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

Background: Efficacy of COVID-19 convalescent plasma (CCP) in COVID-19 pneumonia is uncertain. Early transfusion of high antibody titre CCP may be beneficial, especially in case of underlying immunosuppression. Methods: The CORIPLASM study was a multicentric, open-label, Bayesian randomised clinical trial evaluating the efficacy of CCP in patients with moderate COVID-19 pneumonia, including patients with underlying immunosuppression. Patients hospitalised with COVID-19 for less than 9 days were assigned to receive 2 plasma units/day over 2 days (CCP) or usual care (UC) alone. Primary outcomes were the proportion of patients with a WHO-Clinical Progression Score (CPS) >= 6 on the 10-point scale on day 4 and survival without ventilation or additional immunomodulatory treatment by day 14. Main analysis was conducted on the whole population and a planned subgroup analysis was performed according to immunosuppression status. Findings: A total of 120 patients were recruited between April 16, 2020, and April 21, 2021, and assigned to CCP (n=60) or UC (n=60) with a 28 day-follow-up. The median time from symptoms onset to randomisation (days) was 7.0 [interquartile range (IQR) 5.0-9.0] and 7.0 [IQR 4.0-8.5] in CCP and UC, respectively. Thirteen (22%) patients with CCP had a WHO-CPS >= 6 at day 4 versus 8 (13%) with UC, adjusted odds ratio (aOR) 1.88 [95% confidence interval (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 0.10 -1.53]), and 7 (12%) with CCP and 12 (20%) with UC at day 28 (aHR 0.51 [95% CI 0.20-1.32]). Subgroup analysis indicated that CCP might be associated with a lower mortality in patients with underlying immunosuppression (HR 0.37 [95% CI 0.14-0.97]). Serious adverse events were noted in 30 (50%) and 26 (43%) patients with CCP or UC, respectively. Interpretation: CCP treatment did not improve early outcomes in patients with mild-to-moderate form COVID-19 pneumonia but was associated with reduced mortality in the subgroup of immunosuppressed patients. Trial registration: clinicaltrials.gov Identifier: NCT04345991

Course of long COVID symptoms over time in the ComPaRe long COVID prospective e-cohort (preprint)

About 10% of people infected by severe acute respiratory syndrome coronavirus 2 experience post COVID-19 disease. We analysed data from 968 adult patients (5350 person-months) with a confirmed infection enrolled in the ComPaRe long COVID cohort, a disease prevalent prospective e-cohort of such patients in France. Day-by-day prevalence of post COVID-19 symptoms was determined from patients’ responses to the Long COVID Symptom Tool, an online validated self-reported questionnaire assessing 53 post COVID-19 disease symptoms. One year after symptom onset, 84.9% patients still reported their persistence, with a progressively lower prevalence of 27/53 symptoms (e.g., loss of taste/smell); 18/53 symptoms (e.g., dyspnoea) were stable, while the prevalence of 8/53 symptoms (e.g., paraesthesia) had increased. The disease impact on patients’ lives began increasing 6 months after onset, as patients realized they had a chronic disease. Our results should be useful for researchers seeking the potential pathophysiological mechanisms underlying post COVID-19 disease.

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

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 COVID-19 during the First Wave from February to May 2020: a Methodological Review (preprint)

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.

Pandemic Lock-down, Isolation, and Exit Policies Based on Machine Learning Predictions (preprint)

The COVID-19 pandemic highlighted the criticality of research on pandemic management when medical solutions, such as vaccines are not available. We present a framework to combine a standard epidemiological SEIR model (susceptible-exposed-infected-removed) with equally standard machine learning classification models for clinical severity risk, defined by the risk of an individual needing intensive care (ICU) if infected. We then simulate isolation and exit policies using COVID-19 data and estimates for France as of spring 2020. We show that policies considering clinical risk predictions could relax isolation restrictions for millions of the lowest-risk population months faster while abiding to the ICU capacity at all times. Exit policies without risk predictions would exceed the ICU capacity by a multiple, or they should isolate a substantial portion of population for over a year to not overwhelm the medical system. Sensitivity analyses further decompose the impact of various elements of our models on the observed effects.The main implication of our work is that predictive modelling, based on machine learning and artificial intelligence -- arguably, the main innovations of the last few decades, -- could bring significant value to managing pandemics. But for that, governments need to develop policies and invest in infrastructure to operationalize personalized isolation and exit policies based on risk predictions at scale. This involves health data policies to train predictive models and apply them to all residents, policies to support targeted resource allocation to maintain strict isolation for high risk individuals, and the likes.

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

Background: In early May 2020, following social distancing measures due to COVID-19, governments consider relaxing lock-down. We combined individual clinical risk predictions with epidemic modelling to examine simulations of risk based differential isolation and exit policies. Methods: We extended a standard susceptible-exposed-infected-removed (SEIR) model to account for personalised predictions of severity, defined by the risk of an individual needing intensive care if infected, and simulated differential isolation policies using COVID-19 data and estimates in France as of early May 2020. We also performed sensitivity analyses. The framework may be used with other epidemic models, with other risk predictions, and for other epidemic outbreaks. Findings: Simulations indicated that, assuming everything else the same, an exit policy considering clinical risk predictions starting on May 11, as planned by the French government, could enable to immediately relax restrictions for an extra 10% (6 700 000 people) or more of the lowest-risk population, and consequently relax the restrictions on the remaining population significantly faster -- while abiding to the current ICU capacity. Similar exit policies without risk predictions would exceed the ICU capacity by a multiple. Sensitivity analyses showed that when the assumed percentage of severe patients among the population decreased, or the prediction model discrimination improved, or the ICU capacity increased, policies based on risk models had a greater impact on the results of epidemic simulations. At the same time, sensitivity analyses also showed that differential isolation policies require the higher risk individuals to comply with recommended restrictions. In general, our simulations demonstrated that risk prediction models could improve policy effectiveness, keeping everything else constant. Interpretation: Clinical risk prediction models can inform new personalised isolation and exit policies, which may lead to both safer and faster outcomes than what can be achieved without such prediction models.