Contact Tracking – Business Intelligence & Analytics
Animated charts like the ones popularized by Professor Hans Rosling of Gapminder Foundation[ii] allow us to track the measures over time.Click here to see some innovative Gapminder data visualizations. Figure 2 – Multi-Level Sankey Template Tableau Viz Author: Ken Flerlage Sankey Charts These are easy to read visualizations[iii] of tracking contacts and the spread of the infection. Additional data and data filtering e.g color coded contact type, positive/negative test results will further enhance the utility and readability of the virus spread patterns. Customized solutions and location-specific insights to control the situation Location analytics in combination with geo-spatial big data analytics can help healthcare officials understand why certain procedures work in one region and fail in another. It can also help government bodies to understand various aspects of the outbreak by monitoring and tracking it in real-time. For example, an individual would decide to avoid particular store in his area that has had a large percent of Covid-19 positive people in the recent past, and instead shop somewhere with a lower rate. An example can be, e.g. for New York City, a person could use this information to determine if it would currently be safe to go for a walk or jog in Central Park, in comparison to a Bronx neighborhood. [iv]Enhanced geo-coded data allows users to visually engage with the data and make appropriate decisions. (Click on the map to see current data from New York Times) In Spain, Asistencia-Covid19, which launched first as a pilot in the Community of Madrid (and has now been rolled out by other autonomous communities in Spain), features a questionnaire based on which users can check whether the symptoms they are experiencing – are similar with typical COVID-19 symptoms. Based on this information, the ‘app’ provides recommendations regarding the need to isolate or contact health services. It also allows users to track how their symptoms evolve. Optionally, users can also accept to share their device’s location data with the app, “with the purpose of guaranteeing the quality of the data and its epidemiological analysis,” as they explain on the app’s website. What the app does require users to do – is to fill out a form with personal info, including a contact address where health authorities can reach the person if necessary. This ‘app’ has some similarities with Stop Covid19 Cat, offered in Catalonia. However, the latter requires users to consent to share their geolocation data, which allows authorities to gather information on how the pandemic is spreading throughout the region. In short, the more sensitive the data is from a privacy protection standpoint, the more useful it is from an epidemiological point of view. This implies that citizens may be faced with having to choose between anonymity and convenience, which is something we all have to do in any case on a daily basis when using digital services. Still, there are cultural factors that set apart the choices that individuals in the East and West might make: faced with this dilemma, in the East, the common good prevails over individual rights. Conclusion The role of big data and artificial intelligence is very important in trying to limit the spread of the disease. Aside from the geo-location and mobility information, the millions of smartphones globally can give both historical collection of data and real-time (or almost real-time) insights for analysis and more accurate predictions. Advanced BI algorithms and computational models can be applied to analyze all these mega-sets of gathered data in order to answers the questions, predict different scenarios under given conditions, and come up with best recommendations and instructions. To sum-up – potential BI / analytics application scenarios can include, but are not limited to:
- Providing accurate and detailed time-based historical location information of infected individuals and close circles (potential infections) to establish chains of transmission.
- Rapid assessment of probability of exposure in a given area (or cluster) by cross-matching of smartphone locations of affected and suspected individuals (GPS, or Bluetooth sensors).
- Adding video / CCTV records to identify people in particular infection hotspots.
- Data visualization that offers a lay person view of the pandemic dataset – making decision making much simpler.
Meenakshinathan (Nathan) Padmanabhan is a Practice Lead for Business Intelligence at Visvero. He’s been supporting several clients, across Financial Services, Travel & Hospitality, Utilities, Retail etc. verticals, in helping build effective strategies to build effective data engagement platforms and dashboard applications. Nathan has been leading a team of subject matter and technical experts on a variety of technologies from Microsoft, Qlik, Tableau, Informatica, Hadoop, Azure and AWS platforms.