Science

Indian youth wins NYAS challenge, builds predictive system for pandemics

PT Jyothi Datta Mumbai | Updated on September 18, 2020 Published on September 18, 2020

SYNSYS: Tracking COVID-19, created by Esha Datanwala, is a syndromic surveillance system that uses online data to predict outbreaks   -  istock/Tzido

An interest in science may just end up becoming a career choice for 21-year-old Esha Datanwala, having recently won the New York Academy of Sciences’ ‘Tracking Coronavirus Innovation Challenge’.

“The winning solution, SYNSYS: Tracking COVID-19 created by Esha Datanwala, is a syndromic surveillance system that uses online data to predict outbreaks,” the NYAS said of the achievement that comes with $5000 purse. SYNSYS is a syndromic surveillance system designed for public and private healthcare sectors and it uses public data from Google Trends, various social media sites, census data, and satellite data to predict outbreaks, before they happen and while they’re happening, the note explains.

A student of English and Media Studies, having graduated from the Ashoka University, Esha says, she approached the challenge to develop a “universal” solution that could be used for other viruses as well. She looked at behavioural patterns of sick people and found Google trends bearing out her hypothesis, as she mapped trends from India, the UK, the US and Italy. The focus was on four keywords, illnesses in this case — fever, pneumonia, cough and flu. With her concept getting established, she fined-tuned it and made her pitch. The 10 finalists at the NYAS challenge had two participants from India, she said, the other being a team from IIT-Madras.

Taking forward

Esha does not intend to stop here and is scouting for a programmer to take the concept towards a workable solution that can be put to use in public health, she says.

With a scalable model, the system has two components - a pre-emptive approach and the combatant system. While the first looks at mapping trends from illness clusters and consumption patterns, the second part looks at inputs like population density data from satellites, for example. Such information can help identify high-risk zones and deploy resources efficiently to tackle the virus in those zones, she says. This could be developed further, to tap into private data from hospitals and pharmaceutical companies, says Esha, still excited about the win, but also planning ahead to engage with programmers and healthcare companies to take ahead her predictive solution.

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Published on September 18, 2020
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