Modeling the waning and boosting of immunity from infection or vaccination

Immunity following natural infection or immunization may wane, increasing susceptibility to infection with time since infection or vaccination. Symptoms, and concomitantly infectiousness, depend on residual immunity. We quantify these phenomena in a model population composed of individuals whose susceptibility, infectiousness, and symptoms all vary with immune status. We also model age, which affects contact, vaccination and possibly waning rates. The resurgences of pertussis that have been observed wherever effective vaccination programs have reduced typical disease among young children follow from these processes. As one example, we compare simulations with the experience of Sweden following resumption of pertussis vaccination after the hiatus from 1979 to 1996, reproducing the observations leading health authorities to introduce booster doses among school-aged children and adolescents in 2007 and 2014, respectively. Because pertussis comprises a spectrum of symptoms, only the most severe of which are medically attended, accurate models are needed to design optimal vaccination programs where surveillance is less effective. (C) 2020 The Authors. Published by Elsevier Ltd

Seasonality as a driver of pH1N12009 influenza vaccination campaign impact

Although the most recent respiratory virus pandemic was triggered by a Coronavirus, sustained and elevated prevalence of highly pathogenic avian influenza viruses able to infect mammalian hosts highlights the continued threat of pandemics of influenza A virus (IAV) to global health. Retrospective analysis of pandemic outcomes, including comparative investigation of intervention efficacy in different regions, provide important contributions to the evidence base for future pandemic planning. The swine-origin IAV pandemic of 2009 exhibited regional variation in onset, infection dynamics and annual infection attack rates (IARs). For example, the UK experienced three severe peaks of infection over two influenza seasons, whilst Australia experienced a single severe wave. We adopt a seasonally forced 2-subtype model for the transmission of pH1N12009 and seasonal H3N2 to examine the role vaccination campaigns may play in explaining differences in pandemic trajectories in temperate regions. Our model differentiates between the nature of vaccine- and infection-acquired immunity. In particular, we assume that immunity triggered by infection elicits heterologous cross-protection against viral shedding in addition to long-lasting neutralising antibody, whereas vaccination induces imperfect reduction in susceptibility. We employ an Approximate Bayesian Computation (ABC) framework to calibrate the model using data for pH1N12009 seroprevalence, relative subtype dominance, and annual IARs for Australia and the UK. Heterologous cross-protection substantially suppressed the pandemic IAR over the posterior, with the strength of protection against onward transmission inversely correlated with the initial reproduction number. We show that IAV pandemic timing relative to the usual seasonal influenza cycle influenced the size of the initial waves of pH1N12009 in temperate regions and the impact of vaccination campaigns

The effect of time since measles vaccination and age at first dose on measles vaccine effectiveness - A systematic review.

BACKGROUND: In settings where measles has been eliminated, vaccine-derived immunity may in theory wane more rapidly due to a lack of immune boosting by circulating measles virus. We aimed to assess whether measles vaccine effectiveness (VE) waned over time, and if so, whether differentially in measles-eliminated and measles-endemic settings. METHODS: We performed a systematic literature review of studies that reported VE and time since vaccination with measles-containing vaccine (MCV). We extracted information on case definition (clinical symptoms and/or laboratory diagnosis), method of vaccination status ascertainment (medical record or vaccine registry), as well as any biases which may have arisen from cold chain issues and a lack of an age at first dose of MCV. We then used linear regression to evaluate VE as a function of age at first dose of MCV and time since MCV. RESULTS: After screening 14,782 citations, we identified three full-text articles from measles-eliminated settings and 33 articles from measles-endemic settings. In elimination settings, two-dose VE estimates increased as age at first dose of MCV increased and decreased as time since MCV increased; however, the small number of studies available limited interpretation. In measles-endemic settings, one-dose VE increased by 1.5% (95% CI 0.5, 2.5) for every month increase in age at first dose of MCV. We found no evidence of waning VE in endemic settings. CONCLUSIONS: The paucity of data from measles-eliminated settings indicates that additional studies and approaches (such as studies using proxies including laboratory correlates of protection) are needed to answer the question of whether VE in measles-eliminated settings wanes. Age at first dose of MCV was the most important factor in determining VE. More VE studies need to be conducted in elimination settings, and standards should be developed for information collected and reported in such studies

Influenza Pandemic Waves under Various Mitigation Strategies with 2009 H1N1 as a Case Study

A significant feature of influenza pandemics is multiple waves of morbidity and mortality over a few months or years. The size of these successive waves depends on intervention strategies including antivirals and vaccination, as well as the effects of immunity gained from previous infection. However, the global vaccine manufacturing capacity is limited. Also, antiviral stockpiles are costly and thus, are limited to very few countries. The combined effect of antivirals and vaccination in successive waves of a pandemic has not been quantified. The effect of acquired immunity from vaccination and previous infection has also not been characterized. In times of a pandemic threat countries must consider the effects of a limited vaccine, limited antiviral use and the effects of prior immunity so as to adopt a pandemic strategy that will best aid the population. We developed a mathematical model describing the first and second waves of an influenza pandemic including drug therapy, vaccination and acquired immunity. The first wave model includes the use of antiviral drugs under different treatment profiles. In the second wave model the effects of antivirals, vaccination and immunity gained from the first wave are considered. The models are used to characterize the severity of infection in a population under different drug therapy and vaccination strategies, as well as school closure, so that public health policies regarding future influenza pandemics are better informed

