Automated artificial intelligence-enabled proactive preparedness real-time system for accurate prediction of COVID-19 infections— Performance evaluation

Abstract

COVID-19 is a contagious disease that has infected over half a billion people worldwide. Due to the rapid spread of the virus, countries are facing challenges to cope with the infection growth. In particular, healthcare organizations face difficulties efficiently provisioning medical staff, equipment, hospital beds, and quarantine centers. Machine and deep learning models have been used to predict infections, but the selection of the model is challenging for a data analyst. This paper proposes an automated Artificial Intelligence-enabled proactive preparedness real-time system that selects a learning model based on the temporal distribution of the evolution of infection. The proposed system integrates a novel methodology in determining the suitable learning model, producing an accurate forecasting algorithm with no human intervention. Numerical experiments and comparative analysis were carried out between our proposed and state-of-the-art approaches. The results show that the proposed system predicts infections with 72.1% less Mean Absolute Percentage Error (MAPE) and 65.2% lower Root Mean Square Error (RMSE) on average than state-of-the-art approaches.

An adaptive data-driven architecture for mental health care applications

“…The strengths and limitations identified in these studies highlight the diverse challenges and factors inherent in health care system architecture, including data interoperability, security, privacy, performance, and consistency ( Ismail et al, 2022 ). These findings provide valuable insights for software architects and practitioners involved in developing resilient and efficient data-driven health care systems.…”

ML-SocMedEmot: Machine Learning Event-based Social Media Emotion Detection Proactive Framework Addressing Mental Health: A Novel Twitter Dataset and Case Study of COVID-19

“…The virus started around the end of December 2019 in Wuhan, China [7], followed by its declaration as a pandemic on March 11, 2020, by the World Health Organization [7]. It is caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-COV-2) and negatively impacts other vital organs such as the brain, heart, liver, pancreas, and kidney [8][9][10]; the virus can also cause stroke [11] and diabetes [12][13][14][15]. COVID-19 infected over half a billion people and led to over 6 million deaths globally [16].…”

ML-NLPEmot: Machine Learning-Natural Language Processing Event-Based Emotion Detection Proactive Framework Addressing Mental Health

“…The virus started around the end of December 2019 in Wuhan, China [7], followed by its declaration as a pandemic on March 11, 2020, by the World Health Organization [7]. It is caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-COV-2) and negatively impacts other vital organs such as the brain, heart, liver, pancreas, and kidney [8]- [10]; the virus can also cause stroke [11] and diabetes [12]- [15]. COVID-19 infected over half a billion people and led to over 6 million deaths globally [16].…”

“…Consequently, countries have imposed precautionary measures to contain it, such as social distancing, travel bans, home confinement, business closures, and vaccination [16]. These preventive actions effectively reduced the number of infections and deaths during the pandemic [17], [18]. However, overly firm restrictions can have a negative impact on the personal level, such as loss of income, anxiety, and depression.…”