Assessing the feasibility of Phase 3 vaccine trials against Marburg Virus Disease: A modelling study.

Background: Outbreaks of Marburg virus disease (MVD) are rare and small in size, with only 18 recorded outbreaks since 1967, only two of which involved more than 100 cases. It has been proposed, therefore, that Phase 3 trials for MVD vaccines should be held open over multiple outbreaks until sufficient end points accrue to enable vaccine efficacy (VE) to be calculated. Here we estimate how many outbreaks might be needed for VE to be estimated. Methods: We adapt a mathematical model of MVD transmission to simulate a Phase 3 individually randomised placebo controlled vaccine trial. We assume in the base case that vaccine efficacy is 70% and that 50% of individuals in affected areas are enrolled into the trial (1:1 randomisation). We further assume that the vaccine trial starts two weeks after public health interventions are put in place and that cases occurring within 10 days of vaccination are not included in VE calculations. Results: The median size of simulated outbreaks was 2 cases. Only 0.3% of simulated outbreaks were predicted to have more than 100 MVD cases. 95% of simulated outbreaks terminated before cases accrued in the placebo and vaccine arms. Therefore the number of outbreaks required to estimate VE was large: after 100 outbreaks, the estimated VE was 69% but with considerable uncertainty (95% CIs: 0%-100%) while the estimated VE after 200 outbreaks was 67% (95% CIs: 42%-85%). Altering base-case assumptions made little difference to the findings. In a sensitivity analysis, increasing R0 by 25% and 50% led to an estimated VE after 200 outbreaks of 69% (95% CIs: 53-85%) and 70% (95% CIs: 59-82%), respectively. Conclusions: It is unlikely that the efficacy of any candidate vaccine can be calculated before more MVD outbreaks have occurred than have been recorded to date. This is because MVD outbreaks tend to be small, public health interventions have been historically effective at reducing transmission, and vaccine trials are only likely to start after these interventions are already in place. Hence, it is expected that outbreaks will terminate before, or shortly after, cases start to accrue in the vaccine and placebo arms.

Vaccine efficacy trials for Crimean-Congo haemorrhagic fever: Insights from modelling different epidemiological settings.

BACKGROUND: Crimean-Congo haemorrhagic fever (CCHF) is a priority emerging pathogen for which a licensed vaccine is not yet available. We aim to assess the feasibility of conducting phase III vaccine efficacy trials and the role of varying transmission dynamics. METHODS: We calibrate models of CCHF virus (CCHFV) transmission among livestock and spillover to humans in endemic areas in Afghanistan, Turkey and South Africa. We propose an individual randomised controlled trial targeted to high-risk population, and use the calibrated models to simulate trial cohorts to estimate the minimum necessary number of cases (trial endpoints) to analyse a vaccine with a minimum efficacy of 60%, under different conditions of sample size and follow-up time in the three selected settings. RESULTS: A mean follow-up of 160,000 person-month (75,000-550,000) would be necessary to accrue the required 150 trial endpoints for a target vaccine efficacy of 60 % and clinically defined endpoint, in a setting like Herat, Afghanistan. For Turkey, the same would be achieved with a mean follow-up of 175,000 person-month (50,000-350,000). The results suggest that for South Africa the low endemic transmission levels will not permit achieving the necessary conditions for conducting this trial within a realistic follow-up time. In the scenario of CCHFV vaccine trial designed to capture infection as opposed to clinical case as a trial endpoint, the required person-months is reduced by 70 % to 80 % in Afghanistan and Turkey, and in South Africa, a trial becomes feasible for a large number of person-months of follow-up (>600,000). Increased expected vaccine efficacy > 60 % will reduce the required number of trial endpoints and thus the sample size and follow-time in phase III trials. CONCLUSIONS: Underlying endemic transmission levels will play a central role in defining the feasibility of phase III vaccine efficacy trials. Endemic settings in Afghanistan and Turkey offer conditions under which such studies could feasibly be conducted.

Disease severity determines health-seeking behaviour amongst individuals with influenza-like illness in an internet-based cohort.

BACKGROUND: Seasonal influenza epidemics place considerable strain on health services. Robust systems of surveillance are therefore required to ensure preparedness. Sentinel surveillance does not accurately capture the community burden of epidemics as it misses cases that do not present to health services. In this study, Flusurvey (an internet-based community surveillance tool) was used to examine how severity of disease influences health-seeking behaviour in the UK. METHODS: Logistic regression with random effects was used to investigate the association between health-seeking and symptom severity, duration of illness and reduction in self-reported health-score over four flu seasons between 2011 and 2015. RESULTS: The majority of individuals did not seek care. In general, there was very strong evidence for an association between all severity indicators and visiting a health service (p < 0.0001). Being female (OR 1.62, 95% CI 1.23-2.14, p = 0.0003) and a self-diagnosis of the flu (OR 3.39, 95% CI 2.38-4.83, p < 0.0001) were also associated with increased likelihood of visiting a health service. During the 2012-13 and 2014-15 flu seasons, there was a significantly larger proportion of individuals with more severe sets of symptoms and a longer duration of illness. Despite this, the proportion of individuals with particular sets of symptoms visiting a health service showed only very slight variation across years. CONCLUSIONS: Traditional surveillance systems capture only the more severe episodes of illness. However, in spite of variation in flu activity, the proportion of individuals visiting a health service remains relatively stable within specific sets of symptoms across years. These data could be used in combination with data on consultation rates to provide better estimates of community burden.

Structural differences in mixing behavior informing the role of asymptomatic infection and testing symptom heritability.

Most infectious disease data is obtained from disease surveillance which is based on observations of symptomatic cases only. However, many infectious diseases are transmitted before the onset of symptoms or without developing symptoms at all throughout the entire disease course, referred to as asymptomatic transmission. Fraser and colleagues [1] showed that this type of transmission plays a key role in assessing the feasibility of intervention measures in controlling an epidemic outbreak. To account for asymptomatic transmission in epidemic models, methods often rely on assumptions that cannot be verified given the data at hand. The present study aims at assessing the contribution of social contact data from asymptomatic and symptomatic individuals in quantifying the contribution of (a)symptomatic infections. We use a mathematical model based on ordinary differential equations (ODE) and a likelihood-based approach followed by Markov Chain Monte Carlo (MCMC) to estimate the model parameters and their uncertainty. Incidence data on influenza-like illness in the initial phase of the 2009 A/H1N1pdm epidemic is used to illustrate that it is possible to estimate either the proportion of asymptomatic infections or the relative infectiousness of symptomatic versus asymptomatic infectives. Further, we introduce a model in which the chance of developing symptoms depends on the disease state of the person that transmitted the infection. In conclusion, incorporating social contact data from both asymptomatic and symptomatic individuals allows inferring on parameters associated with asymptomatic infection based on disease data from symptomatic cases only.