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I will explore these techniques for the transmission model, and also in statistical modelling aiming to account for the observed patterns of drug-resistance. The transmission model will ultimately be extended to include different types of drug resistance. As new treatments and vaccines emerge, those with responsibility for public health will want to understand the potential impact these new technologies can have in terms of gains in health, and changes in spending. Producing cost-effectiveness and budget impact evidence requires a model that includes transmission, in order to account for indirect benefits accrued by avoiding secondary cases. We will use our model to provide guidance to decision-makers seeking to maximise health gain with limited resources. 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However there have been limited uptake of system-wide mathematical optimisation techniques (in part due to significant data requirements); no efforts to using data routinely collected by HISs for billing and claims purposes, as well as Electronic Health Records (EHRs), for improving efficiency and quality, even though existing attempts among LMICs to use such data retrospectively as inputs into HTA and guidelines show promise. We hypothesise that a LHS framework for national HISs, combining resource optimization, argumentation (as understood in AI), clinical decision support and clinical and policy analytics, will facilitate the uptake of evidence-based, cost-effective and data-driven healthcare interventions, and improve the overall efficiency and quality of integrated care systems in LMICs transitioning to UHC. ROAD2H will deliver DSS tools that integrate individual patient data (drawn from EHRs) with localized clinical guidelines (including HTA, clinical pathways) obtained via policy and clinical analytics from best available clinical and cost-effectiveness evidence as well as HIS billing/claim data. We will use argumentation to parameterize optimisation problems drawn from cost-effectiveness and resource optimization considerations, and then use standard methods in mathematical optimisation to maximize efficiency of clinical interventions. We will use argumentation also to integrate optimization for maximal efficiency and reasoning with localized guidelines, and resolve conflicts as well as transparently explain recommendations for clinical interventions. The DSS will make use of these explained recommendations as well as data provenance techniques to generate and transparently collect patient-tailored recommendations to enable further learning, in line with the LHS methodology. 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