Advances in scientific computing have allowed the development of complex models that are being routinely applied to problems in disease epidemiology, public health and decision making. The utility of these models depends in part on how well they can reproduce empirical data. However, fitting such models to real world data is greatly hindered both by large numbers of input and output parameters, and by long run times, such that many modelling studies lack a formal calibration methodology. We present a novel method that has the potential to improve the calibration of complex infectious disease models (hereafter called simulators). We present this in the form of a tutorial and a case study where we history match a dynamic, event-driven, individual-based stochastic HIV simulator, using extensive demographic, behavioural and epidemiological data available from Uganda. The tutorial describes history matching and emulation. History matching is an iterative procedure that reduces the simulator's input space by identifying and discarding areas that are unlikely to provide a good match to the empirical data. History matching relies on the computational efficiency of a Bayesian representation of the simulator, known as an emulator. Emulators mimic the simulator's behaviour, but are often several orders of magnitude faster to evaluate. In the case study, we use a 22 input simulator, fitting its 18 outputs simultaneously. After 9 iterations of history matching, a non-implausible region of the simulator input space was identified that was times smaller than the original input space. Simulator evaluations made within this region were found to have a 65% probability of fitting all 18 outputs. History matching and emulation are useful additions to the toolbox of infectious disease modellers. Further research is required to explicitly address the stochastic nature of the simulator as well as to account for correlations between outputs.

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Abstract. Approximate Bayesian Computation (ABC) and other simulationbased inference methods are becoming increasingly used for inference in complex systems, due to their relative ease-of-implementation. We briefly review some of the more popular variants of ABC and their application in epidemiology, before using a real-world model of HIV transmission to illustrate some of challenges when applying ABC methods to high dimensional, computationally intensive models. We then discuss an alternative approach-history matching-that aims to address some of these issues, and conclude with a comparison between these different methodologies.

Prolonged alcohol consumption in humans followed by abstinence precipitates a withdrawal syndrome consisting of anxiety, agitation and in severe cases, seizures. Withdrawal is relieved by a low dose of alcohol, a negative reinforcement that contributes to alcohol dependency. This phenomenon of ‘withdrawal relief’ provides evidence of an ethanol-induced adaptation which resets the balance of signalling in neural circuits. We have used this as a criterion to distinguish between direct and indirect ethanol-induced adaptive behavioural responses in C. elegans with the goal of investigating the genetic basis of ethanol-induced neural plasticity. The paradigm employs a ‘food race assay’ which tests sensorimotor performance of animals acutely and chronically treated with ethanol. We describe a multifaceted C. elegans ‘withdrawal syndrome’. One feature, decrease reversal frequency is not relieved by a low dose of ethanol and most likely results from an indirect adaptation to ethanol caused by inhibition of feeding and a food-deprived behavioural state. However another aspect, an aberrant behaviour consisting of spontaneous deep body bends, did show withdrawal relief and therefore we suggest this is the expression of ethanol-induced plasticity. The potassium channel, slo-1, which is a candidate ethanol effector in C. elegans, is not required for the responses described here. However a mutant deficient in neuropeptides, egl-3, is resistant to withdrawal (although it still exhibits acute responses to ethanol). This dependence on neuropeptides does not involve the NPY-like receptor npr-1, previously implicated in C. elegans ethanol withdrawal. Therefore other neuropeptide pathways mediate this effect. These data resonate with mammalian studies which report involvement of a number of neuropeptides in chronic responses to alcohol including corticotrophin-releasing-factor (CRF), opioids, tachykinins as well as NPY. This suggests an evolutionarily conserved role for neuropeptides in ethanol-induced plasticity and opens the way for a genetic analysis of the effects of alcohol on a simple model system.

The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract. History matching is a model (pre-)calibration method that has been applied to computer models from a wide range of scientific disciplines. In this work we apply history matching to an individualbased epidemiological model of HIV that has 96 input and 50 output parameters, a model of much larger scale than others that have been calibrated before using this or similar methods. Apart from demonstrating that history matching can analyze models of this complexity, a central contribution of this work is that the history match is carried out using linear regression, a statistical tool that is elementary and easier to implement than the Gaussian process-based emulators that have previously been used. Furthermore, we address a practical difficulty with history matching, namely, the sampling of tiny, nonimplausible spaces, by introducing a sampling algorithm adjusted to the specific needs of this method. The effectiveness and simplicity of the history matching method presented here shows that it is a useful tool for the calibration of computationally expensive, high dimensional, individual-based models.