###### Abstract  
This paper presents the mHealth Predictive Outbreak for COVID-19 (mPOC) framework, an autonomous platform based on wearable Internet of Medical Things (IoMT) devices for outbreak prediction and monitoring. It utilizes real-time physiological and environmental data to assess user risk. The framework incorporates the analysis of psychological and user-centric data, adopting a combination of top-down and bottom-up approaches. The mPOC mechanism utilizes the bidirectional Mobile Health (mHealth) Disaster Recovery System (mDRS) and employs an intelligent algorithm to calculate the Predictive Exposure Index (PEI) and Deterioration Risk Index (DRI). These indices trigger warnings to users based on adaptive threshold criteria and provide updates to the Outbreak Tracking Center (OTC). This paper provides a comprehensive description and analysis of the framework's mechanisms and algorithms, complemented by the performance accuracy evaluation. By leveraging wearable IoMT devices, the mPOC framework showcases its potential in disease prevention and control during pandemics, offering timely alerts and vital information to healthcare professionals and individuals to mitigate outbreaks' impact. Mobile Health (mHealth); COVID-19; Biological Watch (Bio-Watch); mHealth Predictive Outbreak (mPOC); Predictive Exposure Index (PEI); Deterioration Risk Index (DRI); 5 th Generation Mobile Network (5G); Internet of Medical Things (IoMT); Disaster Recovery System (mDRS) +
Footnote †: journal: _Future Internet_  
Things (IoMT) technologies offer a potential solution to address this challenge. These devices, such as smartwatches, wristbands, and other mobile-based gadgets, provide a means for continuous monitoring of individuals' physiological and environmental data. They can capture vital signs, body temperature, respiratory patterns, and other relevant health metrics in real-time, offering a wealth of information for outbreak prediction and monitoring. By leveraging the continuous data collection capabilities of wearable devices, public health authorities and healthcare professionals can gain valuable insights into population health trends and detect early signs of disease outbreaks. This continuous monitoring enables proactive measures, such as implementing targeted interventions, resource allocation, and contact tracing, to prevent further spread of infectious diseases. The critical importance of accurate outbreak predictions cannot be overstated. Early detection allows for timely response measures, including quarantine protocols, travel restrictions, and vaccination campaigns. By deploying an autonomous outbreak prediction and monitoring platform, such as the framework proposed in this paper, public health officials can optimize resource allocation, identify at-risk populations, and implement evidence-based measures to control the spread of infectious diseases. Therefore, the challenges in outbreak management and disease prevention necessitate a paradigm shift towards data-driven approaches that prioritize timely and accurate outbreak predictions. Wearable IoMT technologies play a pivotal role in this endeavor, as they offer real-time data collection and continuous monitoring, enabling public health authorities and healthcare professionals to take proactive measures that reduce the impact of infectious diseases on public health. To that effect, the proposed mPOC: Mobile Health (mHealth) Predictive Outbreak for COVID-19 framework offers a promising solution to address these challenges by leveraging wearable devices and integrating user-centric and public health data to provide an innovative and effective tool for outbreak prediction and monitoring. This paper introduces the mPOC framework, which is an extension to the mDRS (mHealth Disaster Recovery System), previously presented in reference [5]. While the mDRS focused on disaster recovery and post-disaster health management, it primarily addressed immediate response and recovery measures after a disaster or outbreak had already occurred.