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Browsing by Author "Ma, Yifei"

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    Comparing Effectiveness of Top-Down and Bottom-Up Strategies in Containing Influenza
    Marathe, Achla; Lewis, Bryan L.; Barrett, Christopher L.; Chen, Jiangzhuo; Marathe, Madhav V.; Eubank, Stephen; Ma, Yifei (Public Library of Science, 2011-09-22)
    This research compares the performance of bottom-up, self-motivated behavioral interventions with top-down interventions targeted at controlling an “Influenza-like-illness”. Both types of interventions use a variant of the ring strategy. In the first case, when the fraction of a person's direct contacts who are diagnosed exceeds a threshold, that person decides to seek prophylaxis, e.g. vaccine or antivirals; in the second case, we consider two intervention protocols, denoted Block and School: when a fraction of people who are diagnosed in a Census Block (resp., School) exceeds the threshold, prophylax the entire Block (resp., School). Results show that the bottom-up strategy outperforms the top-down strategies under our parameter settings. Even in situations where the Block strategy reduces the overall attack rate well, it incurs a much higher cost. These findings lend credence to the notion that if people used antivirals effectively, making them available quickly on demand to private citizens could be a very effective way to control an outbreak.
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    A Database Supported Modeling Environment for Pandemic Planning and Course of Action Analysis
    Ma, Yifei (Virginia Tech, 2013-06-24)
    Pandemics can significantly impact public health and society, for instance, the 2009 H1N1
    and the 2003 SARS. In addition to analyzing the historic epidemic data, computational simulation of epidemic propagation processes and disease control strategies can help us understand the spatio-temporal dynamics of epidemics in the laboratory. Consequently, the public can be better prepared and the government can control future epidemic outbreaks more effectively. Recently, epidemic propagation simulation systems, which use high performance computing technology, have been proposed and developed to understand disease propagation processes. However, run-time infection situation assessment and intervention adjustment, two important steps in modeling disease propagation, are not well supported in these simulation systems. In addition, these simulation systems are computationally efficient in their simulations, but most of them have
    limited capabilities in terms of modeling interventions in realistic scenarios.
    In this dissertation, we focus on building a modeling and simulation environment for epidemic propagation and propagation control strategy. The objective of this work is to
    design such a modeling environment that both supports the previously missing functions,
    meanwhile, performs well in terms of the expected features such as modeling fidelity,
    computational efficiency, modeling capability, etc. Our proposed methodologies to build
    such a modeling environment are: 1) decoupled and co-evolving models for disease propagation, situation assessment, and propagation control strategy, and 2) assessing situations and simulating control strategies using relational databases. Our motivation for exploring these methodologies is as follows: 1) a decoupled and co-evolving model allows us to design modules for each function separately and makes this complex modeling system design simpler, and 2) simulating propagation control strategies using relational databases improves the modeling capability and human productivity of using this modeling environment. To evaluate our proposed methodologies, we have designed and built a loosely coupled and database supported epidemic modeling and simulation environment. With detailed experimental results and realistic case studies, we demonstrate that our modeling environment provides the missing functions and greatly enhances many expected features, such as modeling capability, without significantly sacrificing computational efficiency and scalability.