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Active learning to understand infectious disease models and improve policy making.

ABSTRACT: Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive. A more systematic exploration is needed to gain a thorough systems understanding. We present an active learning approach based on machine learning techniques as iterative surrogate modeling and model-guided experimentation to systematically analyze both common and edge manifestations of complex model runs. Symbolic regression is used for nonlinear response surface modeling with automatic feature selection. First, we illustrate our approach using an individual-based model for influenza vaccination. After optimizing the parameter space, we observe an inverse relationship between vaccination coverage and cumulative attack rate reinforced by herd immunity. Second, we demonstrate the use of surrogate modeling techniques on input-response data from a deterministic dynamic model, which was designed to explore the cost-effectiveness of varicella-zoster virus vaccination. We use symbolic regression to handle high dimensionality and correlated inputs and to identify the most influential variables. Provided insight is used to focus research, reduce dimensionality and decrease decision uncertainty. We conclude that active learning is needed to fully understand complex systems behavior. Surrogate models can be readily explored at no computational expense, and can also be used as emulator to improve rapid policy making in various settings.

SUBMITTER: Willem L

PROVIDER: S-EPMC3990517 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Active learning to understand infectious disease models and improve policy making.

Willem Lander L Stijven Sean S Vladislavleva Ekaterina E Broeckhove Jan J Beutels Philippe P Hens Niel N

PLoS computational biology 20140417 4

Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive. A more systematic exploration is needed to gain a thorough systems understanding. We present an active learning approach based on machine learning techniques as iterative surrogate modeling and model-guided experimentation to systematically analyze both common and edge manifestations of complex model runs. Symbolic regression is used for nonl ...[more]

PMID: 24743387

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Project description:PurposeTo identify co-produced multi-stakeholder perspectives important for successful widespread physically active learning (PAL) adoption and implementation.MethodsA total of 35 stakeholders (policymakers n?=?9; commercial education sector, n?=?8; teachers, n?=?3; researchers, n?=?15) attended a design thinking PAL workshop. Participants formed 5 multi-disciplinary groups with at least 1 representative from each stakeholder group. Each group, facilitated by a researcher, undertook 2 tasks: (1) using Post-it Notes, the following question was answered: within the school day, what are the opportunities for learning combined with movement? and (2) structured as a washing-line task, the following question was answered: how can we establish PAL as the norm? All discussions were audio-recorded and transcribed. Inductive analyses were conducted by 4 authors. After the analyses were complete, the main themes and subthemes were assigned to 4 predetermined categories: (1) PAL design and implementation, (2) priorities for practice, (3) priorities for policy, and (4) priorities for research.ResultsThe following were the main themes for PAL implementation: opportunities for PAL within the school day, delivery environments, learning approaches, and the intensity of PAL. The main themes for the priorities for practice included teacher confidence and competence, resources to support delivery, and community of practice. The main themes for the policy for priorities included self-governance, the Office for Standards in Education, Children's Services, and Skill, policy investment in initial teacher training, and curriculum reform. The main themes for the research priorities included establishing a strong evidence base, school-based PAL implementation, and a whole-systems approach.ConclusionThe present study is the first to identify PAL implementation factors using a combined multi-stakeholder perspective. To achieve wider PAL adoption and implementation, future interventions should be evidence based and address implementation factors at the classroom level (e.g., approaches and delivery environments), school level (e.g., communities of practice), and policy level (e.g., initial teacher training).

Project description:Converging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms.

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Active learning to understand infectious disease models and improve policy making.

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Active learning to understand infectious disease models and improve policy making.

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