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Post-Pandemic Situational Monitoring with Big Data

The period of lockdowns around the world is coming to an end. It is inevitable that business and operations need to reopen for economic survival. However, the possibility of new waves of the virus after re-opening is an issue that requires close monitoring.

Balancing Economic Activities and Epidemic Control

The answer to this problem is actively sought out by various government agencies. From gradual re-openings within borders, to the conditional re-openings at borders (such as travel bubbles), these policies and measures are already starting to be implemented in many areas.    

The most important supporting measure after a lockdown – apart from wearing masks, social distancing, and frequent hand-washing – should be epidemic surveillance. This means monitoring those in home quarantines and self-isolation. For example, once a confirmed case is discovered, the history of who they came in contact with needs to be determined in the shortest time possible. Once those potentially infected people are discovered, monitoring them and possibly enforcing home quarantine and self-isolation is a high priority.  

When monitoring is not automated, the staffing required will be massive and it may not be possible to execute quickly. A failure like this could cause a large-scale community-based spread of the virus that cannot be traced, which would then lead to the city needing to return to lockdowns yet again.

Considering automated monitoring, it can be applied via the deployment and use of post-pandemic area management solutions like big data analysis. From monitoring home quarantines and whether the monitoring device of a quarantined person strays from its designated location, to understanding the previous movements of a newly diagnosed person – automated big data analysis can be applied to identify possible connections quickly, accurately, and efficiently.

The monitoring devices used for quarantine and self-isolation are IoT devices that can report back to the big data platform at set times. Once an irregular report from the IoT device is recorded, the big data platform can immediately issue an alarm to notify personnel.

Understanding Contact History with Big Data

To accurately understand the contact history of a newly diagnosed person, data from multiple sources is required. For example, the logs from a telecommunication company’s cell tower, check-in/out times from locations using ID verification, CCTV recordings, credit card spending history, etc. would all be utilized in contact tracing. These data can ultimately be analyzed to find possible contacts with location/time crossovers.

For integrating heterogeneous data from multiple sources, Gorilla’s big data analysis platform provides real-time event notifications and space–time analysis functionality, making it an optimal solution for contact tracing.

Contact us to learn more about post-pandemic area management solutions like our big data platform. If you’d like to read more in-depth on the topic, please check out the whitepaper we recently published here: https://www.gorilla-technology.com/Edge-AI/whitepapers

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