In the Kingdom of Saudi Arabia (KSA), rotavirus universal mass vaccination has been introduced in 2013, however, there is limited information available on the economic benefit and the epidemiological impact of the programme to date. We used a Markov cohort model to evaluate and compare the economic value of rotavirus vaccination with no vaccination in a birth cohort of 562,428 infants. This lifetime analysis considered the societal perspective. Model input was obtained through consensus of local experts after two rounds of evaluation of the proposed estimates. The primary outcome measure was to assess cost-effectiveness and to define the cost-neutrality level reached by comparing vaccination with no vaccination as a function of the price adjustment of the vaccination course. With an assumed vaccine coverage rate of 96%, the already started rotavirus vaccination is expected to reduce the overall burden of rotavirus gastroenteritis by 65% with model exercise, over lifetime. The maximum impact will be seen in rotavirus gastroenteritis-related hospitalizations (93%). Outpatient and emergency visits are shown to decline by 87% each while the occurrence of nosocomial infections, by 78%. Cost neutrality is reached if the vaccine price per course is less than SAR 178.20 at steady state. Rotavirus vaccination should be recommended in KSA given the important clinical impact the vaccine can have and the good value for money it can obtain.

Predicting length of stay (LoS) and understanding its underlying factors is essential to minimizing the risk of hospital-acquired conditions, improving financial, operational, and clinical outcomes, and better managing future pandemics. The purpose of this study was to forecast patients’ LoS using a deep learning model and to analyze cohorts of risk factors reducing or prolonging LoS. We employed various preprocessing techniques, SMOTE-N to balance data, and a TabTransformer model to forecast LoS. Finally, the Apriori algorithm was applied to analyze cohorts of risk factors influencing hospital LoS. The TabTransformer outperformed the base machine learning models in terms of F1 score (0.92), precision (0.83), recall (0.93), and accuracy (0.73) for the discharged dataset and F1 score (0.84), precision (0.75), recall (0.98), and accuracy (0.77) for the deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to laboratory, X-ray, and clinical data, such as elevated LDH and D-dimer levels, lymphocyte count, and comorbidities such as hypertension and diabetes. It also reveals what treatments have reduced the symptoms of COVID-19 patients, leading to a reduction in LoS, particularly when no vaccines or medication, such as Paxlovid, were available.

Predicting Length of Stay (LoS) and understanding its underlying factors is essential to minimize the risk of hospital-acquired conditions, improve financial, operational, and clinical outcomes, and to better manage future pandemics. The purpose of this study is to forecast patients’ LoS using a deep learning model and analyze cohorts of risk factors minimizing or maximizing LoS. We employed various pre-processing techniques, SMOTE-N to balance data, and Tab-Transformer model to forecast LoS. Finally, Apriori algorithm was applied to analyze cohorts of risk factors influencing LoS at hospital. The Tab-Transformer outperformed the base Machine Learning models with an F1-score (.92), precision (.83), recall (.93), and accuracy (.73) for discharge dataset, and F1-score (.84), precision (.75), recall (.98), and accuracy (.77) for deceased dataset. The association mining algorithm was able to identify significant risk factors/indicators belonging to lab, X-Ray, and clinical data such as elevated LDH, and D-Dimer, lymphocytes count, and comorbidities such as hypertension and diabetes responsible for extending patients LoS. It also reveals what treatments has reduced the symptoms of COVID-19 patients leading to reduction in LoS particularly when no vaccines or medication such as Paxlovid were available.