- Introduction
- Media coverage
- Study 1 (May 19 - June 1, 2020)
- Study 2 (July 12 - July 25, 2020)
- Study 3 (May 22 - June 05, 2021)
In this project we evaluated the quality of COVID-19 data reporting across India during three time-periods between 2020 (first wave) and 2021 (second wave). In study 1 and study 2 we assess the quality in the reporting of surveillance data. In study 3 we assess the quality in the reporting of surveillance data, vaccination monitoring data, and vacant bed reporting.
The following are few news articles from India that covered our work.
- English
- IndiaSpend news article by data journalist Rukmini Shrinivasan. The article includes comments from government officials when asked about the findings from our study.
- English
- Regional languages
Transparent and accessible reporting of COVID-19 data is critical for public health efforts. Back in 2020, each Indian state and union territory had its own mechanism for reporting COVID-19 data, and the quality of their reporting had not been systematically evaluated.
Using WHO's recommendations for reporting COVID-19 surveillance data, and the best practices used in data reporting, we developed a framework with 45 indicators to evaluate the "quality of COVID-19 data reporting" done by the state and union territory governments in India. This framework captures four key aspects of public health data reporting โ availability, accessibility, granularity, and privacy.
We evaluated 29 out of 36 states and union territories in India. Our results showed a large disparity in the quality of reporting across India. For more details please read the paper, Disparity in the quality of COVID-19 data reporting across India published in BMC Public Health. We also have a Tableau interactive dashboard summarizing this study.
If you find this framework/study useful, please cite our paper:
@article{vasudevan2021disparity,
title={Disparity in the quality of COVID-19 data reporting across India},
author={Vasudevan, Varun and Gnanasekaran, Abeynaya and Sankar, Varsha and Vasudevan, Siddarth A and Zou, James},
journal={BMC public health},
volume={21},
number={1},
pages={1--12},
year={2021},
publisher={BioMed Central},
doi = {https://doi.org/10.1186/s12889-021-11054-7}
}
Six months into the COVID-19 pandemic, what was the quality of data reporting in India? We answer this question by evaluating the reporting done by 35 out of 36 states and union territories during July 2020. We compare the findings from this assessment with those from the first study. For more details please read the paper, Variation in COVID-19 Data Reporting Across India: 6 Months into the Pandemic published in the Journal of the Indian Institute of Science.
If you find this study useful, please cite our paper:
@article{vasudevan2020variation,
title={Variation in COVID-19 Data Reporting Across India: 6 Months into the Pandemic},
author={Vasudevan, Varun and Gnanasekaran, Abeynaya and Sankar, Varsha and Vasudevan, Siddarth A and Zou, James},
journal={Journal of the Indian Institute of Science},
pages={1--8},
volume={100},
year={2020},
publisher={Springer},
doi = {https://doi.org/10.1007/s41745-020-00188-z}
}
How many men and women have died due to COVID-19 in each state in India? What severe adverse events have been reported following vaccination? Which states are reporting the number of vacant oxygen beds in their hospitals? Such questions have strong public health implications. Is data reporting from the national and subnational governments in India granular enough to answer such questions? We answer that in this paper by documenting and analyzing the reporting of surveillance data, vaccination monitoring data, and bed availability during the second wave of COVID-19.
If you find this study useful, please cite our paper:
@article{vasudevan2021asse,
title={Assessment of COVID-19 data reporting in 100+ websites and apps in India},
author={Vasudevan, Varun and Gnanasekaran, Abeynaya and Bansal, Bhavik and Lahariya, Chandrakant and Parameswaran, Giridara Gopal and Zou, James},
year={2021},
publisher={OSF Preprints},
doi={https://doi.org/10.31219/osf.io/wa3gn}
}