INDONESIA | COVID-19 Data

Corona virus distribution map in Indonesian district level

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Data Source: Pulse Lab Jakarta and BNPB   |  updated at:
Note: The visualisation is based on the number of reported COVID-19 cases at district level. Each point represents a person with red as Positive case, orange as PDP (Patient under observation), and green as ODP (individual under observation). The location of each point does not represent actual location of the person. The PSBB (Large-scale Social Restrictions) simulation is an animation done to show the possible movement of each case. At this stage, it illustrates random movement within district boundaries and how each can further spread the virus even with PSBB restrictions.

INDONESIA – COVID-19 Data


Without data on COVID-19 cases, it is difficult to understand how the pandemic is spreading through time and how it is being reported. Currently, we are able to gather COVID-19 data in Indonesia at the province level and from most of the districts/municipalities. Observable also is sub-district and village level data on a range of different sites. This tool is to gather publically available data from different levels of government (starting with district level data) to help understand the underlying data ecosystem in relation to COVID-19 reporting in Indonesia.

Many of the provinces share their data on public sites through a common domain format (https://corona.provincename.go.id). Other are doing so through their regional health office, while a number are reporting through customized links.

What we found...

Of the 34 provinces and 523 district level governments (including municipalities) in Indonesia, we were able to find and collate data directly from 22 provinces and 299 district level datasets.

Some provinces provide very good data on COVID-19...

Some provinces present comprehensive, detailed and regularly updated data. West Java is one of the examples. Jakarta goes even further, showing breakdowns by district, sub-district, and village. For many provinces, however, available data on “Positive” cases, ODP (individuals under observation) and PDP (patients under observation) is either incomplete or else is completely unavailable, at least in terms of publicly available official sites.

Indicating what indicators mean…

Provinces that do publish data on COVID-19 cases often do not define or clarify what the provided numbers mean, and this is crucial for meaningful comparisons between provinces and over time. Within the four categories official government categories used: a) Positif (confirmed positive case), Orang Tanpa Gejala - OTG (asymptomatic individual), Orang Dibawah Pengawasan - ODP (individual under observation) , and Pasien Dibawah Pengawasan – PDP(Patient under observation) data is presented under different categories, subcategories and format. This allows for possible misinterpretation of data and issues in reporting. The following table outlines some of the subcategories we found under the four main categories which illustrates where clear definitions and protocols may help.

Category Subcategory
Positif (confirmed positive case) Positif rawat (positive treated)
Positif isolasi mandiri (positive self isolation)
Positif sembuh (positive recovered)
Positif meninggal (positive deceased)
Orang Tanpa Gejala - OTG (asymptomatic individual) OTG dalam pemantauan (asymptomatic under observation)
OTG selesai pemantauan (asymptomatic completed observation)
OTG hasil negatif (negative tested asymptomatic)
OTG hasil positif (positive asymptomatic)
OTG meninggal (deceased asymptomatic)
Orang Dibawah Pengawasan - ODP (individual under observation) ODP dalam pantau (symptomatic under observation)
ODP selesai pemantauan (symptomatic no longer under observation)
ODP total diperiksa (total tested asymptomatic cases)
ODP dalam prosesl ab (symptomatic awaiting test results)
ODP hasil negatif (negatively tested symptomatic)
ODP hasil positif (positive symptomatic)
ODP meninggal (deceased symptomatic)
Pasien Dibawah Pengawasan – PDP(Patient under observation) PDP dalam pemantauan (patient under observation)
PDP selesai pengawasan (patient no longer under observation)
PDP total periksa ((total tested patients under observation)
PDP dalam proses lab (patients under observation awaiting test results)
PDP hasil negatif (negative patients under observation)
PDP hasil positif (positive patient under observation)
PDP meninggal (deceased patient under observation)

A basic checklist for COVID-19 data

Based on this exercise, we suggest the following “checklist” could be employed in examining data presented and/or provided. By fulfilling each of these items, the underlying data ecosystem to report on COVID-19 cases can be somewhat improved, allowing for more efficient and hopefully more reliable interpretation and analysis of published data:

  • Is it the data available in a format that is easy to extract?
    Many provinces are providing and aggregating figures on district level cases. We found that a number do not do so on a regular basis. Currently, much of the data is often not easy to find and inconsistent. Some even come in a format that can’t be extracted or use technologies to prevent simple extraction of the data.
  • Do the figures include all the required indicators?
    Figures reported by provinces and districts may only be partial, and there is no certainty whether all reports from laboratories and hospitals are included. We observed that currently there is no data publicy provided that explicitly identifies the source(s). Also whether the figures published on any given date is a final consolidated figure where actual figures due to delays in testing results. As such, some clarity on where they may be discrepancies should also be provided.
  • Are there any issues that affect the comparability of the data over time?
    This is to ensure that, when recording or analysing figures over time, there is clarity if reporting protocols, formats or other factors may have been enforced which as affected how the data is structured and reported.

