Dynamic Modeling of Bacterial and Organizational Colony Networks for Coordinated Response to Influenza

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Summary

We propose to develop biologically inspired dynamic social network algorithm for developing robust monitoring, prediction and evaluation of the spread of infections as well as the robustness in coordinated response to infections. In our proposed study, new bio inspired dynamic network algorithms would be developed to perform the real time monitoring of spread of infections which would be used to perform optimized link prediction in a distributed spread of infections network. We aim to provide statistical validation of our proposed spread of infections monitoring and link prediction algorithm by using past spread of infections test case scenarios. We then plan to provide an analysis of how the area health services network evolve and adapt dynamically during the spread of infections for providing robust coordinated response. For the purpose of testing and validation of robustness of coordinated response, we will use spread of infection and response dataset from previous situated infections as well as simulated experiments. The outcome of our study would lead to the development of not only new optimized approach to monitor and link prediction of the spread dynamics of the bacterial colony, but also to explore the robustness of organizational colony of Area Health Networks in responding to the infections optimally. We investigate the following questions: How can we provide robust monitoring of the dynamics of bacterial colony networks (i.e., spread of infections) using biologically inspired dynamic social networks? What are the implications of dynamic monitoring of the spread dynamics and their rate of infections for predicting future link of the spread of infections optimally? How does the organizational colony (i.e., groups, subgroups, and cliques of the Area Health Networks) formed within the network involving multi organizational coordinated response structures? Who is central to each of these multi organizational coordinated response structures? Who serves as a bridge among multi organizational coordinating teams? Can we optimize the robustness of coordinated response by integrating the bacterial and organizational colony network dynamics in a single predictive framework?

Supervisor(s)

Professor Liaquat Hossain

Research Location

Civil Engineering

Program Type

Masters/PHD

Synopsis

The objectives of this study are as follows: 

  1. To determine the bacterial colony dynamics by applying dynamic social network modeling;
  2. To determine the structure of existing organizational colony networks within the Area Health Network teams;
  3. To compare existing bacterial and organizational colony networks with the corresponding structures in biological networks;
  4. To map the algorithmic description of biological networks to the structure and nature of agency-public and agency-agency information networks in multi-organizational colony networks;
  5. To develop new information network structures based on the biological models;
  6. To compare the dynamics of current information networks with those predicted by the biologically-inspired networks.
  7. To compare the scalability of current information-networks with that of the biologically-inspired networks by studying the dynamic behavior in (3) for different sizes of network and spatial structures.

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Keywords

social network algorithm, evaluation of the spread of infections, statistical validation, structure of existing organizational colony networks, Information networks, dynamics of bacterial colony networks, responding to the infections

Opportunity ID

The opportunity ID for this research opportunity is: 1353

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