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AI-defined COVID-19 testing strategy could lead to fewer infections
A new algorithm developed by researchers at the College of Information Sciences and Technology could help leaders of governments and organizations make better informed decisions on how many symptomatic and asymptomatic individuals to test for COVID-19 with a limited supply of daily tests, and at what stage of the pandemic.
Image: Adobe Stock: Giovanni Cancemi
When the novel coronavirus pandemic spread across the globe, governments and institutions worldwide faced hard decisions about who to test for the virus – and when – with limited testing supplies.
Now, a new algorithm developed by researchers at Penn State’s College of Information Sciences and Technology could help leaders make better informed decisions on how many symptomatic and asymptomatic individuals to test with rationed daily tests, and at what stage of the pandemic. The model’s simulated testing strategies resulte ....

Lyu Liang , Amulya Yadav , Penn State College Of Information Sciences , Technologies Career Development Assistant Professor , International Conference On Autonomous Agents , Penn State , Information Sciences , Career Development Assistant Professor , Partially Observable Markov Decision Processes , International Conference , Autonomous Agents , Multiagent Systems , யூ லியாங் , அமுல்யா யாதவ் , பென் நிலை கல்லூரி ஆஃப் தகவல் அறிவியல் , தொழில்நுட்பங்கள் தொழில் வளர்ச்சி உதவியாளர் ப்ரொஃபெஸர் , பென் நிலை , தகவல் அறிவியல் , தொழில் வளர்ச்சி உதவியாளர் ப்ரொஃபெஸர் , சர்வதேச மாநாடு ,