Readme

This code runs simulations of a problem called Cost Function Firefighter, a variant of The Firefighter Problem (see survey by Finbow and MacGillivray [1]). We implement four cost functions (some with variable amount of stochasticity) and three main heuristics, each of which can be combined with another heuristic to break ties.

Main simulation code can be found in simulation.py - the code in the main method can be used to reproduce the results used in the associated extended manuscript (under review). This relies on soct function and heuristic definitions in costs_and_heuristics.py. The file prelim_code.py contains code used to run preliminary simulations presented in the original conference paper (in press). The file graph_analysis.py contains code used to analyze contact graphs and plotting.py contains mostly helper methods to produce the plots used in the extended publication.

In the output directory, we provide results of simulations used to produce the plots in the extended publication (and those plots).

Heuristic strategies

Heuristics create strategies by selecting vertices per turn greedily with respect to:

1. Highest degree
2. Lowest cost
3. Highest threat (proximity to fire)

We also implement a strategy that defends randomly for comparison.

Cost functions

We implemented the following cost functions:

1. Uniform - defaults to 1 for all vertices
2. Uniformly random - costs chosen uniformly at random from the specified or default range [1,5]
3. Binary hesitation - costs are 2 with probability given probability (defaults to 29.72%, to model average rate of vaccine hesitancy reported by Pourrazavi et al. [2]), 1 otherwise
4. Threat with stochasticity - costs are proportional to the threat of the vertex (proximity to fire), with random noise added to each cost by adding an integer from the specified range

About the Network Data Files

Information about the network data files and how they are currently formatted.

Formatting

Files are saved as CSV files. They can have headings, although the actual names are ignored.

This repo contains two files used in the publication

Raccoons

• Filename: mammalia-raccoon-proximity.csv
• From: Network Data Repository, original source
• Used in citation: Reynolds, Jennifer JH, et al. "Raccoon contact networks predict seasonal susceptibility to rabies outbreaks and limitations of vaccination." Journal of Animal Ecology 84.6 (2015): 1720-1731.
• Extra data:
  • Third column: weights for edges
  • Fourth column edge timestamps.
• Nodes: 24
• Edges:
• Density:
• Max degree:
• Min degree:
• Avg degree:
• Assortativity:

Lizard Social Network

Sleepy Lizard population in Australia. Two lizards were assumed to had made a social contact if they were within 2 m of each other at any of the synchronized 10 min GPS locations.

• Filename: reptilia-lizard-network-social.csv
• From: Network Data Repository, original source
• Used in citation: Bull, C. M., S. S. Godfrey, and D. M. Gordon. "Social networks and the spread of Salmonella in a sleepy lizard population." Molecular Ecology 21.17 (2012): 4386-4392.
  • Abstract: "Although theoretical models consider social networks as pathways for disease transmission, strong empirical support, particularly for indirectly transmitted parasites, is lacking for many wildlife populations. We found multiple genetic strains of the enteric bacterium Salmonella enterica within a population of Australian sleepy lizards (Tiliqua rugosa), and we found that pairs of lizards that shared bacterial genotypes were more strongly connected in the social network than were pairs of lizards that did not. In contrast, there was no significant association between spatial proximity of lizard pairs and shared bacterial genotypes. These results provide strong correlative evidence that these bacteria are transmitted from host to host around the social network, rather than that adjacent lizards are picking up the same bacterial genotype from some common source."
• Nodes: 60
• Edges: 318
• Density: 0.179661
• Maximum degree: 23
• Minimum degree: 2
• Average degree: 10
• Assortativity: 0.350035

References

[1]: Finbow, S. and MacGillivray, G. The Firefighter Problem: a survey of results, directions and questions. Australasian Journal of Combinatorics. 43 (2009): 57-78.

[2]: Pourrazavi S., Fathifar Z., Sharma M., Allahverdipour H. COVID-19 vaccine hesitancy: A Systematic review of cognitive determinants. Health Promotion Perspect. 2023 Apr 30;13(1):21-35. doi: 10.34172/hpp.2023.03. PMID: 37309435; PMCID: PMC10257562.