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BACKGROUND Serological data on endemic human coronaviruses (HCoVs) and SARS-CoV-2 in southern Africa are scarce. Here, we report on i) endemic HCoV seasonality, ii) SARS-CoV-2 seroprevalence, and iii) predictive factors for SARS-CoV-2 seropositivity and strength of SARS-CoV-2 and HCoV serological response during a 17-month period at the start of the COVID-19 pandemic among adults living with HIV. METHODS Plasma samples were collected from February 2020 to July 2021 within an outpatient HIV cohort in Lesotho. We used the ABCORA multiplex immunoassay to measure antibody responses to endemic HCoV (OC43, HKU1, NL63, and 229E) and SARS-CoV-2 antigens. RESULTS Results of 3'173 samples from 1'403 adults were included. Serological responses against endemic HCoVs increased over time and peaked in winter/spring. SARS-CoV-2 seropositivity reached >35% among samples collected in early 2021 and was associated with female sex (p = 0.004), obesity (p

A research project has been conceptualized to develop and implement an evidence-based toolbox that provides meaningful and hands-on Patient and Public Involvement (PPI) measures for the Swiss HIV Cohort Study (SHCS). The proposed study protocol is for a mapping review that represents the first part of this research project.

Allogeneic haematopoietic cell transplantation (allo-HCT) recipients show impaired antibody (Ab) response to a standard two-dose vaccination against severe acute respiratory syndrome coronavirus-2 and currently a third dose is recommended as part of the primary vaccination regimen. By assessing Ab titres 1 month after a third mRNA vaccine dose in 74 allo-HCT recipients we show sufficient neutralisation activity in 77% of the patients. Discontinuation of immunosuppression before the third vaccine led to serological responses in 50% of low responders to two vaccinations. Identifying factors that might contribute to better vaccine responses in allo-HCT recipients is critical to optimise current vaccination strategies.

BACKGROUND Despite effective prevention approaches, ongoing HIV-1 transmission remains a public health concern indicating a need for identifying its drivers. METHODS We combine a network-based clustering method using evolutionary distances between viral sequences with statistical learning approaches to investigate the dynamics of HIV-1 transmission in the Swiss HIV Cohort Study and to predict the drivers of ongoing transmission. RESULTS We find that only a minority of clusters and patients acquire links to new infections between 2007 and 2020. While the growth of clusters and the probability of individual patients acquiring new links in the transmission network was associated with epidemiological, behavioral and virological predictors, the strength of these associations decreased substantially when adjusting for network characteristics. Thus, these network characteristics can capture major heterogeneities beyond classical epidemiological parameters. When modeling the probability of a newly diagnosed patient being linked with future infections, we found that the best predictive performance (median AUCROC = 0.77) was achieved by models including characteristics of the network as predictors and that models excluding them performed substantially worse (median AUCROC = 0.54). CONCLUSIONS These results highlight the utility of molecular epidemiology-based network approaches for analysing and predicting ongoing HIV-1-transmission dynamics. This approach may serve for real-time prospective assessment of HIV-1-transmission.

We present a parameter estimation method for nonlinear mixed effect models based on ordinary differential equations (NLME-ODEs). The method presented here aims at regularizing the estimation problem in presence of model misspecifications, practical identifiability issues and unknown initial conditions. For doing so, we define our estimator as the minimizer of a cost function which incorporates a possible gap between the assumed model at the population level and the specific individual dynamic. The cost function computation leads to formulate and solve optimal control problems at the subject level. This control theory approach allows to bypass the need to know or estimate initial conditions for each subject and it regularizes the estimation problem in presence of poorly identifiable parameters. Comparing to maximum likelihood, we show on simulation examples that our method improves estimation accuracy in possibly partially observed systems with unknown initial conditions or poorly identifiable parameters with or without model error. We conclude this work with a real application on antibody concentration data after vaccination against Ebola virus coming from phase 1 trials. We use the estimated model discrepancy at the subject level to analyze the presence of model misspecification.

International audience; The 2014-2016 Ebola outbreak in West Africa has triggered accelerated development of several preventive vaccines against Ebola virus. Under the EBOVAC1 consortium, three phase I studies were carried out to assess safety and immunogenicity of a two-dose heterologous vaccination regimen developed by Janssen Vaccines and Prevention in collaboration with Bavarian Nordic. To describe the immune responses induced by the two-dose heterologous vaccine regimen, we propose a mechanistic ODE based model, which takes into account the role of immunological memory. We perform identifiability and sensitivity analysis of the proposed model to establish which kind of biological data are ideally needed in order to accurately estimate parameters, and additionally, which of those are non-identifiable based on the available data. Antibody concentrations data from phase I studies have been used to calibrate the model and show its ability in reproducing the observed antibody dynamics. Together with other factors, the establishment of an effective and reactive immunological memory is of pivotal importance for several prophylactic vaccines. We show that introducing a memory compartment in our calibrated model allows to evaluate the magnitude of the immune response induced by a booster dose and its long-term persistence afterwards.

