Group testing is the technique of pooling together diagnostic samples in order to increase the efficiency of medical testing. Traditionally, works in group testing assume that the infections are i.i.d. However, contagious diseases like COVID-19 are governed by community spread and hence the infections are correlated. This survey presents an overview of recent research progress that leverages the community structure to further improve the efficiency of group testing. We show that taking into account the side-information provided by the community structure may lead to significant savings—up to 60% fewer tests compared to traditional test designs. We review lower bounds and new approaches to encoding and decoding algorithms that take into account the community structure and integrate group testing into epidemiological modeling. Finally, we also discuss a few important open questions in this space