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Data Science Approaches to Public Health: Case Studies Using Routine Health Data from India

EasyChair Preprint no. 9596

29 pagesDate: January 20, 2023


The promise of data science for social good has not yet percolated to public health, where the need is most, but lacks priority. The lack of data use policy or culture in Indian health information systems could be one of the reasons for this. Learning from global experiences on how routine health data has been used might benefit us as a newcomer in the field of digital health. The current study aims to demonstrate the potential of data science in transforming publicly available routine health data from India into evidence for public health decision-making. Four case studies were conducted using the expanded data sources to integrate data and link various sources of information. Implementing these data science projects required developing robust algorithms using reproducible research principles to maximize efficiency. They also led to new and incremental challenges that needed to be addressed in novel ways. The paper successfully demonstrates that data science has immense potential for applications in public health. Additionally, the data science approach to public health can ensure transparency and efficiency while also addressing systemic and social issues such as data quality and health equity.

Keyphrases: Data Science, decision making, deep learning, digital health, Health Information Systems, ICT4D, public health

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Arun Mitra and Biju Soman and Rakhal Gaitonde and Tarun Bhatnagar and Engelbert Nieuhas and Sajin Kumar},
  title = {Data Science Approaches to Public Health: Case Studies Using Routine Health Data from India},
  howpublished = {EasyChair Preprint no. 9596},

  year = {EasyChair, 2023}}
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