This dissertation contributes to a methodology and a better understanding that can be used to study the effects of strategies during a pandemic, especially in multi-community networks. The dissertation is structured as the following: In the first chapter, we introduce the concept of networks and its properties, and node and link percolation, which is an important process embedded in networks. Then we discuss different epidemic models, among which the SIR model is representative of many infectious diseases, and can also be mapped into a link percolation problem. We bring up two quantities that are most important in evaluating the effectiveness of epidemic strategies, one is the total fraction of individuals ever been infected by the final steady state of the SIR model, the other is the peak fraction of infected throughout the process, the second of which has seldom been studied before. There have been many researches on epidemic models within isolated networks, but recently people start getting more interested in network of networks, due to its better representation of real world systems. So we study those two quantities and their dependence on the fraction of bridge nodes in multi-community networks, in the second and third chapters: In the second chapter, we look at the final steady state of the SIR (Susceptible-Infected-Recovered) model, which can be mapped as one cluster in a link percolation problem. Using the scaling relations for the cluster size distributions around the critical point within isolated networks, we find multiple regimes in a network with two communities so that the total fraction of individuals ever been infected asymptotically follows different power laws with the fraction of bridge nodes within each regime. We also find crossovers between neighbor regimes so that the power law exponent changes from one regime to the other. It is interesting to note that the power-law relations get steeper in regimes with smaller transmissibilities, so those epidemic strategies that reduce connections between communities are more effective in those regimes. In the third chapter, we look at the peak fraction of infected of the SIR model, which also shows power law relations with the fraction of bridge nodes in different regimes, as well as crossovers between regimes. We also find that the power-law relation for the peak fraction of infected with the fraction of bridge nodes is steeper than the one for the total fraction of individuals ever been infected in the same regime, which indicates that the peak fraction of infected is more sensitive to strategies that reduce connections between communities. This explains why strategies to flatten the curve are usually taken when there are limited medical resources.