Winners recognized location-based dangers, developed apps to calculate an infection danger, and delivered data-driven suggestions for Los Angeles County’s reopening levels.
California is among the hardest-hit states in the case of coronavirus with more than 200,000 total cases. Knowledge scientists in search of methods to assist the state reopen the financial system participated in a two-week 2020 COVID-19 Computational Challenge (CCC) in mid-June.
The problem was to supply steering for danger mitigation for Los Angeles County. Moreover, the answer “should incorporate the moral safety of particular person information and respect information privateness norms.”
The profitable groups revealed location-based COVID-19 publicity at totally different L.A. communities, developed apps for individuals to calculate their potential for an infection, and delivered relevant data-driven suggestions together with L.A.’s reopening levels, officers stated.
Of the 66 data science groups worldwide (comprising a complete of 405 contestants) which entered, six tasks have been chosen by a panel of judges from the Metropolis of Los Angeles, L.A. County Division of Public Well being, the L.A. Chamber of Commerce, and academia.
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The occasion was co-hosted by the World Affiliation of Analysis Strategies and Knowledge Science (RMDS) Lab, which is able to compile the winners’ findings to deploy a web-based danger evaluation app and an alert system to serve the general public. The event is far wanted because the coronavirus an infection numbers in Los Angeles County and California are skyrocketing this week: As of June 28, L.A. County reported 100,417 confirmed circumstances and three,325 deaths, and California reported 217,000 confirmed circumstances and 5,937 deaths.
The chosen ones
First place: Staff USC-ANRG from the College of Southern California Viterbi Faculty of Engineering—danger estimation utilizing SIR, a simplified color-coded danger degree for every group. The danger rating corresponds to the chance of a wholesome individual changing into contaminated by COVID-19 sooner or later.
Second place: was shared by three groups (challenge names are between parentheses)
Staff DSO from the USC Division of Knowledge Sciences and Operations (“DSO An infection and Danger Scores”), which predicted the place and when the danger of contracting COVID-19 is highest, by predicting the variety of new infections in a particular neighborhood on a given day. Principally, it estimates which space/neighborhood presents the next danger of contracting the coronavirus.
Staff RPI Solver from Rensselaer Polytechnic Institute (“Location-based danger rating for locations within the metropolis of L.A.,” which developed a mathematical mannequin to judge the location-based danger in L.A., with information from SafeGraph and open-data portals, and generated danger scores for 30,864 totally different locations, akin to grocery and clothes shops, gasoline stations, and extra.
Staff Modern Li from Zhejiang College (“Metropolis of L.A. re-open danger analysis”)
assessed the danger index of COVID-19 an infection in L.A. at totally different levels of the reopening course of by constructing a multi-indicator analysis system, in addition to proposed epidemic prevention suggestions for the federal government and communities of L.A.
Finest software: Staff The Padron Peppers from Grinnell School (COVID-19 exercise danger calculator), which studied socioeconomic disparities throughout L.A. County neighborhoods and the way they might have been/be an element within the unfold of the coronavirus based mostly on optimistic check outcomes. It evaluates the danger of leaving dwelling, based mostly on a personal-risk profile, a neighborhood-based danger profile, and exercise based mostly on danger profile.
Rising star in information science: Staff HDMA from the Middle for Human Dynamics within the Cell Age used “inhabitants, case fee, demise fee, and aged inhabitants to find out danger scores of L.A. County neighborhoods” to make a projection for the full deaths and infections for L.A. County within the coming weeks. The danger scores discovered can extrapolate if a neighborhood may have an above or beneath common demise and case complete within the coming weeks.
Assist for the competitors
The CCC was supported by SafeGraph, Snowflake, UCLA Computational Drugs, Esri, Gartner, Mastercard, and the L.A. County Division of Public Well being on open information sources, public well being insurance policies, epidemiology, COVID danger scoring examples, information ethics, in addition to the enterprise perspective on find out how to create and make the most of this danger rating.
Winners acquired money prizes of greater than $3,000, in addition to consideration for internship positions on the Metropolis of Los Angeles, UCLA Computational Drugs, and different associate organizations.
As well as, they are going to have one-on-one mentorship with information executives, a suggestion of a technical report for publishing at Harvard Knowledge Science Evaluation journal, certificates for winners and contestants who make an entire submission, and an invite to current at IM Knowledge 2020.