Covid-19 in India


So here's the deal. Covid-19 is here and its going to be a while before the world gets a handle on it. Meanwhile the internet has exploded with innumerable charts, graphs, and models to visualize the virus's spread across the globe. There's also a ton of misinformation and unverified, non-peer reviewed content going viral and raising concerns. Check the Indian Scientists' Response to CoViD-19 website for reliable information and hoax busters in the Indian context.

Mindful of the design principles and disclaimers recommended by the dataviz experts, I'll start this Covid-19 dataviz series with a reminder of the poignancy of the datasets and the real stakes involved.

India had its first reported case from Kerala on the same day that the WHO declared the outbreak a Public Health Emergency of International Concern, on 30th January 2020. The government initiated a slew of measures over the next couple of months, culminating in a country-wide lockdown on the 24th of March. The next few vizzes follow the state-wise scenarios for India, using the metrics of confirmed cases, deaths and recovered patients, updated routinely.

  1. Bar chart race of confirmed cases
  2. Multi-series line chart for disease progression trends
  3. Small multiples stacked chart of active cases, recoveries and deaths
  4. Symptoms dashboard of week-wise Covid-19 symptoms from a patient survey
  5. Packed bubble chart of prevalence of SARS-CoV-2 genomic variants
  6. Sunburst for test positivity in context of symptoms status

The data for the first four posts is sourced from covid19india using their API and tallies with India's MOHFW. The last few vizzes are from data hosted at the Genome Evolution Analysis Resource for COVID-19.

Platforms used include Observable, Flourish, Vega-Lite and Tableau.

A summary chart showing the reported number of cases and recoveries (added and updated in June 2020) is below

These posts track the data reported from all the states of India and are not intended to forecast the pandemic's trends. Developed primarily as a destresser and a way to handle the situation constructively, they help me continue learning the nuances of data visualization. Here are some thoughts to reflect upon before we start off with this series:

-Acknowledgement of the fact that we are in the middle of a lockdown and there is no getting around constantly updating oneself with the rapidly evolving scenario. The numbers look a little less overwhelming if the focus is on learning from them.

-A paucity of visuals in the Indian context. Here's the summary chart in Hindi.

-To be fair, the number of confirmed cases in India have just started hitting the exponential phase in the first week of April, and news agencies as well as sites like covid19india, CovidIndia, ( are on the job collating and presenting the data. A few compilations of relevant visualizations that can help follow the pandemic in India are provided by the IIT Bombay Nutrition Group and the Indian Statistical Institute, Bangalore Centre.

-With the (mostly) well curated data on the pandemic from government and official sources - India's MOHFW - the primary bottleneck in data gathering is eased. Also, with constantly updated graphs from the world's most reliable sources and experts to learn from, focussing on the data viz aspects can be a constructive experience.

-Wherever possible, I highlight caveats such as using only confirmed case numbers, reliability based on number of tests performed, focus on number of deaths without controlling for confounding factors, and others.

-Extrapolating and inferring from plots and building infection models is best left to the experts.

Visualizing data simply  © 2021 by Surabhi Srivastava. All Rights Reserved.
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