Addressing Gaps…

Currently observable are multiple data “gaps”. In undertaking this process, it immediately becomes apparent the need for strong and effective collaboration. So far, Pulse Lab Jakarta has been able to work with BNPB to provide necessary data not openly available through the mapping process we implemented to collect COVID-19 data across Indonesia. This has allowed us to include data from areas we were unable to obtain. Ideally to avoid duplication of processes and efforts, this would entail all 34 provinces presenting aggregated data sourced from 436 Districts datasets (12 provinces and 137 districts sourced through BNPB).

Below are the details of the current condition:

Province Link Compiled Format Note
Aceh https://covid19.acehprov.go.id/ PLJ HTML District data available on this site (HTML)
Bali https://pendataan.baliprov.go.id/ PLJ HTML District data available on this site (HTML)
Banten https://infocorona.bantenprov.go.id/odp PLJ HTML District data available on this site (HTML)
Bengkulu https://covid19.bengkuluprov.go.id/ | https://siaga.bnpb.go.id/pm/covidkab.json BNPB JSON PDF | system automatically use BNPB data
Daerah Istimewa Yogyakarta http://corona.jogjaprov.go.id/map-covid-19-diy | https://siaga.bnpb.go.id/pm/covidkab.json BNPB JSON Tableau | system automatically use BNPB data
DKI Jakarta https://corona.jakarta.go.id/id/peta-persebaran PLJ JSON ArcGIS API on peta-sebaran
Gorontalo https://dinkes.gorontaloprov.go.id/covid-19/ PLJ HTML District data available on this site (HTML)
Jambi http://corona.jambiprov.go.id/maps/js/data.js PLJ HTML/Javascript Javascript crawling
Jawa Barat https://pikobar.jabarprov.go.id/data PLJ JSON Really Good API
Jawa Tengah https://corona.jatengprov.go.id/ | https://siaga.bnpb.go.id/pm/covidkab.json BNPB JSON Tableau | system automatically use BNPB data
Jawa Timur http://infocovid19.jatimprov.go.id/ PLJ HTML District data available on this site (HTML)
Kalimantan Barat http://covid19.kalbarprov.go.id/ | https://siaga.bnpb.go.id/pm/covidkab.json BNPB JSON Tableau | system automatically use BNPB data
Kalimantan Selatan https://corona.kalselprov.go.id/ PLJ HTML/Javascript Javascript crawling
Kelimantan Tengah https://corona.kalteng.go.id/ PLJ HTML/Javascript Javascript crawling
Kalimantan Timur https://covid19.kaltimprov.go.id/#downloads PLJ / BNPB DOCX Not always work. if it works at the crawling time, it will be compiled by PLJ. If not, system will outomatically use BNPB's data
Kalimantan Utara https://www.kaltaraprov.go.id/ | https://siaga.bnpb.go.id/pm/covidkab.json BNPB JSON There is no breakdown information | system automatically use BNPB data
Kepulauan Bangka Belitung https://covid19.babelprov.go.id/ PLJ HTML District data available on this site (HTML)
Kepulauan Riau https://corona.kepriprov.go.id/data.phtml PLJ HTML District data available on this site (HTML)
Lampung http://dinkes.lampungprov.go.id/petacovid19/data.geojson PLJ JSON Api based
Maluku https://corona.malukuprov.go.id/peta/ PLJ HTML/Javascript Javascript crawling
Maluku Utara http://corona.malutprov.go.id/datawilayah?_=1587349389885 PLJ JSON Api based
Nusa Tenggara Barat https://corona.ntbprov.go.id/list-data | https://siaga.bnpb.go.id/pm/covidkab.json BNPB JSON failed (blocked) | system automatically use BNPB data
Nusa Tenggara Timur http://nttprov.go.id/ | https://siaga.bnpb.go.id/pm/covidkab.json - There is no covid information | BNPB also does not have data
Papua https://www.papua.go.id/ | https://siaga.bnpb.go.id/pm/covidkab.json BNPB JSON PDF | system automatically use BNPB data
Papua Barat https://dinkes.papuabaratprov.go.id/ PLJ HTML District data available on this site (HTML)
Riau https://corona.riau.go.id/data-statistik/ | https://siaga.bnpb.go.id/pm/covidkab.json BNPB JSON Image | system automatically use BNPB data
Sulawesi Barat https://covid19.sulbarprov.go.id/utama/data PLJ HTML District data available on this site (HTML)
Sulawesi Selatan https://covid19.sulselprov.go.id/data PLJ HTML District data available on this site (HTML)
Sulawesi Tengah https://washcovidsulteng.maps.arcgis.com/apps/opsdashboard PLJ JSON ArcGIS Server API
Sulawesi Tenggara http://sultraprov.go.id/ | https://siaga.bnpb.go.id/pm/covidkab.json BNPB JSON PDF | system automatically use BNPB data
Sulawesi Utara https://corona.sulutprov.go.id</td> PLJ HTML/Javascript Javascript crawling
Sumatra Barat https://corona.sumbarprov.go.id/details/peta_covid19 PLJ JSON ArcGIS Server API
Sumatera Selatan http://sumselprov.go.id | https://siaga.bnpb.go.id/pm/covidkab.json BNPB JSON There is no breakdown information | system automatically use BNPB data
Sumatra Utara http://covid19.sumutprov.go.id/ | https://siaga.bnpb.go.id/pm/covidkab.json BNPB JSON There is no breakdown information | system automatically use BNPB data
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