International audience

Vaccines have been one of the most successful developments in public healthin the last years. However, a major challenge still resides in developing effective vaccinesagainst infectious diseases such as HIV or Ebola virus. This can be attributed to our lack ofdeep knowledge in immunology and the mode of action of immune memory. Mathematicalmodels can help understanding the mechanisms of the immune response, quantifyingthe underlying biological processes and eventually developing vaccines based on a solidrationale. First, we present a mechanistic model for the dynamics of the humoral immuneresponse following Ebola vaccine immunizations based on ordinary differential equations.The parameters of the model are estimated by likelihood maximization in a populationapproach, which allows to quantify the process of the immune response and its factors ofvariability. The vaccine regimen is found to impact only the response on a short term,while significant differences between subjects of different geographic regions are found ata longer term. This could have implications in the design of future clinical trials. Then,we develop a numerical tool based on dynamic programming for optimizing schedule ofrepeated injections. In particular, we focus on HIV-infected patients under treatment butunable to recover their immune system. Repeated injections of an immunotherapeuticproduct (IL-7) are considered for improving the health of these patients. The process isfirst modeled by a piecewise deterministic Markov model and recent results of the impulsecontrol theory allow to solve the problem numerically with an iterative sequence. We showin a proof-of-concept that this method can be applied to a number of pseudo-patients. Alltogether, these results are part of an effort to develop sophisticated methods for analyzingdata from clinical trials to answer concrete clinical questions., Les vaccins ont été une grande réussite en matière de santé publique au coursdes dernières années. Cependant, le développement de vaccins efficaces contre les maladiesinfectieuses telles que le VIH ou le virus Ebola reste un défi majeur. Cela peut être attribuéà notre manque de connaissances approfondies en immunologie et sur le mode d’actionde la mémoire immunitaire. Les modèles mathématiques peuvent aider à comprendre lesmécanismes de la réponse immunitaire, à quantifier les processus biologiques sous-jacentset à déveloper des vaccins fondés sur un rationnel scientifique. Nous présentons un modèlemécaniste de la dynamique de la réponse immunitaire humorale après injection d’un vaccinEbola basé sur des équations différentielles ordinaires. Les paramètres du modèle sontestimés par maximum de vraisemblance dans une approche populationnelle qui permet dequantifier le processus de la réponse immunitaire et ses facteurs de variabilité. Le schémavaccinal n’a d’impact que sur la réponse à court terme, alors que des différences significativesentre des sujets de différentes régions géographiques sont observées à plus long terme.Cela pourrait avoir des implications dans la conception des futurs essais cliniques. Ensuite,nous développons un outil numérique basé sur la programmation dynamique pouroptimiser des schémas d’injections répétées. Nous nous intéressons en particulier à despatients infectés par le VIH sous traitement mais incapables de reconstruire leur systèmeimmunitaire. Des injections répétées d’un produit immunothérapeutique (IL-7) sont envisagéespour améliorer la santé de ces patients. Le processus est modélisé par un modèlede Markov déterministe par morceaux et des résultats récents de la théorie du contrôleimpulsionnel permettent de résoudre le problème numériquement à l’aide d’une suite itérative.Nous montrons dans une preuve de concept que cette méthode peut être appliquéeà un certain nombre de pseudo-patients. Dans l’ensemble, ces résultats s’intègrent dansun effort de développer des méthodes sophistiquées pour analyser les données d’essaiscliniques afin de répondre à des questions cliniques concrètes.

