Mechanistic model for booster doses effectiveness in healthy, cancer and immunosuppressed patients infected with SARS-CoV-2

SARS-CoV-2 vaccines are effective at limiting disease severity, but effectiveness is lower among patients with cancer or immunosuppression. Effectiveness wanes with time and varies by vaccine type. Moreover, vaccines are based on the ancestral SARS-CoV-2 spike-protein that emerging variants may evade. Here, we describe a mechanistic mathematical model for vaccination-induced immunity, validate it with available clinical data, and predict vaccine effectiveness for varied vaccine platforms in the setting of variants with ability to escape immunity, increased virulence, or enhanced transmissibility. We further account for concurrent cancer or underlying immunosuppression. The model confirms enhanced immunogenicity following booster vaccination in immunosuppressed patients but predicts at least one more booster dose is required for these individuals to maintain protection. We further studied the impact of variants on immunosuppressed individuals as a function of the interval between multiple booster doses. Our model is useful for planning future vaccinations, and tailoring strategies to risk groups. Significance StatementCurrent SARS-CoV-2 vaccines are effective at preventing COVID-19 or limiting disease severity in healthy individuals, but effectiveness is lower among patients with cancer or immunosuppression. Here, we address the need for predictions of vaccine effectiveness over time by building on our mathematical framework to account for vaccination-induced immunity. A booster dose of both mRNA vaccines can induce a robust enhancement of both antibody levels and numbers of pertinent types of adaptive immune cells, which is predicted to provide sufficient protection for more than one year in healthy patients. However, our model suggests that for immunosuppressed people or patients with cancer receiving an immunosuppressive treatment, the booster effect may wane, and perhaps could be considered on a more frequent basis.

Comparing Machine Learning Algorithms for Predicting ICU Admission and Mortality in COVID-19

As predicting the trajectory of COVID-19 disease is challenging, machine learning models could assist physicians determine high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) healthcare database, we developed and internally validated models using patients presenting to Emergency Department (ED) between March-April 2020 (n = 1144) and externally validated them using those individuals who encountered ED between May-August 2020 (n = 334). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and procalcitonin levels were important for ICU admission models whereas eGFR <60 ml/min/1.73m2, ventilator use, and potassium levels were the most important variables for predicting mortality. Implementing such models would help in clinical decision-making for future COVID-19 and other infectious disease outbreaks.