## 6 Novel mHealth Predictive Outbreak for COVID-19 (mPOC) Algorithm and IoMT Protocol
### DRI Construct  
This subsection focuses on the calculation of DRI, and as illustrated in Table 2, it outlines the risk factors involved in determining the DRI value, including age, gender, ethnicity, height/weight, and pre-existing conditions. The relationships among these risk factors are as follows:  
1. Age: Age plays a significant role in the outcomes following viral exposure. Generally, a higher age is associated with increased chances of complications. However, it is important to note that the severity of complications is also influenced by pre-existing conditions, which have notably contributed to higher mortality rates among younger patients.
2. Body Mass Index (BMI): BMI is calculated using weight (W) and height (H) with the formula \(\mathrm{W}/\mathrm{H}^{2}\) (when weight is in kilograms and height is in meters), or BMI = \(703\times\mathrm{W}/\mathrm{H}^{2}\) (when weight is in pounds and height is in inches). Individuals who are classified as obese (BMI > 30) face a higher risk of complications during hospitalization, and this factor is included in the calculation of DRI.
3. Ethnicity: The mortality rate from COVID-19 has an association with patients' ethnic backgrounds, which is largely influenced by factors such as healthcare access, education, income, and social standing. These factors are taken into consideration when calculating DRI.
4. Pre-existing conditions: Based on reports from the World Health Organization (WHO), the increase in mortality rates for individuals with pre-existing health conditions (in addition to the rates for otherwise healthy individuals) are as follows: cardiovascular diseases (10.5%), diabetes (7.3%), pulmonary diseases (6.3%), hypertension (6%), and cancer (5.6%) [44]. These conditions are factored into the calculation of DRI.  
The DRI value is presented as a color-coded indicator on the user's device, as mentioned in Table 3. This value is influenced by various scenarios, including the user's location at home and subsequent movement to other places. Therefore, the calculation of DRI is represented by Equation (1), indicating that the future (recursive) value of DRI is a function of the user's age, gender, BMI value, ethnicity, pre-existing medical conditions, and the current value of DRI.  
\[DRI\ [i+1]=F(age,g,BMI,e,phc,DRI\ [i]) \tag{1}\]  
\(g\): gender, \(e\): ethnicity, \(phc\): pre-existing health conditions.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Location** & **Location** & **PEI** & **DRI** & **PEI** & **Alert** \\
**(Now)** & **(Next)** & **(Now)** & **DRI** & **(Next)** & **(Light-Audio)** \\ \hline Home & Home & \(\downarrow\) & \(\downarrow\) & \(\approx\) & G/S \\ \hline Home & Home & \(\uparrow\) & \(\downarrow\) & \(\approx\) & Y/S \\ \hline Home & Home & \(\downarrow\) & \(\uparrow\) & \(\approx\) & G/S \\ \hline Home & Home & \(\uparrow\) & \(\uparrow\) & \(\uparrow\uparrow\) & A/S \\ \hline Home & LRL & \(\downarrow\) & \(\downarrow\) & \(\approx\) & G/LRA \\ \hline Home & LRL & \(\uparrow\) & \(\downarrow\) & \(\uparrow\uparrow\) & Y/LRA \\ \hline Home & LRL & \(\downarrow\) & \(\uparrow\) & \(\approx\) & Y/LRA \\ \hline \hline \end{tabular}
\end{table}
Table 3: User profile risk assessment criteria.