COVID-19 India Dataset: Parsing COVID-19 Data in Daily Health Bulletins from States in India
This work addresses data accessibility issues for researchers and policymakers in India, though it is incremental as it builds on existing parsing and ML methods.
The paper tackled the problem of inaccessible COVID-19 data in India by automating extraction from unstructured public health bulletins, resulting in a dataset generated using a combination of PDF parsers and machine learning techniques.
While India has been one of the hotspots of COVID-19, data about the pandemic from the country has proved to be largely inaccessible at scale. Much of the data exists in unstructured form on the web, and limited aspects of such data are available through public APIs maintained manually through volunteer effort. This has proved to be difficult both in terms of ease of access to detailed data and with regards to the maintenance of manual data-keeping over time. This paper reports on our effort at automating the extraction of such data from public health bulletins with the help of a combination of classical PDF parsers and state-of-the-art machine learning techniques. In this paper, we will describe the automated data-extraction technique, the nature of the generated data, and exciting avenues of ongoing work.