Leveraging Big Data for Disease Surveillance and Public Health Interventions

Big data analytics is playing a new role in approaches to disease surveillance and public health promotion by facilitating the aggregation, combination, and analysis of many different types of information. Explored in this paper is how big data availability can aid the processes of timely identification of disease outbreak incidences, projection models, and subsequent focused public health interventions. Master-level case studies, including The All of Us Research Program based on NIH and Big Data to Knowledge (BD2K), showcase a real-world usage of big data and its health implications. However, the application of big data in this field has some limitations, such as data quality and integration, privacy and ethical sensitives, resource and expertise availabilities, and regulations. These are critical areas that need to be addressed to harness big data analytics in the surveillance of diseases. Accordingly, to enhance the effectiveness of big data applications in public health, it is possible to focus on the strengthening of data integration patterns, maintaining high levels of data protection, and increasing interdisciplinary cooperation. Thus, to foster the application of Big Data in public health, further investment into infrastructural developments, staff training, and ethical guidelines should be made. In this manner, big data can be leveraged significantly in augmenting disease monitoring, identifying the likelihood of disease outbreaks, and informing purposeful public health actions that would prompt positive health outcomes and increase population health emergencies' preparedness for future threats.