(REPOST) US-based startup Mobilewalla, which started as a mobile audience measurement platform, is using cutting-edge data science to manage the coronavirus spread.
Mobilewalla is a next-generation data science startup, which employs Big Data, Artificial Intelligence (AI), and Machine Learning techniques to arrive at granular consumer intelligence.
The micro data sets it offers are integrated by businesses in their operations with the aim of growing customer engagement, ramping up revenues, and improving other business outcomes.
The urgent need for data-tracking solutions in COVID-19 management has suddenly opened up all-new opportunities for players like Mobilewalla, whose primary tech hinges on capturing consumer behaviour on mobile devices. Even though the demand for data is rising globally, the dearth of granular consumer insights in India is even more glaring. Mobilewalla has come up with a fix.
Because the coronavirus spreads via direct (and even indirect) human contact, the potential scale of the spread is enormous. And, in the absence of prevention and cure methods — a vaccine could still be a year away — mitigation is the only way in which the pandemic can be managed at present. The traditional approach to mitigating virus spread is “contact tracing”, which includes tracking movements of infected people to identify who came in contact with them. But the scale of COVID-19 has made it challenging to map the spread accurately.
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Mobilewalla has created AI and machine learning models to predict infection and mortality rates across geographical clusters. The data aids in risk assessment of locations, and helps authorities understand the movement of infected individuals.
Anindya elaborates, “We have built a class of social isolation metrics that captures an individual or an area and whether they are conforming to the distancing rules. There will always be a small number of rule violators, and we help identify those cases. Then, we aggregate the data collected into geo-granules to form area trends. This helps us determine the risk profiles of every population cluster.”
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