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Long- vs. Short-Term Friendships and the Spread of Disease |
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Goal of Project:
This project will focus on examining the effect of varying the percentages of long- vs. short-term social contacts on patterns of disease spread in a population over time.
Build a theoretical understanding of how varying the percentage of each duration of friendship among social contacts over time will affect disease dynamics. Question to Address: By keeping long term friendships and minimizing short-term contacts, are you less prone to getting a disease? How to go about it? 1. Determine the length of a long and short term friendship 2. Vary duration of Long and Short term friendships Higher Percentage of Long Term —> Static Network
In the current model, the number of friendships changed at each iteration of each node is uniformly distributed throughout the network. (e.g. At each iteration, a single node will lose 2 friends and keep the 3 friends with the highest metric value of choice.) I will first choose one node and vary the friendships based on a longer time interval, representing a longer connection to a specific friend. I will also decrease the number of iterations to represent short term friendships. I will continue to do this for two nodes, three nodes…until I determine the number of long term and short term friendships of all nodes of the network.
3. Vary percentage of Long and Short Friendships In the current model, each node begins with the same number of contacts. I will vary the number of long term friendships and short term friendships assigning each node an introductory number of each.
4. Randomly place disease in network We run multiple tests to choose the node that becomes infected with the disease to give the worst possible outcome (possible deaths; later paper) and the best possible outcome (saving lives). We will then run random iterations to see the effect of placing a disease into a random network. Publications Consulted: Fefferman, N.H. and K.L Ng. 2007. The role of individual choice in the evolution of social complexity. Annales Zoologici Fennici, 44:58-69.
Fefferman, N.H. and K.L Ng. 2007. How disease models in static networks can fail to approximate disease in dynamic networks. Phys. Rev. E 76, 031919 |
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To contact us:
Bobby Zandstra Florida Gulf Coast University Bioengineering and Mathematics DIMACS REU 2008
Mentor:
Nina H. Fefferman, PhD Assistant Research Professor The Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) feferman@math.princeton.edu
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