India COVID-19 Vaccine Tracker

On January 16, 2021, the Government of India and State governments launched one of the most extensive vaccination drives against COVID-19, targeting 300 million priority beneficiaries comprising healthcare workers, frontline workers, and people above 50 years of age, and further expanding eligibility to those above 18 years of age in May 2021. Detailed Operational Guidelines are supporting the mass vaccination effort, as well as a COVID-19 Vaccine Intelligence Network (Co-WIN) system to track beneficiaries on a real-time basis. The dashboards below visualize daily updates of data from CoWIN for the districts and parliamentary constituencies of India. District administrators, elected representatives, policymakers and the public-at-large can use this data to understand how vaccine distribution is progressing and respond effectively.

Geographic Visualization

Geographic Visualization

 

Note: Click on the category name to open up the indicators for selection. (Example: Vaccines by Age has three indicators once opened). Click on the "Table View" to see the rankings of districts and parliamentary constituencies. The numbers for districts and parliamentary constituencies might not add up because of the crosswalk methodology applied. Please visit the Methodology section to learn how the parliamentary constituencies' estimates were derived.

Citation: Akhil Kumar, William Joe, Abhishek Kumar, Atif Amin, Weiyu Wang, Rockli Kim, Rakesh Sarwal, S V Subramanian. India COVID-19 Vaccine Tracker for Districts and Parliamentary Constituencies of India. Apr 2021. Geographic Insights Lab at the Harvard Center for Population and Development Studies; Center for Geographic Analysis at Harvard University, Cambridge, MA.

Methodology

Data Sources

The data visualized is from the CoWIN Dashboard. The data visualizations will be updated daily around 8 pm IST (10:30 am ET). In the source data, the age and dose-specific category numbers might not add up to the total doses administered. The data for Niwari (Madhya Pradesh), Hnahthial (Mizoram), Khawzawl (Mizoram), Mayiladuthurai (Tamil Nadu), Noklak (Nagaland) is not available. The data for Mumbai and Mumbai Suburban in the state of Maharashtra are reported under Mumbai.

We downloaded the shapefiles for district and parliamentary constituencies from the Community Created Maps of India (CCMA) project published by Data {Meet}. We edited the district shapefile in alignment with the latest district boundary of 2020.

We calculated the population estimates (https://doi.org/10.7910/DVN/RXYJR6) for districts and parliamentary constituencies by aggregating the population count across 100m × 100m pixels over the shapefile boundaries using the WorldPop raster data. The derived population estimates are therefore related to the accuracy of the shapefiles. We calculated the percentage of the dose administered by dividing the Total Dose Administered by the estimated population of 2020.

Crosswalk Methodology (Districts to Parliamentary Constituencies)

Using district-level information for vaccines, we generated a crosswalk between districts and parliamentary constituencies. We adopted a dasymetric mapping technique that allowed for weighting of the population in each PC assuming the indicators were distributed uniformly across the population in one district. See the workflow below and read the paper for more details.

  1. Calculate district area (dist_area): perform 'Calculate Geometry' function on the district shapefile, using the Kalianpur 1975/India Zone lla (EPSG: 24379) coordinate system.
  2. Intersect PC and district boundaries, link each segment with district ID and PC ID: perform 'intersect' command to district and PC shapefile, create a new shapefile called Dist_PC_intersect.
  3. Calculate segment area (seg_area): perform 'Calculate Geometry' function on the district shapefile, using the Kalianpur 1975/India Zone lla (EPSG: 24379) coordinate system.
  4. Calculate segment population (seg_ment): perform 'zonal statistics' command using segment shapefile as the 'zone' and the WorldPop2020 as the data raster; derive pc level population by aggregating segment population by PC ID.
  5. Calculate the percentage of district area (Pct_area) by dividing segment area by district area. Remove segments that were less than a hundredth of a percent of the original district area. Those extremely small areas are created because of the slight boundary inaccuracies between district and PC shapefiles. In total, there are 3468 segments.
  6. Calculate segment headcount = district rate * segment population, assuming district prevalence rate unchanged across the whole district
  7. Aggregate segment headcounts by PC ID to get PC level headcount.
  8. Calculate PC level prevalence rate by PC headcount divided by PC population.