Multiple sclerosis and the risk of infection: a consensus guideline

Multiple sclerosis (MS) is an inflammatory condition affecting the central nervous system. Infection is a major consideration in the MS population due to its relevance to several stages of the disease process:(i) it has been suggested that infective processes may be ‘triggering’ or aetiological factors for MS, (ii) concurrent infection is known to exacerbate symptoms in MS, (iii) people with MS are at higher risk of infection when compared to the general population, and this risk is exaggerated in those receiving disease modifying therapies (DMTs).This guidance document was developed by specialists in the field of MS, Immunology, Infectious Disease and Pharmacy. A modified Delphi approach was used to develop clinically relevant, evidence-based consensus guidelines to help physicians navigate the complex interaction between DMTs and infectious diseases.We focus on specific risks predisposing people with MS to infection and how to manage these risks. We also provide recommendations on how to screen for, prevent, and manage infection in this population, in particular tuberculosis, progressive multifocal leukoencephalopathy, hepatitis B, human papillomavirus, herpetic and other opportunistic infections. We also discuss vaccination and the COVID-19 pandemic in people on DMTs.

Response to COVID-19 booster vaccinations in seronegative people with multiple sclerosis.

BACKGROUND: People with MS treated with anti-CD20 therapies and fingolimod often have attenuated responses to initial COVID-19 vaccination. However, uncertainties remain about the benefit of a 3rd (booster) COVID-19 vaccine in this group. METHODS: PwMS without a detectable IgG response following COVID-19 vaccines 1&2 were invited to participate. Participants provided a dried blood spot +/- venous blood sample 2-12 weeks following COVID-19 vaccine 3. Humoral and T cell responses to SARS-CoV-2 spike protein and nucleocapsid antigen were measured. RESULTS: Of 81 participants, 79 provided a dried blood spot sample, of whom 38 also provided a whole blood sample; 2 provided only whole blood. Anti-SARS-CoV-2-spike IgG seroconversion post-COVID-19 vaccine 3 occurred in 26/79 (33%) participants; 26/40 (65%) had positive T-cell responses. Overall, 31/40 (78%) demonstrated either humoral or cellular immune response post-COVID-19 vaccine 3. There was no association between laboratory evidence of prior COVID-19 and seroconversion following vaccine 3. CONCLUSIONS: Approximately one third of pwMS who were seronegative after initial COVID-19 vaccination seroconverted after booster (third) vaccination, supporting the use of boosters in this group. Almost 8 out of 10 had a measurable immune response following 3rd COVID-19 vaccine.

COVID-19 Vaccine Response in People with Multiple Sclerosis.

OBJECTIVE: The purpose of this study was to investigate the effect of disease modifying therapies on immune response to severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) vaccines in people with multiple sclerosis (MS). METHODS: Four hundred seventy-three people with MS provided one or more dried blood spot samples. Information about coronavirus disease 2019 (COVID-19) and vaccine history, medical, and drug history were extracted from questionnaires and medical records. Dried blood spots were eluted and tested for antibodies to SARS-CoV-2. Antibody titers were partitioned into tertiles with people on no disease modifying therapy as a reference. We calculated the odds ratio of seroconversion (univariate logistic regression) and compared quantitative vaccine response (Kruskal Wallis) following the SARS-CoV-2 vaccine according to disease modifying therapy. We used regression modeling to explore the effect of vaccine timing, treatment duration, age, vaccine type, and lymphocyte count on vaccine response. RESULTS: Compared to no disease modifying therapy, the use of anti-CD20 monoclonal antibodies (odds ratio = 0.03, 95% confidence interval [CI] = 0.01-0.06, p < 0.001) and fingolimod (odds ratio = 0.04; 95% CI = 0.01-0.12) were associated with lower seroconversion following the SARS-CoV-2 vaccine. All other drugs did not differ significantly from the untreated cohort. Both time since last anti-CD20 treatment and total time on treatment were significantly associated with the response to the vaccination. The vaccine type significantly predicted seroconversion, but not in those on anti-CD20 medications. Preliminary data on cellular T-cell immunity showed 40% of seronegative subjects had measurable anti-SARS-CoV-2 T cell responses. INTERPRETATION: Some disease modifying therapies convey risk of attenuated serological response to SARS-CoV-2 vaccination in people with MS. We provide recommendations for the practical management of this patient group. ANN NEUROL 20219999:n/a-n/a.

COVID-19: Mechanistic model calibration subject to active and varying non-pharmaceutical interventions.

Mathematical models are useful in epidemiology to understand COVID-19 contagion dynamics. We aim to demonstrate the effectiveness of parameter regression methods to calibrate an established epidemiological model describing infection rates subject to active, varying non-pharmaceutical interventions (NPIs). We assess the potential of established chemical engineering modelling principles and practice applied to epidemiological systems. We exploit the sophisticated parameter regression functionality of a commercial chemical engineering simulator with piecewise continuous integration, event and discontinuity management. We develop a strategy for calibrating and validating a model. Our results using historic data from 4 countries provide insights into on-going disease suppression measures, while visualisation of reported data provides up-to-date condition monitoring of the pandemic status. The effective reproduction number response to NPIs is non-linear with variable response rate, magnitude and direction. Our purpose is developing a methodology without presenting a fully optimised model, or attempting to predict future course of the COVID-19 pandemic.

Insights into the dynamics and control of COVID-19 infection rates.

This work aims to model, simulate and provide insights into the dynamics and control of COVID-19 infection rates. Using an established epidemiological model augmented with a time-varying disease transmission rate allows daily model calibration using COVID-19 case data from countries around the world. This hybrid model provides predictive forecasts of the cumulative number of infected cases. It also reveals the dynamics associated with disease suppression, demonstrating the time to reduce the effective, time-dependent, reproduction number. Model simulations provide insights into the outcomes of disease suppression measures and the predicted duration of the pandemic. Visualisation of reported data provides up-to-date condition monitoring, while daily model calibration allows for a continued and updated forecast of the current state of the pandemic.