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Mobile Location Data Tracking: Helping local authorities to the fight against Coronavirus
Governments and the private sector are increasingly relying on data-driven technologies to avail contain the novel coronavirus, Covid-19. While some optically discern technological solutions as a critical implement for contact tracing, quarantine enforcement, tracking the spread of the virus, and allocating medical resources, these practices raise paramount human rights concerns.
Tracking movement with Mobile Data
Utilizing mobile data, perforation of cell phones, companies' market share, and population, we can infer the physical magnitude of movements over time between geographic areas. Predicated on this data, we utilize network algorithms to define germane key performance indicators (KPIs) by geographic area to better understand the pattern of the spread of the virus according to the flow of people across locations.
These KPIs drive the engendered of a set of interactive visualization dashboards and reports utilizing visual analytics -- which enable the investigation of mobility deportment and how key locations affect the spread of the virus across geographic areas over time.
Utilizing network analytics and the KPIs, we can understand the network topology and how this topology is correlated to the spread of the virus. For example, KPIs can:
- Identify key locations that, according to the flow of people, contribute most to the velocity of the spread of the virus.
- Identify locations that accommodate as gatekeepers -- locations that do not indispensably have a high number of positive cases but accommodate as bridges that spread the virus to other locations by flowing a great number of people across geographic regions.
- Avail in understanding clusters of locations that have a high caliber of interconnectivity with deference to the mobility flow, and how these interconnected flows impact the spread of the virus among even distant geographic areas.
Mobility explorer: Visualizing the spread of COVID-19
SAS® Visual Analytics dashboards and reports provide an interactive view, over time, of the mobility data and the health information, amalgamated to the network KPIs, or the network metrics computed predicated on the mobility flows over time.
Community detection: Identifying hot spots
As we commonly do when applying network analysis for business events, categorically in marketing, we performed community detection to understand the mobility deportment groups’ locations and the flow of people peregrinating between them. And it’s no surprise that most of the communities group together locations which are geographically proximate to each other. That signifies people incline to peregrinate to near locations. Of course, there are people that probably need to commute long distances. But most people endeavor to somehow stay proximate to work, or school, or any paramount community to them. If they must peregrinate perpetually to the same place, it makes all sense to live as proximate as possible to that place. Consequently, predicated on the in- and -outflows of people peregrinating across geographic locations, most communities comprise locations in proximity.
Outbreak prediction with Machine Learning
The network metrics computed to expound the mobility demeanor can be utilized as features to supervised machine learning models. These models can be trained to soothsay locations that will present incipient cases or locations that will present an incrementation in the number of cases.
These features, used to train the machine learning models, are predicated on the network metrics. These network metrics describe how the network evolves over time while convivial containment policies are put in place as the virus spreads. These features ultimately hold a high predictive power as they correlate the mobility deportment to the virus spread. The machine learning models utilize this high predictive power in the features to accurately relegate the targets or the possible locations for incipient outbreaks.
Conclusion:
Local authorities’ entities can utilize the outcomes from mobility tracing and outbreak presage to previse where the virus is emanating from and where it’s going next. Being well apprised about the comportment of the virus spread its prevalent trajectories, and the circumventing geographic locations around the key locations and sultry spots, can avail health agencies make good policy decisions in terms of shelter in place, public conveyance orchestrating, getting medical resources prepared in categorical locations where the mobility tracing forecasts a substantial increase -- or facilitating gregarious distancing restrictions and reopening the economy where the outcomes are not prevising any incrementation in the number of cases.
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