How Artificial Intelligence helped to save a whole country from COVID19

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The founder of AgentRisk.com and scientists from USC and Wharton, team up for an AI-based project that combines real-time testing data with information from tourists visiting Greece to build a risk profile, helping officials make decisions about who to test for COVID-19.

Jon Vlachogiannis

This is a break-through in algorithmic governance, Jon Vlachogiannis said. Having algorithms “managing” a country is scary but important in hectic times.

Jon Vlachogiannis, Entrepreneur and founder of AgentRisk - a machine learning wealth management Startup - along with two faculty members at the University of Southern California (USC) and a Senior Fellow and Wharton School Assistant Professor, have created a platform that algorithmically automates the process of vetting the COVID-19 risk of incoming tourists through all ports of entry in Greece.

Their new country-wide AI screening and testing program — thought to be the first of its kind in the world — is called EVA and began operating beginning of July.

EVA combines the vast experience of Jon Vlachogiannis in Big Data analytics and Machine Learning (BugSense, Splunk, AgentRisk) with machine learning experts from Penn’s Wharton School and the USC’s Marshall School of Business in a project that rethinks how a country collects, distributes and uses, coronavirus testing data throughout its border security system.

33 million tourists
A country with a population of 11 million, Greece in normal years hosts more than 33 million tourists — an industry that supports nearly a quarter of the country’s jobs. That tourism sector, which accounts for 20.6% of the nation’s GDP, tanked when the pandemic hit. But the government’s overall anti-pandemic measures resulted in the lowest infection and death rates in Europe and it began reopening the country’s border in mid June.
Earlier, in May, it also began planning border operations aimed at both welcoming and more effectively screening and testing returning tourists.

The task of designing and building a new online decision-support system for border vetting was given to Jon Vlachogiannis, Hamsa Bastani, PhD at Wharton, Kimon Drakopoulos, PhD, and Vishal Gupta, PhD, both Assistant Professors of Data Science and Operations at USC.

Core of the new AI System: local testing results
Since the earliest days of the pandemic, Greece has been testing incoming visitors for the coronavirus; that data collection continues to grow as the core of the new AI system that predicts risk of infection among incoming visitors.
Here’s how it works: visitors preparing to depart their countries for Greece must fill out an online “passenger locator form” providing basic information including what country they are coming from, gender, age, and other details. When the traveler arrives at a Greek border, they’re issued a QR code from Eva that is scanned by security. It indicates if they should pass on into the country or get a COVID test. If the test results are negative, they continue on their journey; if positive, they must quarantine themselves for 14 days.

A key element of the AI risk assessment process is Eva’s access to de-identified results from all coronavirus tests performed in Greece. This allows the system to match an incoming person against the known prevalence of infection among tourists from the same country or region. Eva exclusively uses Greek test data rather than the COVID-19 data publicly reported by the tourists’ own countries.

With the self-learning algorithms of EVA, Greece is possible to accept more than 50k to 100k tourists per day with less than 20 cases attributed to external infections. This is a break-through in algorithmic governance, Jon Vlachogiannis said.
Having algorithms “managing” a country is scary but important in hectic times. It is equally important to always have human supervision by scientists and law makers.

AgentRisk builds investment products and tools for individuals as well as for financial advisors and wealth managers, leveraging state-of-the-art machine learning algorithms and time-tested investment theories.

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