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We are living in a dramatically altered business world today. COVID-19 has taken the world by storm- a global pandemic whose ripples are being felt on all aspects of our lives. With so many dimensions and uncertainties, what appears to be true one day may not be applicable the next. The major challenge -especially for businesses, is to stay on top of the rapidly changing scenarios without people or system constraints.
LTI’s LENI comes with the inherent ability to read critical business disruptions, simulate potential business realities and allow for rapid adjustments as business realities evolve. With a powerful feedback mechanism, Leni also learns and re-learns on the fly, to provide the most accurate and reliable predictions based on real life interventions
LTI is on the course to making data-driven world a reality. The speed and scale at which the Covid-19 virus is spreading is producing massive amount of data across multiple dimensions and the need to tie them together is now more than ever. It is important to stay informed about the rapidly changing global landscape.
With this thought, we created LENI’s COVID -19 webapp that will help you understand the COVID disease metrics on a day to day basis as well as give you a glimpse of the true power of LENI’s AI and ML capabilities
LENI is using the COVID-19 dataset collated by StarSchema from credible sources like JHU and WHO and is hosted on Snowflake Inc
LENI uses “federated” query to connect to COVID-19 dataset hosted on Snowflake Inc. to fetch data on confirmed, active, recovered and deceased cases globally and at a country/region level
LENI's inherent on-the-fly query resolution capability and her ability to apply that to dynamic queries, enables her to bring insightful views from the Covid-19 database that is updated on a daily basis
LENI is also getting trained on epidemiological modeling to provide more accurate predictions on an ongoing basis which would be available soon
Data Dimensions - The data has two main dimensions - Geography ( like countries, states and counties (only for US and UK) and type of cases - confirmed and deceased
Measures - The analysis is being done on the incremental cases ( Change in number of cases compared to previous day) and cumulative cases ( the aggregate number of cases till date)
LENI can easily summarise incremental or cumulative confirmed cases and deaths across time or a certain month and by default is trained to provide the data as per last day refresh
While LENI is not an epidemiologist , she is also getting herself acquainted with the models to provide more accurate predictive insights which will be available soon
You can ask the following sample questions to Leni and start exploring.
There are several limitations to the data set which include underlying assumptions of the John Hopkins University data base available here