###### Abstract
The mDRS played a crucial role in providing health services and support during emergencies, but its capabilities were limited to addressing the aftermath of an outbreak. In contrast, the mPOC system was introduced to take outbreak management and disease prevention to the next level. It extends beyond disaster recovery to provide real-time outbreak prediction, continuous monitoring, and early warning capabilities. By leveraging the Internet of Medical Things (IoMT) and wearable devices, the mPOC system captures user-centric physiological data to gain valuable insights into an individual's health status and response to outbreak situations. The mPOC mechanism is designed to address the need for timely diagnosis and effective management of pandemic-related illnesses on a public health scale. It incorporates two distinctive and collaborative approaches, namely, a bottom-up and top-down approaches. 1. Bottom-up approach: This involved capturing user-centric physiological parameters (that will be discussed in Section 3), which offers valuable insights into an individual's well-being and response to outbreaks. Integrating these parameters into the mPOC system enables a comprehensive understanding of individuals' health status, leading to the evaluation of the Deterioration Risk Index (DRI), which measures users' medical risk levels. Concrete examples of data collection scenarios can facilitate personalized and timely outbreak predictions, thereby optimizing public health responses and ensuring individual well-being during infectious disease outbreaks. A bottom-up approach is used to collect and analyze user-centric data, including psychological factors, to assess and monitor individuals. 2. Top-down approach: Simultaneously, the mPOC's top-down approach involves transmission of information to and from the Outbreak Tracking Centre (OTC) and other public-health-related resources. The top-down approach complements the user-centricdata obtained in the bottom-up approach, providing valuable insights from a broader perspective. Examples of the top-down data collection include:  
Public Health Data--The algorithm receives real-time data on confirmed cases, outbreak trends, and demographic information from health authorities and healthcare institutions. Analyzing this data helps assess outbreak severity, identify high-risk regions, and allocate resources effectively. OTC Information--The mPOC framework establishes a bi-directional connection with the OTC to transmit daily updates on outbreak progression, containment strategies, and healthcare capacities. This enables timely adjustments to predictions and response strategies. Environmental Data--Integrating data from environmental sensors and weather databases provide insights into factors influencing disease transmission. For instance, monitoring air pollution, wind speed, and humidity levels aids in identifying potential hotspots for rapid outbreak spread. Healthcare Infrastructure and Resource Availability--Data on hospital bed capacities and medical supplies contributes to effective outbreak response planning. Assessing available resources helps determine the healthcare system's readiness and facilitates resource allocation. Mobility and Transportation Data--Analyzing data from transportation systems, such as public transit usage and travel patterns, offers insights into population movement and its impact on disease spread. This data supports the optimization of containment measures, such as travel restrictions and quarantine protocols. The mPOC algorithm evaluates the top-down data and calculates the Predictive Exposure Index (PEI). The values of DRI and PEI are then used to assess the current status and expected pandemic-related exposure, which help create appropriate warnings that are triggered using an adaptive threshold-based criteria. These warnings are then sent to the Outbreak Tracking Center (OTC) on a regular basis. This paper provides a detailed analysis of the mechanisms and algorithm employed. To summarize, the main contributions of this paper are as follows:  
1.