Utilizing Transfer Learning Approach for Epidemiological Study of Region-Specific Post-Acute Sequelae of SARS-COV-2

Abstract

Post-Acute Sequelae of SARS-CoV-2 (PASC) is posing an extreme risk environment with severe consequences, especially for middle-aged and older adults and those with chronic health conditions such as cardiovascular diseases, cancers, respiratory illnesses, and diabetes. Physical disorders include severe and persistent body pains, fatigue, and difficulty with body movements. Similarly, mood disorders such as depression, mood swings, feelings of hopelessness, and difficulty concentrating are significant predictors of PASC. Individuals, predominantly middle-aged and elderly people, are facing increased physical and mental consequences, including frequent hospitalizations, medically unstable conditions, and in some cases, unexpected fatalities. Timely identification of PASC is essential to mitigating the severity of associated health issues. This research recommends a latent transfer model to integrate patient data from different regions, to extract insights into the data, and to deliver personalized healthcare solutions. This study features the potential of the latent transfer learning model to improve data simplification, generalization, personalization, insight detection, variability, scalability, and adaptability to improve public health outcomes.