Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies

Pandemic influenza has the epidemic potential to kill millions of people. While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies that lead to their most effective and efficient use remains challenging. To this end, individual-based epidemiological models are essential to assist decision makers in determining the best strategy to curb epidemic spread. However, individual-based models are computationally intensive and it is therefore pivotal to identify the optimal strategy using a minimal amount of model evaluations. Additionally, as epidemiological modeling experiments need to be planned, a computational budget needs to be specified a priori. Consequently, we present a new sampling technique to optimize the evaluation of preventive strategies using fixed budget best-arm identification algorithms. We use epidemiological modeling theory to derive knowledge about the reward distribution which we exploit using Bayesian best-arm identification algorithms (i.e., Top-two Thompson sampling and BayesGap). We evaluate these algorithms in a realistic experimental setting and demonstrate that it is possible to identify the optimal strategy using only a limited number of model evaluations, i.e., 2-to-3 times faster compared to the uniform sampling method, the predominant technique used for epidemiological decision making in the literature. Finally, we contribute and evaluate a statistic for Top-two Thompson sampling to inform the decision makers about the confidence of an arm recommendation

Dementia as a source of social disadvantage and exclusion

Objective To explore perceptions of the impacts of dementia on people living with the condition and those close to them and examine the relationship between dementia, disadvantage and social exclusion. Methods Semi-structured in-depth interviews were conducted with 111 participants: people with dementia (n = 19), carers (n = 28), health-care professionals (n = 21), social workers (n = 23) and service professionals (n = 20). NVivo 11 was used to code descriptions and identify impact areas. Results Participants described social, psychological, carer, material, service-based and disparity impacts associated with the experience of dementia. Some of these impacts correspond to social exclusion associated with age, but some are distinctive to dementia. Discussion It is argued that dementia generates its own forms of social disadvantage and exclusion. This is in addition to being subject to structural risk factors. The implications of the active effects of dementia as a social phenomenon should give rise to new policy and practice priorities.Peer reviewe

FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model

Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development

Simulations for designing and interpreting intervention trials in infectious diseases

Background: Interventions in infectious diseases can have both direct effects on individuals who receive the intervention as well as indirect effects in the population. In addition, intervention combinations can have complex interactions at the population level, which are often difficult to adequately assess with standard study designs and analytical methods.Discussion: Herein, we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly with regard to emerging infectious diseases, one that more accurately reflects the dynamics of the transmission process. In an increasingly complex world, simulations can explicitly represent transmission dynamics, which are critical for proper trial design and interpretation. Certain ethical aspects of a trial can also be quantified using simulations. Further, after a trial has been conducted, simulations can be used to explore the possible explanations for the observed effects.Conclusion: Much is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods, and the conduct of clinical trials

The FAT10- and ubiquitin-dependent degradation machineries exhibit common and distinct requirements for MHC class I antigen presentation

Like ubiquitin (Ub), the ubiquitin-like protein FAT10 can serve as a signal for proteasome-dependent protein degradation. Here, we investigated the contribution of FAT10 substrate modification to MHC class I antigen presentation. We show that N-terminal modification of the human cytomegalovirus-derived pp65 antigen to FAT10 facilitates direct presentation and dendritic cell-mediated cross-presentation of the HLA-A2 restricted pp65495–503 epitope. Interestingly, our data indicate that the pp65 presentation initiated by either FAT10 or Ub partially relied on the 19S proteasome subunit Rpn10 (S5a). However, FAT10 distinguished itself from Ub in that it promoted a pp65 response which was not influenced by immunoproteasomes or PA28. Further divergence occurred at the level of Ub-binding proteins with NUB1 supporting the pp65 presentation arising from FAT10, while it exerted no effect on that initiated by Ub. Collectively, our data establish FAT10 modification as a distinct and alternative signal for facilitated MHC class I antigen presentation

The Population Impact of a Large School-Based Influenza Vaccination Campaign

The optimal vaccination strategy to mitigate the impact of influenza epidemics is unclear. In 2005, a countywide school-based influenza vaccination campaign was launched in Knox County, Tennessee (population 385,899). Approximately 41% and 48% of eligible county children aged 5-17 years were immunized with live attenuated influenza vaccine before the 2005-2006 and 2006-2007 influenza seasons, respectively. We sought to determine the population impact of this campaign.Laboratory-confirmed influenza data defined influenza seasons. We calculated the incidence of medically attended acute respiratory illness attributable to influenza in Knox and Knox-surrounding counties (concurrent controls) during consecutive seasons (5 precampaign and 2 campaign seasons) using negative binomial regression and rate difference methods. Age-stratified analyses compared the incidence of emergency department (ED) visits and hospitalizations attributable to influenza.During precampaign seasons, estimated ED visit rates attributable to influenza were 12.39 (95% CI: 10.34-14.44) per 1000 Knox children aged 5-17 years and similar in Knox-surrounding counties. During the campaign seasons, annual Knox influenza-associated ED visit rates declined relative to rates in Knox-surrounding counties: rate ratios 0.55 (95% CI: 0.27-0.83) and 0.70 (95% CI: 0.56-0.84) for the first and second campaign seasons, respectively. Overall, there were about 35% or 4.86 per 1000 fewer influenza-associated ED visits among Knox County children aged 5-17 years attributable to the campaign. No significant declines in Knox compared to surrounding counties were detected for influenza associated ED visits in children aged <5 years, all adults combined or selected adult age subgroups, although power for these analyses was limited. Alternate rate-difference analyses yielded consistent results.Vaccination of approximately 45% of Knox school-aged children with influenza vaccine was associated with a 35% annual reduction (4.86 per 1000) in ED visit rates attributable to influenza. Higher vaccination coverage and/or larger studies would be needed to determine whether similar interventions have indirect benefits in other age groups

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model

ABSTRACT: BACKGROUND: Simulation models of influenza spread play an important role for pandemic preparedness. However, as the world has not faced a severe pandemic for decades, except the rather mild H1N1 one in 2009, pandemic influenza models are inherently hypothetical and validation is, thus, difficult. We aim at reconstructing a recent seasonal influenza epidemic that occurred in Switzerland and deem this to be a promising validation strategy for models of influenza spread. METHODS: We present a spatially explicit, individual-based simulation model of influenza spread. The simulation model bases upon (i) simulated human travel data, (ii) data on human contact patterns and (iii) empirical knowledge on the epidemiology of influenza. For model validation we compare the simulation outcomes with empirical knowledge regarding (i) the shape of the epidemic curve, overall infection rate and reproduction number, (ii) age-dependent infection rates and time of infection, (iii) spatial patterns. RESULTS: The simulation model is capable of reproducing the shape of the 2003/2004 H3N2 epidemic curve of Switzerland and generates an overall infection rate (14.9 percent) and reproduction numbers (between 1.2 and 1.3), which are realistic for seasonal influenza epidemics. Age and spatial patterns observed in empirical data are also reflected by the model: Highest infection rates are in children between 5 and 14 and the disease spreads along the main transport axes from west to east. CONCLUSIONS: We show that finding evidence for the validity of simulation models of influenza spread by challenging them with seasonal influenza outbreak data is possible and promising. Simulation models for pandemic spread gain more credibility if they are able to reproduce seasonal influenza outbreaks. For more robust modelling of seasonal influenza, serological data complementing sentinel information would be beneficia

Real-time numerical forecast of global epidemic spreading: Case study of 2009 A/H1N1pdm

Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. Methods We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Results Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Conclusions Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models