2015美赛A题优秀论文

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2.4 2.5 2.6
Model Modeling Objectives . . . . . . . . . . . . . . . . . . . . Problem Space . . . . . . . . . . . . . . . . . . . . . . . The Multi-Layer State Based Stochastic Epidemic Model 2.3.1 Individual Layer - Stochastic State Based Model . 2.3.2 Inter-Region Layer modeling . . . . . . . . . . . . 2.3.3 Human Mobility Model . . . . . . . . . . . . . . . 2.3.4 Supply Distribution Model . . . . . . . . . . . . . 2.3.5 A note on GLEAM . . . . . . . . . . . . . . . . . Implementation . . . . . . . . . . . . . . . . . . . . . . . Additional Considerations . . . . . . . . . . . . . . . . . 2.5.1 Modeling of Hospitals . . . . . . . . . . . . . . . Consequences of Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Team # 32722 Contents 1 Introduction 1.1 Context of the Problem . 1.2 The Task at Hand . . . 1.3 Previous Work . . . . . 1.4 The GLEAM Simulator 2 Our 2.1 2.2 2.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Team # 32722
Page 1 es: Modeling the Fight against Ebola
Team 32722
Abstract We have constructed a multi-layer state-based model that appears to hold promise for providing insight into not only how the Ebola outbreak in West Africa will continue to develop, but also the best course of intervention in the event an effective vaccine or cure should be created. This model considers a large number of parameters thought to be important to or characteristic of the outbreak, including the impact of the infection’s unique progression in humans, transmission to new individuals from infected persons at different stages of the infection, and the effects of geographic connections on the movement of infected persons and pharmaceuticals. The aforementioned multi-layer approach includes interactions on the level of the individual, a collection of individuals (or “region”), and a collection of regions, in order to capture behaviors at each scale. Individuals transition through states, representing the various stages of an Ebola infection, in a stochastic way with some probabilities being intrinsic to the illness and some being related to the current state-of-affairs in the region. Individuals are treated as homogeneously mixed within regions, but are capable of moving between regions with probabilities determined by the various characteristics of each region. Distribution of medicines occurs at the local level through hospitals and at the regional levels through transit, with medicines and vaccines entering the simulation through defined transportation “hubs”, then being distributed along defined delivery routes from region to region. Our model focuses exclusively on the three countries still suffering from an active presence of Ebola (Guinea, Liberia, and Sierra Leone), making predictions across the next 6 months. Specific attention is given to the sensitivity of our model to variations in its fundamental parameters, whose values are based largely on inadequate and varied statistical data from regions affected by the outbreak. We attempt to use our model to identify important factors related to the effectiveness of distributing a cure for Ebola, and show simulation results related to the influence of geographic factors, delivery frequency/quantity, and distribution tactics in comparison to baseline predictions. Ultimately, we have concluded that given the complex nature of this scenario and the limited quantity and quality of detailed information concerning the outbreak, it is not possible at this time to quantitatively predict the effectiveness of a vaccine/cure distribution plan. Instead, the results presented here qualitatively show how our baseline model responds to changes in the parameters that govern its behavior. These qualitative results are then used to craft a tentative deployment strategy for these hypothetical vaccines and cures.