# A Tri-Model Prediction Approach for COVID-19 ICU Bed Occupancy: A Case Study
## 2 Literature Review  
There is a plethora of studies applying prediction models to various COVID-19 related aspects. As we focus on predicting ICU needs, we review recent research examining factors related to ICU requirements during the pandemic. Figure 2: Impact on model outputs according to covariates from government interventions [24]. In [32], the authors developed a prediction model reporting risk scores for ICU admission and mortality for COVID-19. They applied the TRIPOD guideline for developing a multivariable regression model on 641 hospitalized COVID-19 positive patients. Their model yielded 74% accuracy when predicting ICU admission and 83% accuracy when predicting mortality. In another study, several classification methods were applied to predict level-of-care requirements based on clinical and laboratory data. This information was collected for 2,566 COVID-19 patients and the model resulted in 88% accuracy for hospitalization needs, 87% for ICU care needs, and 86% for mechanical ventilation needs. The authors also produced predictions for Pneumonia severity for ICU care and ventilation with 73% and 74% accuracy, respectively. When predictions were limited to patients with more complex disease, the accuracy of ICU prediction and ventilation was 83% and 82% respectively [9]. A Machine Learning (ML)-based risk prioritization tool was developed to predict imminent (within 24 h) ICU Transfer for Hospitalized COVID-19 patients in [33]. Several time series analyses were used, including vital signs, nursing assessments, laboratory data, and electrocardiograms, as input for training a Random Forest (RF) model. The dataset, that was randomly split into training and test sets using a 70%:30% ratio, consisted of 1987 unique patients who were diagnosed with COVID-19 and admitted to non-ICU hospital units. The research found that the median time to ICU transfer was 2.45 days from the time of admission. Their model performed well compared to actual admissions, with 72.8% sensitivity, 76.3% specificity, 76.2% accuracy, and 79.9% Receiver Operating Characteristic (ROC) Area Under the ROC Curve (AUC). A similar study was conducted by researchers who developed a Deep Learning prediction of likelihood of ICU admission and mortality for COVID-19 patients, using clinical variables. They collected data including demographics, chronic co-morbidities, vital signs, symptoms and laboratory tests at admission. With the aid of a deep neural model, they predicted ICU admission and mortality with an AUC of 0.780 and 0.844 respectively, whilst the corresponding risk scores yielded an AUC of 0.728 and 0.848, respectively [22; 33; 34]. In [28; 35], researchers attempted to detect early predictive factors upon admission to enhance the management of COVID-19 patients hospitalized in ICUs. The study used data from a hospital in Paris, France, and the authors utilized multivariable logistic regression models; models' performances, including discrimination and calibration (C-index, calibration curve, Coefficient of Determination (\(R^{2}\)), Brier score) were evaluated. Their dataset was about 152 patients hospitalized with positive severe COVID-19 symptoms and the probability of ICU transfer or death was found to be 32% at the 14 th day of hospitalization. [PERSON] et al. [33] developed an external validation of a prognostic multivariable model on admission for hospitalized patients with COVID-19. They collected data from 299 patients for a hospital located at point zero of the pandemic, Wuhan, China (internal evaluation) whilst the external validation was conducted using a retrospective cohort from another Wuhan hospital (145 patients). They utilized a multivariable logistic regression model to predict inpatient mortality for COVID-19 positive patients using 9 variables common with acute respiratory symptoms. In this model they included parameters of age, lymphocyte count, lactate dehydrogenase and \(SpO_{2}\) as independent predictors of mortality, and performed very well in both internal (c = 0.89) and external (c = 0.98) validation.