumulative number of confirmed cases on that date
• Deaths – Cumulative number of deaths on that date
• Recovered – Cumulative number of recoveries on that date

2. 2019_ncov_data.csv (Legacy File)

This file contains earlier COVID-19 records. It is no longer updated and is provided only for historical reference. For current analysis, please use covid_19_data.csv.

3. COVID_open_line_list_data.csv

This file provides individual-level case information, obtained from an open data source. It includes patient demographics, travel history, and case outcomes.

4. COVID19_line_list_data.csv

Another individual-level case dataset, also obtained from public sources, with detailed patient-level information useful for micro-level epidemiological analysis.

✅ Use covid_19_data.csv for up-to-date aggregated global trends.

✅ Use the line list datasets for detailed, individual-level case analysis.

Country level datasets:

If you are interested in knowing country level data, please refer to the following Kaggle datasets:

India - https://www.kaggle.com/sudalairajkumar/covid19-in-india

South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset

Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa

Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland

Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

Acknowledgements :

Johns Hopkins University for making the data available for educational and academic research purposes

MoBS lab - https://www.mobs-lab.org/2019ncov.html

World Health Organization (WHO): https://www.who.int/

DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.

BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/

National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml

China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm

Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html

Macau Government: https://www.ssm.gov.mo/portal/

Taiwan CDC: https://sites.google....

Understanding the social determinants of Covid-19 infection and death is vital for effective Covid-19 early detection and mitigation strategies. This study aims to examine social determinants of Covid-19 infection and death in the context of rural Indonesia. We used Malang district government Covid-19 contact tracing data from 14,264 individuals, spanning the period from March 1, 2020 to July 29, 2020. The contact tracing data was merged with administrative data from 390 villages to determine whether village characteristics (i.e., the number of health workers, number of community-based healthcare interventions, access to Covid-19 referred hospitals, number of indigenous socio-cultural activities, poverty level and distance to a Covid-19 epicentre city) are associated with Covid-19 infection and death. We used multilevel logistic regression to take advantage of the nested structure of data at the village level. We found among the 14,264 samples, 551 individuals were confirmed infected with Covid-19, and 62 died of Covid-19. Individuals aged 18 and older, civil servants (non-health workers), and those having close contact with people with confirmed cases had a higher likelihood of infection with Covid-19. Greater numbers of community-based healthcare interventions and a lesser distance to a pandemic epicentre reduced the likelihood of infection with the virus. Males, older people, individuals with hypertension, individuals diagnosed with pneumonia, and those diagnosed with respiratory failure had a higher likelihood of death due to Covid-19. A greater number of community-based healthcare interventions seems to reduce the likelihood of Covid-19 infection, while better access to a Covid-19 referred hospital seems to reduce the risk of death among Covid-19 patients. The findings suggest the government to prioritise strategies to control the pandemic in rural area through empowering rural community in health education to prevent Covid-19 and in monitoring people mobility, while providing Covid-19 emergency services for rural areas for reducing mortality.

Risk factors (according to sex, age group and main island) for mortality of COVID-19 in Indonesia from March 2nd–August 2nd2020.

The data set contain the COVID-19 case report (total case) accumulated by province in Indonesia from 2020 - 2022. There are 7 variables within the data set, such as total case, population (total population), Aglo (number of districts stated as agglomeration area), prop_internet (proportion of internet user), density (density level of each province), and vacc 2 (number of population vaccinated). All data are in aggregate form.

COVID-19: To-Date: Vaccination: Dose 1: General Public and Vulnerable: Central Kalimantan: East Kotawaringin Regency data was reported at 184,160.000 Person in 17 May 2025. This stayed constant from the previous number of 184,160.000 Person for 16 May 2025. COVID-19: To-Date: Vaccination: Dose 1: General Public and Vulnerable: Central Kalimantan: East Kotawaringin Regency data is updated daily, averaging 184,160.000 Person from Nov 2021 (Median) to 17 May 2025, with 1028 observations. The data reached an all-time high of 331,160.000 Person in 05 Nov 2022 and a record low of 108,166.000 Person in 25 Nov 2021. COVID-19: To-Date: Vaccination: Dose 1: General Public and Vulnerable: Central Kalimantan: East Kotawaringin Regency data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under Indonesia Premium Database’s Health Sector – Table ID.HLB012: Coronavirus Disease 2019 (Covid-19): Vaccination Status: by Regency and Municipality: General Public and Vulnerable.

Indonesia Number of Bed: Covid-19: Intensive: Available: Bengkulu data was reported at 16.000 Unit in 09 Oct 2022. This stayed constant from the previous number of 16.000 Unit for 08 Oct 2022. Indonesia Number of Bed: Covid-19: Intensive: Available: Bengkulu data is updated daily, averaging 37.000 Unit from Aug 2021 (Median) to 09 Oct 2022, with 370 observations. The data reached an all-time high of 46.000 Unit in 01 Apr 2022 and a record low of 16.000 Unit in 09 Oct 2022. Indonesia Number of Bed: Covid-19: Intensive: Available: Bengkulu data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under Indonesia Premium Database’s Health Sector – Table ID.HLA013: Number of Available Hospital Bed: Covid-19: by Province (Discontinued).

According to a survey conducted in September 2020, about ** percent of Indonesian respondents that had a baccalaureate/university degree believe that it is really unlikely to get infected by COVID-19. As of October 13, 2020, the total number of COVID-19 cases in Indonesia amounted to ******* and the number had been increasing since Indonesia had its first COVID-19 case in March 2020.

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

COVID-19 CHILE dataset