Integrating Real-Time Data and Machine Learning in Predicting Infectious Disease Outbreaks: Enhancing Response Strategies in Sub-Saharan Africa
Olabisi Promise Lawal
*
Department of Medical Laboratory Science, University of Benin, Benin City, Nigeria.
Ejikeme Peter Igwe
Department of Applied Biochemistry, Faculty of Natural Sciences, Enugu State University of Science and Technology, Nigeria.
Adedapo Olosunde
Department of Chemistry & Biochemistry, University of Toledo, Toledo, OH 43606, USA.
Ezeamii Patra Chisom
Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, USA.
Debra Ukamaka Okeh
Corona Management Systems, Abuja, Nigeria.
Adepeju Kafayat OLOWOOKERE
University of North Carolina at Greensboro, Department of Nanoscience, United States.
Olufemi Adesola Adedayo
University of Massachusetts, Amherst, School of Natural Sciences, Department of Mathematics and Statistics, United States.
Chiamaka Pamela Agu
Department of Psychiatry, Yale School of Medicine, United States.
Fatimah Adeola Mustapha
Department of Mathematics, Tai Solarin University of Education, Ijagun, Ogun State, Nigeria.
Favour E Odubo
Department of Chemistry and Biochemistry, Baylor University, United States.
Enibokun Theresa Orobator
College of Medicine and Veterinary Medicine, University of Edinburgh, United Kingdom.
*Author to whom correspondence should be addressed.
Abstract
The field of infectious disease prediction and public health response is changing due to the integration of real-time data with machine learning (ML). This paper examines how diverse real-time data types — including mobility patterns, social media activity, wearable sensor data, environmental signals, and electronic health records — can be successfully combined with machine learning approaches to enhance early diagnosis, forecast illness trajectories, and optimize intervention options. The potential of key machine learning models, such as reinforcement learning, deep learning, and supervised learning, to improve forecasting accuracy and facilitate dynamic decision-making is investigated. There is a critical discussion of issues such as algorithmic opacity, privacy problems, data inconsistencies, and a lack of standards. The COVID-19 pandemic case study demonstrates how these tools have already aided in resource allocation and policy planning. A forward-looking outlook on developments in data collecting, explainable Artificial Intelligence, and the necessity of global cooperation is presented in the manuscript's conclusion. When taken as a whole, these elements emphasize how crucial it is to combine technology and international collaboration to fortify public health systems and better prepare for future epidemics. This paper examines how diverse real-time data types — including mobility patterns, social media activity, wearable sensor data, environmental signals, and electronic health records — can be successfully combined with machine learning approaches to enhance early diagnosis, forecast illness trajectories, and optimize intervention options.
Keywords: Global health security, machine learning, predicting infectious disease, COVID-19, pandemic, real-time data