The rest are active cases undergoing treatment, as shown in the orange line. For this example, 20% of the cases have recovered, as shown in the green line, while 10% died, as shown in the red line. The proportion of patients in the interior is determined based on this location, as illustrated in Point 2 in Fig. The interior of the ternary diagram signifies states where patients fall under all three categories. For instance, the edge between vertices R and D corresponds to a population where all patients have either recovered or died. The edges of the ternary diagram indicate states of the system where all patients fall into just two of the categories. The vertices A, R, and D correspond to states of the system where all patients are under treatment, recovered, or deceased, respectively. The fractions of A, R, and D in the total infected population can be plotted in a ternary diagram, as shown in Fig. At any given time, the cumulative infected population can be regarded as a mixture of active cases (A), recovered patients (R), and deceased victims (D). Our proposed visualization method is based on a ternary diagram. We discuss insights that can be drawn from the alternative display and then give conclusions and prospects for future work. We then apply this display to COVID-19 data of Spain, Italy, the USA, and the Philippines. The next section describes the logic of this approach. 2004) and biorefinery synthesis (Tay et al. Such diagrams are commonly used in chemical engineering, chemistry, geology, and material science to provide graphical depictions of three-component mixtures for example, notable process engineering applications include property integration (El-Halwagi et al. In this paper, we propose an alternative approach to displaying epidemic data using a ternary diagram. An alternative approach to tracking an outbreak’s progress provides much-needed information to guide decisions on pandemic control measures. For example, patients with life-threatening conditions may be unable to get proper health care due to hospital congestion. Monitoring excess mortality above background levels is also proposed to estimate the accurate scale of the pandemic (e.g., Vandoros 2020) however, analysis of excess mortality is also confounded by deaths resulting indirectly from COVID-19 or the imposed control measures. ( 2020) argue that the cumulative mortality rate relative to the general population should also be tracked during the course of the outbreak. Alternative approaches have thus been proposed to address this flaw. Such a problem is particularly true in the developing world due to resource constraints on testing capacity (Lee 2020). The latter factor puts a natural upper limit to the official number of cases and may thus provide a distorted picture of the extent of the outbreak. In the COVID-19 pandemic case, the use of epidemic curves is hampered by limited testing capacity or compliance.
Relaxation of non-pharmaceutical interventions can be justified by a downward trend in cases, while re-imposition of stringent lockdowns can be done if a renewed surge seems imminent. In the current pandemic, real-time monitoring of case statistics is being used in many countries to guide lockdown measures and adjustment. Epidemic curves that plot the number of cases against time are the most common graphical tools for displaying and analyzing dynamic trends in an outbreak (Mathieu and Sodahlon 2017). These measures have been largely successful in “flattening the curve,” thus preventing healthcare systems from being inundated by COVID-19 patients (Feng et al.
With the limited availability of vaccines and therapeutics, most countries have resorted to implementing non-pharmaceutical interventions (i.e., community quarantines and lockdowns) to stem the spread of the virus. The COVID-19 pandemic has escalated into a major global health crisis that has infected over a hundred million people and claimed the lives of millions of victims to date.
0 kommentar(er)
