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Overview

From March 25th to May 6th, 2020, over 2000 young innovators from 74 different countries came together to join the fight against COVID-19. In response to the coronavirus outbreak and global shutdown, the New York Academy of Sciences invited creative problem-solvers from around the world to participate in the challenge for a chance to receive a $500 travel scholarship to attend the Global STEM Alliance Summit. The winning solution, GOvid-19, is a virtual assistant and chatbot that provides users with accurate pandemic-related information. Learn more about the winning solution and the solvers who designed them.

The World Health Organization (WHO) declared the outbreak of the coronavirus disease 2019 (COVID-19) a pandemic in March 2020. As scientists and public health experts rush to find solutions to contain the spread, existing and emerging technologies are proving to be valuable. In fact, governments and health care facilities have increasingly turned to technology to help manage the outbreak. The rapid spread of COVID-19 has sparked alarm worldwide. Many countries are grappling with the rise in confirmed cases. It is urgent and crucial for us to discover ways to use technology to contain the outbreak and manage future public health emergencies.

Challenge

Consider the obstacles faced by governments, healthcare providers and/or patients and design a technology-based solution that can be deployed in response to combat COVID-19. The solution can be an improvement of an already existing technology or a new application.  Solutions should consider the following: 

• Modes and rates of disease transmission 
• Known preventative and protective measures against COVID-19
• Lack of vaccine, medication, and treatment for COVID-19
• The public health system, local healthcare infrastructure, access to technology and other relevant contexts

Winners

The winning solution, GOvid-19, is a virtual assistant and chatbot that provides users with accurate pandemic-related information about government responses, emergency resources, statistics on COVID-19 while utilizing grassroots feedback, streamlining medical supply chains with blockchain and AI techniques address potential accessibility issues among the most vulnerable groups.

Overview

From May 8th to June 19th, 2020, over 250 innovators from 21 different countries worked together to develop syndromic surveillance systems that help us better understand the current pandemic and prevent future outbreaks. The New York Academy of Sciences invited solvers from around the world to participate in the challenge for a chance to win a $5,000 USD grand prize. The winning solution, SYNSYS: Tracking COVID-19 created by Esha Datanwala, is a syndromic surveillance system that uses online data to predict outbreaks. Learn more about the winning solution and the solver who designed it.

In the last two decades three new Corinaviruses have jumped from animals to humans – called the spillover effect– causing serious illness and fatalities. Scientists and researchers in various sectors are racing to develop treatments and a vaccine while also investigating fundamental questions about the virus such as the seasonality, full range of symptoms, true fatality rate, viral latency, dose response curve of the viral load, long-term immunity, mutation rate etc.

The lack of Syndromic Surveillance for Coronavirus has grossly exposed the global and local preparedness for pandemics making us vulnerable as well as putting extreme stress on our government, healthcare facilities, medical supply demands and economies.

Challenge

Using available data from the COVID-19 pandemic and/or past outbreaks of SARS and MERS (see below for some suggestions), design an innovative syndromic surveillance system that addresses the need for improved surveillance networks to better understand the threat of future waves of COVID-19 and/or future Coronavirus outbreaks.

Winners

SYNSYS is a syndromic surveillance system designed for the public & private healthcare sectors. This system uses public domain mined data from Google Trends, various social media sites, census data, and satellite data to predict outbreaks, both before they happen and while they’re happening.

Team Member: Esha Datanwala