Reduced modelling and optimal control of epidemiological individual-based models with contact heterogeneity

Modelling epidemics via classical population-based models suffers from shortcomings that socalled individual-based models are able to overcome, as they are able to take heterogeneity features into account, such as super-spreaders, and describe the dynamics involved in small clusters. In return, such models often involve large graphs which are expensive to simulate and difficult to optimize, both in theory and in practice. By combining the reinforcement learning philosophy with reduced models, we propose a numerical approach to determine optimal health policies for a stochastic epidemiological graphmodel taking into account super-spreaders. More precisely, we introduce a deterministic reduced population-based model involving a neural network, and use it to derive optimal health policies through an optimal control approach. It is meant to faithfully mimic the local dynamics of the original, more complex, graph-model. Roughly speaking, this is achieved by sequentially training the network until an optimal control strategy for the corresponding reduced model manages to equally well contain the epidemic when simulated on the graph-model. After describing the practical implementation of this approach, we will discuss the range of applicability of the reduced model and to what extent the estimated control strategies could provide useful qualitative information to health authorities

Reduced modelling and optimal control of epidemiological individual-based models with contact heterogeneity

Modelling epidemics via classical population-based models suffers from shortcomings that socalled individual-based models are able to overcome, as they are able to take heterogeneity features into account, such as super-spreaders, and describe the dynamics involved in small clusters. In return, such models often involve large graphs which are expensive to simulate and difficult to optimize, both in theory and in practice. By combining the reinforcement learning philosophy with reduced models, we propose a numerical approach to determine optimal health policies for a stochastic epidemiological graphmodel taking into account super-spreaders. More precisely, we introduce a deterministic reduced population-based model involving a neural network, and use it to derive optimal health policies through an optimal control approach. It is meant to faithfully mimic the local dynamics of the original, more complex, graph-model. Roughly speaking, this is achieved by sequentially training the network until an optimal control strategy for the corresponding reduced model manages to equally well contain the epidemic when simulated on the graph-model. After describing the practical implementation of this approach, we will discuss the range of applicability of the reduced model and to what extent the estimated control strategies could provide useful qualitative information to health authorities

Dispersed oil decreases the ability of a model fish (Dicentrarchus labrax) to cope with hydrostatic pressure

© 2016, Springer-Verlag Berlin Heidelberg. Data on the biological impact of oil dispersion in deep-sea environment are scarce. Hence, the aim of this study was to evaluate the potential interest of a pressure challenge as a new experimental approach for the assessment of consequences of chemically dispersed oil, followed by a high hydrostatic pressure challenge. This work was conducted on a model fish: juvenile Dicentrarchus labrax. Seabass were exposed for 48 h to dispersant alone (nominal concentration (NC) = 4 mg L-1), mechanically dispersed oil (NC = 80 mg L-1), two chemically dispersed types of oil (NC = 50 and 80 mg L-1with a dispersant/oil ratio of 1/20), or kept in clean seawater. Fish were then exposed for 30 min at a simulated depth of 1350 m, corresponding to pressure of 136 absolute atmospheres (ATA). The probability of fish exhibiting normal activity after the pressure challenge significantly increased from 0.40 to 0.55 when they were exposed to the dispersant but decreased to 0.26 and 0.11 in the case of chemical dispersion of oil (at 50 and 80 mg L-1, respectively). The chemical dispersion at 80 mg L-1also induced an increase in probability of death after the pressure challenge (from 0.08 to 0.26). This study clearly demonstrates the ability of a pressure challenge test to give evidence of the effects of a contaminant on the capacity of fish to face hydrostatic pressure. It opens new perspectives on the analysis of the biological impact of chemical dispersion of oil at depth, especially on marine species performing vertical migrations