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Machine Learning Can Identify Areas Most at Risk from Pandemic

#artificialintelligence

Areas most at risk from the COVID-19 pandemic can be identified by a new machine learning tool developed by researchers at startup company Akai Kaeru LLC, which is affiliated with Stony Brook University's Department of Computer Science and the Institute for Advanced Computational Science. The software they use analyzes a massive data set from all 3,007 U.S. counties. The researchers found that combinations of factors such as poverty, rural settings, low education, low poverty but housing debt, and sleep deprivation are associated with higher death rates in counties. The researchers use an automatic pattern mining engine and software to analyze a data set with approximately 500 attributes, which cover details related to demographics, economics, race and ethnicity, and infrastructure in all U.S. counties. After analyzing and assessing the data within counties they created nearly 300 sets of counties at a high risk for COVID-19 and related death rates. Many of these counties within the


New machine learning tool identifies US counties at higher risk for COVID deaths

#artificialintelligence

The task of controlling the COVID-19 pandemic nationwide and predicting where cases will spike next and which areas may have high mortality rates remains daunting for scientists and public officials. A new machine learning tool developed by researchers at a startup company (Akai Kaeru LLC) affiliated with Stony Brook University's Department of Computer Science and the Institute for Advanced Computational Science (IACS) may help gauge areas most at risk for the virus and high death rates. The software they use analyzes a massive data set from all 3,007 U.S. counties. They found that combinations of factors such as poverty, rural settings, low education, low poverty but housing debt, and sleep deprivation are associated with higher death rates in counties. The researchers use an automatic pattern mining engine and software to analyze a data set with approximately 500 attributes, which cover details related to demographics, economics, race and ethnicity, and infrastructure in all U.S. counties.