Use of AI to assess COVID-19 variant impacts on hospitalization, ICU, and death - Hilal, Waleed; Chislett, Michael G.; Snider, Brett; McBean, Edward A.; Yawney, John; Gadsden, S. Andrew (Frontiers in Artificial Intelligence, 2022)
The rapid spread of COVID-19 and its variants have devastated communities worldwide, and as the highly transmissible Omicron variant becomes the dominant strain of the virus in late 2021, the need to characterize and understand the difference between the new variant and its predecessors has been an increasing priority for public health authorities. Artificial Intelligence has played a significant role in the analysis of various facets COVID-19 since the early stages of the pandemic. This study proposes the use of AI, specifically an XGBoost model, to quantify the impact of various medical risk factors (or “population features”) on the possibility of a patient outcome resulting in hospitalization, ICU admission, or death. The results are compared between the Delta and Omicron COVID-19 variants. Results indicated that older age and an unvaccinated patient status most consistently correspond as the most significant population features contributing to all three scenarios (hospitalization, ICU, death). The top 15 features for each variant-outcome scenario were determined, which most frequently included diabetes, cardiovascular disease, chronic kidney disease, and complications of pneumonia as highly significant population features contributing to serious illness outcomes. The Delta/Hospitalization model returned the highest performance metric scores for area under the receiver operating characteristic (AUROC), F1, and Recall, while Omicron/ICU and Omicron/Hospitalization had the highest accuracy and precision values, respectively. Recall was found to be above 0.60 in most cases (with only two exceptions), indicating that the total number of false positives were generally minimized (accounting for more of the people who would theoretically require medical care).