Innovations in real-time infectious disease surveillance using AI and mobile data


  • Janet Aderonke Olaboye Mediclinic Hospital Pietermaritzburg, South Africa
  • Chukwudi Cosmos Maha Public Health Specialist, Albada General Hospital, Tabuk, Saudi Arabia
  • Tolulope Olagoke Kolawole Independent Researcher, Richmond Virginia, USA
  • Samira Abdul University of North Florida, USA



The integration of artificial intelligence (AI) and mobile health data has ushered in a new era of real-time infectious disease surveillance, offering unprecedented insights into disease dynamics and enabling proactive public health interventions. This paper explores the innovative applications of AI and mobile data in transforming traditional surveillance systems for infectious diseases. By harnessing the power of AI algorithms, coupled with the vast amount of data generated from mobile devices, researchers and public health authorities can now monitor disease outbreaks in real-time with greater accuracy and efficiency. AI-driven predictive models analyze diverse datasets, including demographic information, travel patterns, and social media activity, to detect early signs of disease emergence and predict potential outbreaks. The use of mobile health data provides a wealth of information that was previously inaccessible to traditional surveillance methods. Mobile apps, wearables, and other connected devices enable continuous monitoring of individuals' health indicators, allowing for early detection of symptoms and rapid response to potential threats. Furthermore, geolocation data from mobile devices facilitates the tracking of population movements and the identification of high-risk areas for disease transmission. However, this innovative approach to infectious disease surveillance also presents challenges and ethical considerations. Privacy concerns regarding the collection and use of mobile health data must be carefully addressed to ensure individuals' rights are protected. Additionally, issues related to data quality, interoperability, and algorithm bias need to be mitigated to ensure the reliability and effectiveness of AI-driven surveillance systems. In conclusion, the integration of AI and mobile health data holds immense promise for revolutionizing real-time infectious disease surveillance. By leveraging these technologies, public health authorities can gain valuable insights into disease dynamics, enhance early detection capabilities, and implement targeted interventions to prevent the spread of infectious diseases. However, it is essential to address the challenges and ethical considerations associated with this approach to ensure its responsible and effective implementation.

Keywords:  Innovations, Real-Time Infectious Disease, Surveillance, AI, Mobile Data.