Artificial intelligence framework for threat assessment and containment for covid-19 and future epidemics while mitigating the socioeconomic impact to women, children, and underprivileged groups

With the emergency situation that arises with COVID-19, the intense containment strategies adopted by many countries had little or no consideration towards socio-economic ramifications or the impact on women, children, socio­economically underprivileged groups. The existence of many adverse impacts raises questions on the approaches taken and demands proper analysis, scrutiny and review of the policies. Therefore, a framework was developed using the artificial intelligence (Al) techniques to detect, model, and predict the behaviour of the COVID-19 pandemic containment strategies, understanding the socio-economic impact of these strategies on identified diverse vulnerable groups, and the development of AI-based solutions, to predict and manage a future spread of COVID or similar infectious disease outbreaks while mitigating the social and economic toil. Based on generated behaviour and movements, Al tools were developed to conduct contact tracing and socio-economic impact mitigation actions in a more informed, socially conscious and responsible manner in the case of the next wave of COVID-19 infections or a different future infectious disease. © 2022, National Science Foundation. All rights reserved.

iMask: An IoT-based Intelligent Mask to Identify and Track COVID-19 Suspects

COVID-19 has become a global health concern, and wearing masks is a key measure to curb COVID-19 from rapidly spreading. While COVID-19 patients can be accurately determined using Rapid Antigen and PCR tests, these tests are costly, time-consuming, invasive, and uncomfortable. Further, they should be performed in a specialized environment despite showing the COVID-19 symptoms such as fever, cough, rapid heart rate, shortness of breath, and low blood oxygen saturation level. To this end, this study aims to automatically identify, and track the COVID-19 suspects in real-time by embedding smart sensors to face masks. The mask was developed to gather the data related to five major symptoms of COVID-19: body temperature, cough, heart rate, breathing pattern, and blood oxygen level. Data collected using smart sensors were used to identify and track COVID-19 suspects using Deep Neural Networks, the Internet of Things (IoT), and Artificial Intelligence (AI). Yielded results showed the proposed mask can identify COVID-19 suspects 92% accurately. © 2022 IEEE.