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.
BT - International Journal of Applied Research in Social Sciences DO - 10.51594/ijarss.v6i7.1334 IS - 7 LA - ENG M3 - Article N2 -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.
PB - Fair East Publishers PY - 2024 SP - 1449 EP - 1460 T2 - International Journal of Applied Research in Social Sciences TI - Big data analytics for epidemic forecasting: Policy Frameworks and technical approaches UR - https://fepbl.com/index.php/ijarss/article/view/1334/1566 VL - 6 SN - 2706-9184, 2706-9176 ER -