During the first half of 2020, the lives of people around the world abruptly changed due to COVID-19. Data visualisations and models related to the spread of the disease became ubiquitous. In this paper, we survey 25 different data analytics dashboards, highlight the modelling approach taken by each, and develop a multi-attribute utility theory model to assess their effectiveness in communicating key features that explain the spread of infectious disease. We show that the dashboards that feature dimensions that span the categories associated with compartmental epidemiology models tend to be relatively robust data visualisations, and we highlight that information systems need to be improved to include data on actions to reduce the spread of the disease. We analyse the actions taken by countries around the world and show that when governments employ strict measures early, particularly those that enforce social distancing and include widespread testing and comprehensive contact tracing, they are more likely to experience better outcomes. Recommendations for how countries should respond in future pandemics are detailed.

We introduce a generalized Orienteering Problem where, as usual, a vehicle is routed from a prescribed start node, through a directed network, to a prescribed destination node, collecting rewards at each node visited, in order to maximize the total reward along the path. In our generalization, transit on arcs in the network and reward collection at nodes both consume a variable amount of the same limited resource. We exploit this resource trade-off through a specialized branch-and-bound algorithm that relies upon partial path relaxation problems which often yield tight bounds and lead to substantial pruning in the enumeration tree. We present the Smuggler Search Problem as an important real-world application of our generalized Orienteering Problem. Numerical results show that our algorithm applied to the Smuggler Search Problem outperforms standard Mixed-Integer Nonlinear Programming solvers for moderate to large problem instances. We demonstrate model enhancements that allow practitioners to represent realistic search planning scenarios by accounting for multiple heterogeneous searchers and complex smuggler motion.

We introduce a generalized Orienteering Problem where, as usual, a vehicle is routed from a prescribed start node, through a directed network, to a prescribed destination node, collecting rewards at each node visited, in order to maximize the total reward along the path. In our generalization, transit on arcs in the network and reward collection at nodes both consume a variable amount of the same limited resource. We exploit this resource trade-off through a specialized branch-and-bound algorithm that relies upon partial path relaxation problems which often yield tight bounds and lead to substantial pruning in the enumeration tree. We present the Smuggler Search Problem as an important real-world application of our generalized Orienteering Problem. Numerical results show that our algorithm applied to the Smuggler Search Problem outperforms standard Mixed-Integer Nonlinear Programming solvers for moderate to large problem instances. We demonstrate model enhancements that allow practitioners to represent realistic search planning scenarios by accounting for multiple heterogeneous searchers and complex smuggler motion.

International law enforcement organizations around the world endeavor to combat high drug related mortality rates by seizing illicit drugs in transit over international waters. This mission requires effective plans that route multiple aerial searchers and position surface interdictors through large expanses of geographical areas in the presence of highly uncertain estimates about drug smuggler whereabouts. This high uncertainty combined with the challenge of coordinating search and interdiction make it particularly difficult to conduct mission planning. We present optimal search and interdiction models that address these important challenges and demonstrate how planners can used these models by applying them to a realistic counterdrug operation scenario.

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