Heterogeneous agent-based models (HABMs) can simulate the dynamics of multiple types of entities and their interactions on contact networks. In recent years, they have gathered great interest and are widely applied in multiple fields, such as personalized recommendations, publication ranking, and epidemic modeling. Nevertheless, conventional HABMs on graphs can only capture pair-wise interactions between agents but fail to capture the more complex dynamics of group interactions (e.g., multiple people in the same location simultaneously), consequently leading to suboptimal performance. To address this, we propose using hypergraphs to capture such group interactions better and extend the current graph-based HABMs to hypergraphs. Specifically, we use MRSA (Methicillin-resistant Staphylococcus aureus, a kind of infectious disease acquired by patients during treatment at healthcare facilities) spread in the University of Virginia hospital as an example to showcase how we extend an existing graph-based HABM, Graph-HeterSIS, to a hypergraph-based HABM (H2ABM), Hypergraph-HeterSIS. We show how the hyper-graphs can capture the structural difference between contacts before and during the first wave of COVID-19 outbreak in Virginia better than graphs. Our experiments show that H2ABM better captures the underlying group interactions and better fits and forecasts MRSA cases.

Only a subset of infections is actually observed in an outbreak, due to multiple reasons such as asymptomatic cases and under-reporting. Therefore, reconstructing an epidemic cascade given some observed cases is an important step in responding to such an outbreak. A maximum likelihood solution to this problem ( referred to as CascadeMLE ) can be shown to be a variation of the classical Steiner subgraph problem, which connects a subset of observed infections. In contrast to prior works on epidemic reconstruction, which consider the standard Steiner tree objective, we show that a solution to CascadeMLE, based on the actual MLE objective, has a very different structure. We design a logarithmic approximation algorithm for CascadeMLE, and evaluate it on multiple synthetic and social contact networks, including a contact network constructed for a hospital. Our algorithm has significantly better performance compared to a prior baseline.

The efficient detection of outbreaks and other cascading phenomena is a fundamental problem in a number of domains, including disease spread, social networks, and infrastructure networks. In such settings, monitoring and testing a small group of pre-selected nodes from the susceptible population (i.e., a sensor set) is often the preferred testing regime. We study the problem of selecting a sensor set that minimizes the delay in detection---we refer to this as the MinDelSS problem. Prior methods for minimizing the detection time rely on greedy algorithms using submodularity. We show that this approach can sometimes lead to a worse approximation for minimizing the detection time than desired. We also show that MinDelSS is hard to approximate within an O(n^(1-1/g))-factor for any constant g greater than or equal to 2 for a graph with n nodes. This instead motivates seeking a bicriteria approximations. We present the algorithm RoundSensor, which gives a rigorous worst case O(log(n))-factor for the detection time, while violating the budget by a factor of O(log^2(n)). Our algorithm is based on the sample average approximation technique from stochastic optimization, combined with linear programming and rounding. We evaluate our algorithm on several networks, including hospital contact networks, which validates its effectiveness in real settings.