Summary

In this chapter, we explored dynamic networks and how changes in network structure over time impact epidemic spread using examples of disease spread among crocodile and blue heron populations. We also explored the relationship between network metrics and epidemic spread, noting patterns of connectivity associated with higher rates of spread and more severe epidemics within a hypothesized population. These tools are critical to understanding trends and epidemics spread across real-world networks, allowing researchers to plan out critical infrastructure to deal with crises such as COVID-19 or Ebola in human populations, infectious diseases threatening endangered animal populations, fake news before a country’s elections, or dangerous behavioral trends spreading among youth on social media.

In the next chapter, we will shift focus to machine learning (ML) on networks.