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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


LI artificial intelligence startup predicts where COVID-19 will spike – IAM Network

#artificialintelligence

A Long Island artificial intelligence startup has built software aimed at pinpointing U.S. counties where the COVID-19 outbreak is likely to be most deadly. In a June report, the data-mining company, Akai Kaeru LLC, forecast spiking COVID-19 mortality with the heaviest concentrations in counties of the Southeast, including Mississippi, Georgia and Louisiana, said co-founder and chief executive Klaus Mueller. Nationwide, the software found 985 out of all 3,007 U.S. counties are at risk. "These patterns identify groups of counties that have a steeper increase in the death-rate trajectory," he said. Closer to home, the software found Nassau and Suffolk counties are likely to be relatively stable, but Westchester and Rockland counties are potential tinderboxes that could tip into crisis, said Mueller, a computer science professor on leave from Stony Brook University.


How L.A. County became coronavirus epicenter: Slower shutdown, density, poverty among theories

Los Angeles Times

In mid-March, as the specter of a society-upending pandemic grew, Los Angeles County emerged as something of a bright spot. When Bay Area counties mandated on March 16 that all residents stay at home, officials said it didn't make sense in L.A. County because far fewer cases of the coronavirus had been detected. "We don't have the same trajectory that they have up north," L.A. County Public Health Director Barbara Ferrer said that day when asked about a stay-at-home order. Two months later, the situation has shifted dramatically. L.A. County now has the highest rate of deaths from COVID-19 in the state, and the second highest infection rate.


Coronavirus is killing more Californians than ever before, and cruel inequities are worsening

Los Angeles Times

California reached another bleak coronavirus milestone this week, recording more than 100 daily deaths in the worst fatality numbers since the pandemic began. But just as troubling, health officials and experts say, is how COVID-19 is stalking certain groups, such as essential workers, and those in institutions including nursing homes and prisons, at much greater rates than those who have the ability to stay home. Californians of color are far more likely to become infected or die from the coronavirus. But the most recent surge in cases is exacerbating those inequities. "The epidemic in the West is particularly among the Latinx community. The virus is spreading through the Latino community as essential workers get sick and spread the illness in their communities, Rutherford and others have noted. The seven-day average for daily coronavirus-related deaths reached 102 on Thursday -- the first time the number went above 100, according to a Los Angeles Times analysis of its ...


Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning

arXiv.org Machine Learning

World Health Organization (WHO) reported that 80% of patients experienced these symptoms mildly. However, older people ( 60 years old) and persons with co-morbid diseases are at a higher risk for severe symptoms and death (Velavan & Meyer, 2020; World Health Organization, 2020). Besides, younger patients with no underlying disease might also experience severe symptoms or even death (Jahromi, Avazpour, et al., 2020; The Washington Post, 2020; Yousefzadegan & Rezaei, 2020). The first positive case of COVID-19 in the United States was reported in the state of Washington on January 20, 2020. By March 17, 2020, Covid-19 has spread across all US states (Centers for Disease Control and Prevention, 2020; Saad B. Omer et al., 2020). Figure 1 shows the aggregated COVID-19 positive case and death count maps for all US states until November 6, 2020. Reports showed that on November 6, 2020, the top states for positive COVID-19 cases are California, Texas, Florida, New York, and Illinois, while the top 5 states for death cases are New York, Texas, California, New Jersey, and Florida.