Internet of Things Enabled Disease Outbreak Detection: A Predictive Modeling System

                                                              Ehsaneh Khodadadi1, S. K. Towfek *2

1Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, AR 72701, USA.

2 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA

Emails: ekhodada@uark.edu ; sktowfek@jcsis.org

*Corresponding Author: sktowfek@jcsis.orgText Box: Abstract Advancements in data analytics and the proliferation of the Internet of Things (IoT) have opened new frontiers in disease surveillance and early outbreak detection. In this paper, we present a comprehensive framework that integrates IoT-driven predictive data analytics with a secure blockchain network to revolutionize the early warning of disease outbreaks. Our system model comprises edge devices equipped with sensors for data collection and processing, coupled with a blockchain network ensuring data integrity and transparency. Within this framework, we focus on the pivotal role of a Support Vector Machine (SVM) for disease outbreak prediction, showcasing its exceptional accuracy and performance. Through extensive experimentation and comparative analysis, we demonstrate that the SVM, when embedded in our IoT ecosystem, excels in predicting disease outbreaks, outperforming other machine learning models. This approach not only enhances the timeliness and precision of outbreak detection but also facilitates informed decision-making and resource allocation. Furthermore, our system model's integration with blockchain technology ensures the secure storage and validation of prediction results, bolstering the trustworthiness of collected data. This research represents a significant leap forward in proactive disease management and public health, offering a blueprint for future endeavors in epidemiology and healthcare. It underscores the transformative potential of IoT-driven predictive analytics in safeguarding global health and well-being.

Received: April 02, 2023  Revised: June 22, 2023  Accepted: September 16, 2023

Keywords: Internet of Things (IoT);  Disease Outbreak; Predictive Modeling; Epidemiological Surveillance; Sensor Networks; Remote Sensing; IoT Sensors.