Big Data Analytics in Epidemiology

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@article{1706330,
      author = {Joseph Jeremiah Adekunle, Dhikrahllah Ayanfe Abdulwahab, Adam Kunle Lawal, Esther Ajiboye, Pwanogoshi Keren Makanto},
      title = {Big Data Analytics in Epidemiology},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {3},
      pages = {531-541},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706330.pdf},
      abstract = {Big data analytics has emerged as a transformative tool in epidemiology, revolutionizing how public health professionals monitor, predict, and manage disease outbreaks. This article explores the application of big data analytics in epidemiology, highlighting how diverse datasets?such as electronic health records (EHRs) and genomic information to social media and environmental data?are utilized to gain actionable insights into disease dynamics. The integration of advanced analytical techniques, such as machine learning, natural language processing, and geospatial analysis, enables more accurate and timely surveillance, improved predictive modeling, and optimized public health interventions Despite its potential, the implementation of big data analytics in epidemiology faces significant challenges, including concerns over data privacy, issues with data quality and integration, and the need for advanced technical infrastructure. By examining case studies such as the COVID-19 pandemic and Ebola outbreaks, this article highlights the benefits, limitations, and future directions of big data in enhancing disease prevention and public health strategies. The discussion aims to provide a comprehensive overview of how big data analytics is reshaping epidemiological practices, offering new opportunities for precision public health and improved disease management.},
      keywords = {Big Data, Epidemiology, Pandemic, Machine learning, Natural Language processing.},
      month = {September},
  }