Modeling Epidemics On Different Networks : Insights into Testing Strategies

Abstract: Modeling epidemics is of great interest because it allows us to anticipate and investigate what countermeasures like testing are more effective in slowing down the spread of a disease. In traditional mathematical modeling techniques, a society is represented as a singular entity where everyone can interact with everyone. Instead of using the traditional non-network models, the use of network models can capture a more realistic social structure. By simulating a population as a network where each individual has a number of connections to other people in the network, our goal is to investigate what testing strategies are more effective, but also how the underlying network structure affects disease spread and testing countermeasures. Despite using a simplified model the results we have obtained could have implications for future research and work. Our key findings show contact tracing seems to be more effective on networks that are divided into smaller cliques connected to each other than networks that are less clustered. Targeted testing is also more effective when there is more variance in node degree in the network. We can also conclude that testing should be concentrated around the peak of the infection rather than spread over a longer time period, however, doing many tests too early could have the effect of just postponing the infection peak.