Large scale screening is a critical tool in the life sciences, but is often limited by reagents, samples, or cost. An important manifestation is the ongoing effort to achieve widespread testing for individuals with SARS-CoV-2 infection in the face of substantial resource constraints. Group testing methods utilize limited testing resources more efficiently by pooling specimens together, potentially allowing larger populations to be screened with fewer tests. A key challenge is to design effective pooling strategies. The global nature of the ongoing pandemic calls for designs that use tests efficiently while also remaining simple (to aid implementation) and flexible (so it can be tailored for different settings). This talk presents HYPER, a new pooled testing method based on hypergraph factorization. HYPER is designed to be easy to implement and adapt, while also producing pools that are balanced and efficient. We will discuss what hypergraph factorizations are and how they generate the pooling designs used in HYPER. Evaluation in theory and simulation highlight the benefit of a balanced and flexible design when faced with diverse settings and varying resource constraints.