@article{100632,
  keywords = {Big Data Analytics, Machine learning, Epidemic Forecasting, Public Health Policy, Data Privacy},
  author = { Igwama GT and  Olaboye JA and Maha CC and Ajegbile MD and  Abdul S},
  title = {Big data analytics for epidemic forecasting: Policy Frameworks and technical approaches},
  abstract = {<p>This review paper explores the intersection of big data analytics and epidemic forecasting, highlighting both technical approaches and policy frameworks. It delves into data collection methods from IoT, mobile data, and social media. It discusses analytical techniques such as machine learning and predictive modelling. The paper also addresses the regulatory and ethical considerations necessary for effective data use, emphasizing the need for adaptive policy frameworks to support innovation. The importance of international collaboration and global initiatives for data integration and sharing is underscored. By integrating advanced analytics with robust policies, the potential for enhanced epidemic forecasting and proactive public health responses is significant.&nbsp;</p>
},
  year = {2024},
  journal = {International Journal of Applied Research in Social Sciences},
  volume = {6},
  pages = {1449-1460},
  publisher = {Fair East Publishers},
  issn = {2706-9184, 2706-9176},
  url = {https://fepbl.com/index.php/ijarss/article/view/1334/1566},
  doi = {10.51594/ijarss.v6i7.1334},
  language = {ENG},
}