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The intuitive response to an invading pathogen is to start disease management as rapidly as possible, since this would be expected to minimise the future impacts of disease. However, since more spread data become available as an outbreak unfolds, processes underpinning pathogen transmission can almost always be characterised more precisely later in epidemics. This allows the future progression of any outbreak to be forecast more accurately, and so enables control interventions to be targeted more precisely. There is also the chance that the outbreak might die out without any intervention whatsoever, making prophylactic control unnecessary. Optimal decision-making involves continuously balancing these potential benefits of waiting against the possible costs of further spread. We introduce a generic, extensible data-driven algorithm based on parameter estimation and outbreak simulation for making decisions in real-time concerning when and how to control an invading pathogen. The Control Smart Algorithm (CSA) resolves the trade-off between the competing advantages of controlling as soon as possible and controlling later when more information has become available. We show–using a generic mathematical model representing the transmission of a pathogen of agricultural animals or plants through a population of farms or fields–how the CSA allows the timing and level of deployment of vaccination or chemical control to be optimised. In particular, the algorithm outperforms simpler strategies such as intervening when the outbreak size reaches a pre-specified threshold, or controlling when the outbreak has persisted for a threshold length of time. This remains the case even if the simpler methods are fully optimised in advance. Our work highlights the potential benefits of giving careful consideration to the question of when to start disease management during emerging outbreaks, and provides a concrete framework to allow policy-makers to make this decision., Author summary Infectious disease outbreaks in human, animal and plant populations can have devastating consequences. Where and how much control should be deployed are difficult questions to answer. However, mathematical modelling is increasingly used by policy-makers to underpin these–sometimes contentious–decisions. When to manage disease is often viewed as more straightforward. Surely interventions should be introduced quickly, in an attempt to contain the outbreak as soon as possible? In practice, there are a number of unavoidable uncertainties at the beginning of any outbreak, such as whether or not the initial cases will lead to a major epidemic, as well as uncertainty about the exact transmissibility of the pathogen. We show that waiting to resolve these uncertainties before starting interventions can lead to less costly outbreaks, even though waiting means there might then be a larger outbreak to control. We have developed a novel algorithm that can be used in real-time during emerging outbreaks to decide when and how to introduce control. While for human diseases it might be controversial for policy-makers to recommend delaying interventions, for pathogens of plants or animals such a strategy will often be optimal and ethically justifiable. Our work shows how this approach might be implemented in practice.

This dissertation investigated the grammatical use of eye gaze in American Sign Language (ASL) in three experiments, all of which make use of head-mounted eye-tracking technology. Experiment 1 investigates the use of eye gaze to mark verb agreement by native signers of ASL. The results support the use of eye gaze as a verb agreement marker. However, the findings differ from previous claims that eye gaze marks object agreement across all three ASL verb types (agreeing, spatial, and plain). Instead, the data argue for a system of eye gaze agreement that marks verbs according to a universal accessibility hierarchy: Subject < Direct Object < Indirect Object < Locative. Experiment 2 investigates the acquisition of eye gaze agreement by hearing late learners of ASL. The focus is how language-specific properties, including modality, might influence the acquisition of a signed second language (L2). Beginning signers (> 2 years of sign exposure) do not direct their gaze appropriately when producing ASL verbs, indicating that the linguistic use of eye gaze does not occur "naturally," but must be learned. Proficient late-learners of ASL followed the eye gaze patterns we had observed for native signers, thus replicating our previous results. However, they overgeneralized the use of eye gaze, marking agreement for plain verbs as well as spatial and agreeing verbs. Overall, L2 learners of ASL eye gaze exhibit the same patterns of L2 acquisition found in spoken languages suggesting that modality had little if any affect on acquisition. Experiment 3 investigated whether directed eye gaze also occurs during the production of pronouns. The use of directed eye gaze to mark pronominal referents is predicted by some analyses of ASL pronominals. However, the results show no systematic gaze distinction occurring with pronouns, thus indicating that the underlying mechanism for verb agreement and pronominal reference is not the same. In sum, this research shows that an exotic language feature such as eye gaze can function as a syntactic marker that follows linguistic principles and can be learned

Andrew J Lloyd,1 Robin Thompson,2 Katy Gallop,1 Megan Teynor3 1Acaster Lloyd Consulting Ltd., London, UK; 2Value and Access, Biogen, Baar, Switzerland; 3Value and Access, Biogen, Cambridge, MA, USACorrespondence: Andrew J LloydAcaster Lloyd Consulting Ltd., 16 Upper Woburn Place, London WC1H0BS, UKTel +44 20 3978 1681Email andrew.lloyd@acasterlloyd.comBackground: Spinal muscular atrophy (SMA) is a rare, genetic, progressive neuromuscular disorder characterized by severe muscle atrophy and weakness and is a leading genetic cause of death in infants and children. Nusinersen was the first treatment targeting the underlying cause of disease approved by the FDA, EMA and other countries for patients with SMA. There are currently very limited data available on the health-related quality of life (HRQoL) burden of SMA suitable for use in a cost-effectiveness analysis.Objective: This study was designed to estimate quality of life weights or utilities for different SMA states.Methods: SMA case studies were developed describing Type I (infantile onset) and Type II (later-onset) patients and different outcomes from treatment. These were developed so that quality of life weights or utilities (where the value of health ranges from 1 – full health to 0 – dead) could be estimated for cost-effectiveness analysis. Clinical experts (n=5) rated each of the case studies using standardized HRQoL instruments – the EQ-5D-Y and PedsQL-NMM (baseline states only).Results: The SMA Type I utilities ranged from −0.33 (requires ventilation) to 0.71 (Type I patient reclassified as Type III following treatment), with quite substantial differences between some states. Most Type I states had a utility score below zero indicating the severity of the states. The SMA Type II utilities ranged from −0.13 (worsened) to 0.72 (stands/walks unaided). In general, the results showed HRQoL improved in line with better health states.Conclusion: The utility scores obtained in this study highlight the very substantial burden experienced by SMA patients. Despite the limitations in the methods used, this study produced data with face validity and is a useful starting point for understanding the burden of SMA Types I and II in cost-effectiveness analysis.Keywords: spinal muscular atrophy, health-related quality of life, utility, EQ-5D, cost-effectiveness analysis