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AI and Machine Learning

大型Data can optimize COVID-19 testing

美国密歇根大学的研究人员正在设计一个基于云的系亚洲必赢统,该系统将分析感染和测试供应链数据,以便更好地组织COVID-19测试的后勤工作。

通过加布樱桃 2020年7月15日
Courtesy: Chris Vavra, CFE Media

The shift in COVID-19 caseloads around the U.S. in recent weeks has scrambled supply and demand in the nation’s testing infrastructure. Even though testing capacity has increased dramatically since spring, the hardest-hit geographical areas are seeing demand outstripping supply, while a few areas are actually reporting excess capacity.

University of Michiganindustrial operations and engineering associate professor Siqian Shen, with support from Microsoft’s AI for Health program, is stepping in to help, designing a cloud-based system that will analyze infection and testing supply chain data to better organize the logistics of testing. It will provide policymakers with data that can help them determine how to distribute tests and site mobile testing centers to make testing as efficient and accessible as possible.

“Without a centralized source of data, it’s very difficult to optimize testing and minimize the spread of COVID.” Shen said. “By pulling in daily updates on infection rates and anonymous demographic data, along with data from the testing supply chain, we’re building a mathematical computer model that will crunch that data and provide visualizations that could help policymakers optimize testing and reduce the spread of the disease.”

Shen said the model will be freely available to U.S. decision makers like federal, state and local governments as well as school districts that are weighing their options for reopening. The research team will build the model during the rest of the summer and aims to have it ready for use in the fall. The nuts and bolts of the model will be freely available, enabling other countries to adapt it for their needs.

“I think this system is likely to be most useful for the U.S., since we’re facing a lack of coordination of testing and disease management. But any country will be able to use the model to manage their testing systems. It may be useful for managing testing and control of other infectious diseases as well,” she said.

该研究小亚洲必赢组计划与网上预约调度系统,使那些寻求测试,很容易地找到他们最近的测试中心,看到在案件在任何一天特定的中心和评估在不同中心的辅助功能选项,进一步扩大了模型的能力。沉设想,该系统的一部分,在2020年年底或2021年初推出在线。

“Different people have different testing needs,” Shen said. “Some may need a facility that’s accessible to the disables, others may be in a high-risk group and want to visit during a time that’s less busy, and still others may be low-income and have limited transportation options. This system can help people meet those needs and also reduce crowding and increase efficiency at testing centers.”

Microsoft’s AI for Health program is supporting the project by providing free access to its Azure computing platform. The team will use freely available public data from the Michigan Institute for Clinical & Health Research (MICHR), as well as US census data and Google Maps data on the locations of testing facilities and testing equipment manufacturing facilities.

– Edited by Chris Vavra, associate editor,必赢亚洲平台, CFE Media and Technology,cvavra@cfemedia.com.


Gabe Cherry
作者简介:加布樱桃,资深作家,密歇根大学