Biology subjects

Griffiths, K.

Publications and source records attributed to Griffiths, K..

2 recordsLinked to original sources

bioRxiv2018

Targeting hotspots to reduce transmission of malaria in Senegal: modeling of the effects of human mobility

BackgroundIn central Senegal malaria incidences have declined in recent years in response to scaling-up of control measures, but now remains stable, making elimination improbable. Additional control measures are needed to reduce transmission.\n\nMethodsBy using a meta-population mathematical model, we evaluated chemotherapy interventions targeting stable malaria hotspots, using a differential equation framework and incorporating human mobility, and fitted to weekly malaria incidences from 45 villages, over 5 years. Three simulated approaches for selecting intervention targets were compared: a) villages with at least one malaria case during the low transmission season of the previous year; b) villages ranked highest in terms of incidence during the high transmission season of the previous year; c) villages ranked based on the degree of connectivity with adjacent populations.\n\nResultsOur mathematical modeling, taking into account human mobility, showed that the intervention strategies targeting hotspots should be effective in reducing malaria incidence in both treated and untreated areas.\n\nConclusionsMathematical simulations showed that targeted interventions allow increasing malaria elimination potential.

epidemiology↗

bioRxiv2018

Models that learn how humans learn: the case of depression and bipolar disorders

Computational models of learning and decision-making processes in the brain play an important role in many domains. Such models typically have a constrained structure and make specific assumptions about the underlying human learning processes; these may make them underfit observed behaviours. Here we suggest an alternative method based on learning-to-learn approaches, using recurrent neural networks (RNNs) as a flexible family of models that have sufficient capacity to represent the complex learning and decision-making strategies used by humans. In this approach, an RNN is trained to predict the next action that a subject will take in a decision-making task, and in this way, learns to imitate the processes underlying subjects choices and their learning abilities. We demonstrate the benefits of this approach with a new dataset containing behaviour of uni-polar depression (n=34), bipolar (n=33) and control (n=34) participants in a two-armed bandit task. The results indicate that the new approach is better than baseline reinforcement-learning methods in terms of overall performance and its capacity to predict subjects choices. We show that the model can be interpreted using off-policy simulations, and thereby provide a novel clustering of subjects learning processes - something that often eludes traditional approaches to modelling and behavioural analysis.

bioinformatics↗