Modelling cross-reactivity and memory in the cellular adaptive immune response to influenza infection in the host

The cellular adaptive immune response plays a key role in resolving influenza infection. Experiments where individuals are successively infected with different strains within a short timeframe provide insight into the underlying viral dynamics and the role of a cross-reactive immune response in resolving an acute infection. We construct a mathematical model of within-host influenza viral dynamics including three possible factors which determine the strength of the cross-reactive cellular adaptive immune response: the initial naive T cell number, the avidity of the interaction between T cells and the epitopes presented by infected cells, and the epitope abundance per infected cell. Our model explains the experimentally observed shortening of a second infection when cross-reactivity is present, and shows that memory in the cellular adaptive immune response is necessary to protect against a second infection.Comment: 35 pages, 12 figure

Systematic, comprehensive, evidence-based approach to identify neuroprotective interventions for motor neuron disease: using systematic reviews to inform expert consensus

Objectives: Motor neuron disease (MND) is an incurable progressive neurodegenerative disease with limited treatment options. There is a pressing need for innovation in identifying therapies to take to clinical trial. Here, we detail a systematic and structured evidence-based approach to inform consensus decision making to select the first two drugs for evaluation in Motor Neuron Disease-Systematic Multi-arm Adaptive Randomised Trial (MND-SMART: NCT04302870), an adaptive platform trial. We aim to identify and prioritise candidate drugs which have the best available evidence for efficacy, acceptable safety profiles and are feasible for evaluation within the trial protocol. Methods: We conducted a two-stage systematic review to identify potential neuroprotective interventions. First, we reviewed clinical studies in MND, Alzheimer’s disease, Huntington’s disease, Parkinson’s disease and multiple sclerosis, identifying drugs described in at least one MND publication or publications in two or more other diseases. We scored and ranked drugs using a metric evaluating safety, efficacy, study size and study quality. In stage two, we reviewed efficacy of drugs in MND animal models, multicellular eukaryotic models and human induced pluripotent stem cell (iPSC) studies. An expert panel reviewed candidate drugs over two shortlisting rounds and a final selection round, considering the systematic review findings, late breaking evidence, mechanistic plausibility, safety, tolerability and feasibility of evaluation in MND-SMART. Results: From the clinical review, we identified 595 interventions. 66 drugs met our drug/disease logic. Of these, 22 drugs with supportive clinical and preclinical evidence were shortlisted at round 1. Seven drugs proceeded to round 2. The panel reached a consensus to evaluate memantine and trazodone as the first two arms of MND-SMART. Discussion: For future drug selection, we will incorporate automation tools, text-mining and machine learning techniques to the systematic reviews and consider data generated from other domains, including high-throughput phenotypic screening of human iPSCs

Understanding vaccine hesitancy in Canada: Results of a consultation study by the Canadian Immunization Research Network

"Vaccine hesitancy" is a concept now frequently used in vaccination discourse. The increased popularity of this concept in both academic and public health circles is challenging previously held perspectives that individual vaccination attitudes and behaviours are a simple dichotomy of accept or reject. A consultation study was designed to assess the opinions of experts and health professionals concerning the definition, scope, and causes of vaccine hesitancy in Canada. We sent online surveys to two panels (1- vaccination experts and 2- front-line vaccine providers). Two questionnaires were completed by each panel, with data from the first questionnaire informing the development of questions for the second. Our participants defined vaccine hesitancy as an attitude (doubts, concerns) as well as a behaviour (refusing some / many vaccines, delaying vaccination). Our findings also indicate that both vaccine experts and front-line vaccine providers have the perception that vaccine rates have been declining and consider vaccine hesitancy an important issue to address in Canada. Diffusion of negative information online and lack of knowledge about vaccines were identified as the key causes of vaccine hesitancy by the participants. A common understanding of vaccine hesitancy among researchers, public health experts, policy-makers and health care providers will better guide interventions that can more effectively address vaccine hesitancy within Canada

Modelling the effects of media during an influenza epidemic

BACKGROUND: Mass media is used to inform individuals regarding diseases within a population. The effects of mass media during disease outbreaks have been studied in the mathematical modelling literature, by including ‘media functions’ that affect transmission rates in mathematical epidemiological models. The choice of function to employ, however, varies, and thus, epidemic outcomes that are important to inform public health may be affected. METHODS: We present a survey of the disease modelling literature with the effects of mass media. We present a comparison of the functions employed and compare epidemic results parameterized for an influenza outbreak. An agent-based Monte Carlo simulation is created to access variability around key epidemic measurements, and a sensitivity analysis is completed in order to gain insight into which model parameters have the largest influence on epidemic outcomes. RESULTS: Epidemic outcome depends on the media function chosen. Parameters that most influence key epidemic outcomes are different for each media function. CONCLUSION: Different functions used to represent the effects of media during an epidemic will affect the outcomes of a disease model, including the variability in key epidemic measurements. Thus, media functions may not best represent the effects of media during an epidemic. A new method for modelling the effects of media needs to be considered