Efficacy and safety of convalescent plasma to treat hospitalised COVID-19 patients with or without underlying immunodeficiency: a randomized clinical trial

Efficacy of COVID-19 convalescent plasma (CCP) in COVID-19 pneumonia is uncertain. The CORIPLASM study was an open-label, Bayesian randomised clinical trial evaluating the efficacy of CCP in patients with moderate COVID-19, including immunocompromised patients. Patients hospitalised with COVID-19 and less than 9 days since symptoms onset were assigned to receive 4 units of plasma over 2 days ({approx} 840 ml)(CCP) or usual care alone (UC). Primary outcomes were the proportion of patients with a WHO-Clinical Progression Score (CPS) [≥]6 on the 10-point scale on day (d) 4 and survival without ventilation or additional immunomodulatory treatment by d14. A total of 120 patients were recruited and assigned to CCP (n=60) or UC (n=60), including 22 (CCP) and 27 (UC) immunocompromised patients. Thirteen (22%) patients with CCP had a WHO-CPS [≥]6 at d4 versus 8 (13%) with UC, adjusted odds ratio (aOR) 1.88 [95%CI 0.71 to 5.24]. By d14, 19 (31.6%) patients with CCP and 20 (33.3%) patients with UC had ventilation, additional immunomodulatory treatment or had died. Cumulative incidence of death was 3 (5%) with CCP and 8 (13%) with UC at d14 (aHR 0.40 [95%CI 0{middle dot}10 -1{middle dot}53]), and 7 (12%) with CCP and 12 (20%) with UC at d28 (aHR 0.51 [95%CI 0.20-1.32]). Subgroup analysis indicated that CCP might be associated with a lower mortality in immunocompromised patients (HR 0.37 [95%CI 0.14-0.97]). CCP treatment did not improve early outcomes in patients with moderate COVID-19 but was associated with reduced mortality in the subgroup of immunocompromised patients.

Epidemic Models for COVID-19 during the First Wave from February to May 2020: a Methodological Review

We review epidemiological models for the propagation of the COVID-19 pandemic during the early months of the outbreak: from February to May 2020. The aim is to propose a methodological review that highlights the following characteristics: (i) the epidemic propagation models, (ii) the modeling of intervention strategies, (iii) the models and estimation procedures of the epidemic parameters and (iv) the characteristics of the data used. We finally selected 80 articles from open access databases based on criteria such as the theoretical background, the reproducibility, the incorporation of interventions strategies, etc. It mainly resulted to phenomenological, compartmental and individual-level models. A digital companion including an online sheet, a Kibana interface and a markdown document is proposed. Finally, this work provides an opportunity to witness how the scientific community reacted to this unique situation.

Epidemic Models for Personalised COVID-19 Isolation and Exit Policies Using Clinical Risk Predictions

The widespread lockdowns imposed in many countries at the beginning of the COVID-19 pandemic elevated the importance of research on pandemic management when medical solutions such as vaccines are unavailable. We present a framework that combines a standard epidemiological SEIR (susceptible-exposed-infected-removed) model with an equally standard machine learning classification model for clinical severity risk, defined as an individuals risk needing intensive care unit (ICU) treatment if infected. Using COVID-19-related data and estimates for France as of spring 2020, we then simulate isolation and exit policies. Our simulations show that policies considering clinical risk predictions could relax isolation restrictions for millions of the lowest-risk population months earlier while consistently abiding by ICU capacity restrictions. Exit policies without risk predictions, meanwhile, would considerably exceed ICU capacity or require the isolation of a substantial portion of population for over a year in order to not overwhelm the medical system. Sensitivity analyses further decompose the impact of various elements of our models on the observed effects. Our work indicates that predictive modelling based on machine learning and artificial intelligence could bring significant value to managing pandemics. Such a strategy, however, requires governments to develop policies and invest in infrastructure to operationalize personalized isolation and exit policies based on risk predictions at scale. This includes health data policies to train predictive models and apply them to all residents, as well as policies for targeted resource allocation to maintain strict isolation for high-risk individuals.