Compartmental models are a tool commonly used in epidemiology for the mathematical modelling of the spread of infectious diseases, with their most popular representative being the Susceptible-Infected-Removed (SIR) model and its derivatives. However, current SIR models are bounded in their capabilities to model government policies in the form of non-pharmaceutical interventions (NPIs) and weather effects and offer limited predictive power. More capable alternatives such as agent based models (ABMs) are computationally expensive and require specialized hardware. We introduce a neural network augmented SIR model that can be run on commodity hardware, takes NPIs and weather effects into account and offers improved predictive power as well as counterfactual analysis capabilities. We demonstrate our models improvement of the state-of-the-art modeling COVID-19 in Austria during the 03.2020 to 03.2021 period and provide an outlook for the future up to 01.2024.

Bill Holmes, facilities manager at the Corona, Calif., plant that produces the iconic Fender Stratocaster and Telecaster guitars, remembers all too well walking the factory floor with a crude handheld vibration analyzer and then plugging the device into a computer to get readings on the condition of his equipment. While all of the woodworking was done by hand when Leo Fender founded Fender Musical Instruments Corp. 75 years ago, today the guitar necks and bodies are produced with computer-controller woodworking routers, then handed off to the craftsmen who build the final product. Holmes says he is always looking for the latest technological advances to solve problems (he uses robotics to help paint the guitars), and there's no problem more vexing than equipment breakdowns. Preventive maintenance, where machines get attention on a predetermined schedule, is insufficient, he says. "Ninety percent of breakdowns are instant failures that shut down processes. If you can spot a failure before it happens, you're not shutting down production and the maintenance team isn't running around putting out fires."

Rabat – Moroccan-born professor of computer science at New York University (NYU) Dr. Anasse Bari has designed an artificial intelligence (AI) tool to analyze and curb the evolution of the COVID-19 pandemic. Managing a team of researchers at NYU, Bari helped create and study the efficacy of an AI instrument to predict patients vulnerable to coronavirus and determine the seriousness of COVID-19 infections. "Our goal was to design and deploy a decision-support tool using AI capabilities--mostly predictive analytics--to flag future clinical coronavirus severity," Bari said. "We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds and who can safely go home, with hospital resources stretched thin," the computer scientist added, in light of the fact that hospital resources are limited as the COVID-19 outbreak continues. The Moroccan professor holds a bachelor's degree in Computer Engineering from Al Akhawayn University in Ifrane (AUI), and is establishing negotiations between NYU and AUI to use the newly developed technology in tackling the spread of COVID-19 in Morocco.

This article is part of our ongoing coverage of the fight against coronavirus. As the novel coronavirus (COVID-19) continues to spread across the world, governments and hospitals are being overwhelmed with an influx of patients. Under such circumstances, one of the key challenges they must address is managing their resources and developing care and hospitalization strategies that can prioritize the riskiest patients. This is one area where artificial intelligence can help, experts at Jvion believe. The company, which specializes in clinical AI, is undertaking a data analysis project that will inform COVID-19 readiness strategies and help hospitals take a proactive approach to manage patient populations in the inpatient and outpatient settings.

This article is part of our ongoing coverage of the fight against coronavirus. As the novel coronavirus (COVID-19) continues to spread across the world, governments and hospitals are being overwhelmed with an influx of patients. Under such circumstances, one of the key challenges they must address is managing their resources and developing care and hospitalization strategies that can prioritize the riskiest patients. This is one area where artificial intelligence can help, experts at Jvion believe. The company, which specializes in clinical AI, is undertaking a data analysis project that will inform COVID-19 readiness strategies and help hospitals take a proactive approach to manage patient populations in the inpatient and outpatient settings.