Metadata about: Containment of Covid-19 requires an extensive testing of the affected population. Some propose global testing to effectively contain Covid-19. Current tests for Covid-19 are administered individually. These tests for Covid-19 are expensive and are limited due to the lack of resources and time. We propose a simple and efficient group testing method for Covid-19. We propose a group testing method where test subjects are grouped and tested. Depending on the result of the group test, subsequent sub groups are formed and tested recursively based on a quartery search algorithm. We designed and built an evaluation model that simulates test subject population, infected test subjects according to available Covid-19 statistics, and the group testing processes in SCALE19. We considered several population models including USA and the world. Our results show that we can significantly reduce the required number of tests up to 89% without sacrificing the accuracy of the individual test of the entire population. For USA, up to 280 million tests can be reduced from the total US population of 331 million and it would be equivalent saving of $28 billion assuming a cost of $100 per test. For the world, 6.96 billion tests can be reduced from the total population of 7.8 billion and it would be equivalent to saving $696 billion. We propose SCALE19 can significantly reduce the total required number of tests compared to individual tests of the entire population. We believe SCALE19 is efficient and simple to be deployed in containment of Covid-19.

About: Containment of Covid-19 requires an extensive testing of the affected population. Some propose global testing to effectively contain Covid-19. Current tests for Covid-19 are administered individually. These tests for Covid-19 are expensive and are limited due to the lack of resources and time. We propose a simple and efficient group testing method for Covid-19. We propose a group testing method where test subjects are grouped and tested. Depending on the result of the group test, subsequent sub groups are formed and tested recursively based on a quartery search algorithm. We designed and built an evaluation model that simulates test subject population, infected test subjects according to available Covid-19 statistics, and the group testing processes in SCALE19. We considered several population models including USA and the world. Our results show that we can significantly reduce the required number of tests up to 89% without sacrificing the accuracy of the individual test of the entire population. For USA, up to 280 million tests can be reduced from the total US population of 331 million and it would be equivalent saving of $28 billion assuming a cost of $100 per test. For the world, 6.96 billion tests can be reduced from the total population of 7.8 billion and it would be equivalent to saving $696 billion. We propose SCALE19 can significantly reduce the total required number of tests compared to individual tests of the entire population. We believe SCALE19 is efficient and simple to be deployed in containment of Covid-19.

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http://ns.inria.fr/covid19/graph/entityfishing	10
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