• Muhannad Elhabbal

COVID-19 and Contact Tracing Technology


As time progresses, developments regarding the status of the novel coronavirus fluctuate between rising and declining numbers of cases and mortality rates. Countries and subsidiary organizations work tirelessly to account for these statistics in order to establish more effective, universal preventive measures. For example, the Gulf Cooperation Council (GCC) countries’ efforts have included using individual tracking apps such as TraceCovid. In their fight against the novel coronavirus in a direct methodical way, GCC countries use Contact Tracing apps to track the movements of infected people.

It goes without saying, however, that contact tracing leaves a considerable margin for error in data reporting. Simply put and as recently reported by France’s digital minister in a recent Telegraph article, individuals are not always willing to employ the apps necessary to enable contact tracing to be effective. When a system is lacking to fill in the gaps left over by inconsistent methodology, gathering accurate comprehensive data becomes an impossibility.

As such, forward-leaning countries have been seeking ways to reinforce the data they are gathering through direct contact tracing by implementing an intuitive strategy that is not dependent on individual cooperation.

In general, the rise of the accounting method started at a rudimentary level. For example, in April 2020, Yale University released a report on population dynamic methods as a means for tracing the status of the novel coronavirus. Using a private satellite to track the number of cars parked outside hospitals in Wuhan, China, researchers found that in months as early as July and August of 2019, the volume of cars increased drastically in comparison with the same month a year prior. In addition, this phenomenon coincided with a surge of google searches inquiring after COVID-19-like symptoms. From this information, the Yale researchers determined that the earliest cases of COVID-19 occurred in summer 2019, predating the common view that marks the beginning of the virus in January 2020. Since this report was released, population dynamics have been employed at a more sophisticated level to gauge how the movements of people correlate with the status of COVID-19.

For various applications and using open-source data, Sovereign Intelligence (SI) has been able to accurately track the dynamic movement of populations across specific geographies worldwide. For this problem, it is empowering policy-makers to gauge the effectiveness of COVID-19 control measures by providing critical insight into the epidemiological characteristics of the virus including predictive trends for transmission and population movement. Using this technology, governments can quickly analyze the impact of mitigation solutions and interventions affecting national, state and local populations. SI’s sensemaking artificial intelligence, mapping visualization, and rapid assimilation of relevant datasets (i.e. terabytes of unstructured data) is giving decision-makers the intelligence they need during this crisis.

Looking at the current situation from a global perspective, the grave responsibility shouldered by every country to understand and report their status with regards to the virus necessarily entails a wide and varied approach. By not drawing from as many data resources as possible in order to fully understand the problem, countries put themselves and the international community at risk, by allowing the virus to perpetuate itself. If this pandemic taught us anything, it is that combating uncertainty on a global scale requires innovation and the use of advanced technology to answer key questions.