Modeling infection spread during pandemics is not new, with models using past data to tune simulation parameters for predictions. These help in understanding of the healthcare burden posed by a pandemic and responding accordingly. However, the problem of how college/university campuses should function during a pandemic is new for the following reasons: (i) social contact in colleges are structured and can be engineered for chosen objectives; (ii) the last pandemic to cause such societal disruption was more than 100 years ago, when higher education was not a critical part of society; (iii) not much was known about causes of pandemics, and hence effective ways of safe operations were not known; and (iv) today with distance learning, remote operation of an academic institution is possible. As one of the first to address this problem, our approach is unique in presenting a flexible simulation system, containing a suite of model libraries, one for each major component. The system integrates agent-based modeling and the stochastic network approach, and models the interactions among individual entities (e.g., students, instructors, classrooms, residences) in great detail. For each decision to be made, the system can be used to predict the impact of various choices, and thus enables the administrator to make informed decisions. Although current approaches are good for infection modeling, they lack accuracy in social contact modeling. Our agent-based modeling approach, combined with ideas from Network Science, presents a novel approach to contact modeling. A detailed case study of the University of Minnesota’s Sunrise Plan is presented. For each decision made, its impact was assessed, and results were used to get a measure of confidence. We believe that this flexible tool can be a valuable asset for various kinds of organizations to assess their infection risks in pandemic-time operations, including middle and high schools, factories, warehouses, and small/medium-sized businesses.

Modeling infection spread during pandemics is not new, with models using past data to tune simulation parameters for predictions. These help understand the healthcare burden posed by a pandemic and respond accordingly. However, the problem of how college/university campuses should function during a pandemic is new for the following reasons: (i) social contact in colleges are structured and can be engineered for chosen objectives, (ii) the last pandemic to cause such societal disruption was over 100 years ago, when higher education was not a critical part of society, (ii) not much was known about causes of pandemics, and hence effective ways of safe operations were not known, and (iii) today with distance learning, remote operation of an academic institution is possible. As one of the first to address this problem, our approach is unique in presenting a flexible simulation system, containing a suite of model libraries, one for each major component. The system integrates agent based modeling (ABM) and stochastic network approach, and models the interactions among individual entities, e.g., students, instructors, classrooms, residences, etc. in great detail. For each decision to be made, the system can be used to predict the impact of various choices, and thus enable the administrator to make informed decisions. While current approaches are good for infection modeling, they lack accuracy in social contact modeling. Our ABM approach, combined with ideas from Network Science, presents a novel approach to contact modeling. A detailed case study of the University of Minnesota's Sunrise Plan is presented. For each decisions made, its impact was assessed, and results used to get a measure of confidence. We believe this flexible tool can be a valuable asset for various kinds of organizations to assess their infection risks in pandemic-time operations, including middle and high schools, factories, warehouses, and small/medium sized businesses.

Decision case education is becoming increasingly important in agriculture. However, use of decision cases within extension education has lagged behind that in resident education. One serious constraint to the use of cases in extension education is the lack of a sufficient number of cases suited to the particular needs and demands of extension. The Perkins Farm case was developed specifically for extension audiences. The case concerns the Perkins' farm management decision whether to purchase a larger row-crop planter and associated equipment to improve efficiency and save time in the field. Complications in the decision include uncertainties about the Perkins' future in farming and the implications of the larger equipment for their son, should he assume operation of the farm in the future. The case is formatted in two easily assimilated segments to make it possible for learners to discuss the case even with little opportunity to prepare prior to the session. The case also includes a two-part video that enhances the capability of learners to identify with the decision makers and their situation. The Perkins Farm case was developed to increase understanding of farming and sustainable agriculture issues with particular emphasis on profitability, quality of life, and the environment. The case also helps learners to become better informed about sustainable agriculture techniques and approaches, and improves their decision-making skills. A lesson plan for using the case with extension audiences is described.