With no available licensed vaccines or therapies, the West African Ebola virus disease epidemic of 2014 to 2016 caused 11,310 deaths. Following this outbreak, the development of vaccines has been accelerated. Combining different vector-based vaccines as heterologous regimens could induce a durable immune response, assessed through antibody concentrations. Based on data from phase 1 trials in East Africa and Europe, the dynamics of the humoral immune response from 7 days after the boost immunization onwards were modeled to estimate the durability of the response and understand its variability. Antibody production is maintained by a population of long-lived cells. Estimation suggests that half of these cells can persist for at least 5 years in humans. Differences in prime-boost vaccine regimens affect only the short-term immune response. Geographical differences in long-lived cell dynamics were inferred, with higher long-term antibody concentrations induced in European participants., The Ebola vaccine based on Ad26.ZEBOV/MVA-BN-Filo prime-boost regimens is being evaluated in multiple clinical trials. The long-term immune response to the vaccine is unknown, including factors associated with the response and variability around the response. We analyzed data from three phase 1 trials performed by the EBOVAC1 Consortium in four countries: the United Kingdom, Kenya, Tanzania, and Uganda. Participants were randomized into four groups based on the interval between prime and boost immunizations (28 or 56 days) and the sequence in which Ad26.ZEBOV and MVA-BN-Filo were administered. Consecutive enzyme-linked immunosorbent assay (ELISA) measurements of the IgG binding antibody concentrations against the Kikwit glycoprotein (GP) were available for 177 participants to assess the humoral immune response up to 1 year postprime. Using a mathematical model for the dynamics of the humoral response, from 7 days after the boost immunization up to 1 year after the prime immunization, we estimated the durability of the antibody response and the influence of different factors on the dynamics of the humoral response. Ordinary differential equations (ODEs) described the dynamics of antibody response and two populations of antibody-secreting cells (ASCs), short-lived (SL) and long-lived (LL). Parameters of the ODEs were estimated using a population approach. We estimated that half of the LL ASCs could persist for at least 5 years. The vaccine regimen significantly affected the SL ASCs and the antibody peak but not the long-term response. The LL ASC compartment dynamics differed significantly by geographic regions analyzed, with a higher long-term antibody persistence in European subjects. These differences could not be explained by the observed differences in cellular immune response. IMPORTANCE With no available licensed vaccines or therapies, the West African Ebola virus disease epidemic of 2014 to 2016 caused 11,310 deaths. Following this outbreak, the development of vaccines has been accelerated. Combining different vector-based vaccines as heterologous regimens could induce a durable immune response, assessed through antibody concentrations. Based on data from phase 1 trials in East Africa and Europe, the dynamics of the humoral immune response from 7 days after the boost immunization onwards were modeled to estimate the durability of the response and understand its variability. Antibody production is maintained by a population of long-lived cells. Estimation suggests that half of these cells can persist for at least 5 years in humans. Differences in prime-boost vaccine regimens affect only the short-term immune response. Geographical differences in long-lived cell dynamics were inferred, with higher long-term antibody concentrations induced in European participants.

Vaccines have been one of the most successful developments in public health in the last years. However, a major challenge still resides in developing effective vaccines against infectious diseases such as HIV or Ebola virus. This can be attributed to our lack of deep knowledge in immunology and the mode of action of immune memory. Mathematical models can help understanding the mechanisms of the immune response, quantifying the underlying biological processes and eventually developing vaccines based on a solid rationale. First, we present a mechanistic model for the dynamics of the humoral immune response following Ebola vaccine immunizations based on ordinary differential equations. The parameters of the model are estimated by likelihood maximization in a population approach, which allows to quantify the process of the immune response and its factors of variability. In particular, the vaccine regimen is found to impact only the response on a short term, while significant differences between subjects of different geographic locations are found at a longer term. This could have implications in the design of future clinical trials. Then, we develop a numerical tool based on dynamic programming for optimizing schedule of repeated injections. In particular, we focus on HIV-infected patients under treatment but unable to recover their immune system. Repeated injections of an immunotherapeutic product (IL-7) are considered for improving the health of these patients. The process is first by a piecewise deterministic Markov model and recent results of the impulse control theory allow to solve the problem numerically with an iterative sequence. We show in a proof-of-concept that this method can be applied to a number of pseudo-patients. All together, these results are part of an effort to develop sophisticated methods for analyzing data from clinical trials to answer concrete clinical questions.; Les vaccins ont été une grande réussite en matière de santé publique au cours des dernières années. Cependant, le développement de vaccins efficaces contre les maladies infectieuses telles que le VIH ou le virus Ebola reste un défi majeur. Cela peut être attribué à notre manque de connaissances approfondies en immunologie et sur le mode d'action de la mémoire immunitaire. Les modèles mathématiques peuvent aider à comprendre les mécanismes de la réponse immunitaire, à quantifier les processus biologiques sous-jacents et à développer des vaccins fondés sur un rationnel scientifique. Nous présentons un modèle mécaniste de la dynamique de la réponse immunitaire humorale après injection d'un vaccin Ebola basé sur des équations différentielles ordinaires. Les paramètres du modèle sont estimés par maximum de vraisemblance dans une approche populationnelle qui permet de quantifier le processus de la réponse immunitaire et ses facteurs de variabilité. En particulier, le schéma vaccinal n'a d'impact que sur la réponse à court terme, alors que des différences significatives entre des sujets de différentes régions géographiques sont observées à plus long terme. Cela pourrait avoir des implications dans la conception des futurs essais cliniques. Ensuite, nous développons un outil numérique basé sur la programmation dynamique pour optimiser des schémas d'injections répétées. En particulier, nous nous intéressons à des patients infectés par le VIH sous traitement mais incapables de reconstruire leur système immunitaire. Des injections répétées d'un produit immunothérapeutique (IL-7) sont envisagées pour améliorer la santé de ces patients. Le processus est modélisé par un modèle de Markov déterministe par morceaux et des résultats récents de la théorie du contrôle impulsionnel permettent de résoudre le problème numériquement à l'aide d'une suite itérative. Nous montrons dans une preuve de concept que cette méthode peut être appliquée à un certain nombre de pseudo-patients. Dans l'ensemble, ces résultats s'intègrent dans un effort de développer des méthodes sophistiquées pour analyser les données d'essais cliniques afin de répondre à des questions cliniques concrètes.