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POD (Point of Dispensing)-based emergency response plans involving mass prophylaxis may seem feasible when considering the choice of dispensing points within a region, overall population density, and estimated traffic demands. However, the plan may fail to serve particular vulnerable sub-populations, resulting in access disparities during emergency response. Federal authorities emphasize on the need to identify sub-populations that cannot avail regular services during an emergency due to their special needs to ensure effective response. Vulnerable individuals require the targeted allocation of appropriate resources to serve their special needs. Devising schemes to address the needs of vulnerable sub-populations is essential for the effectiveness of response plans. This research focuses on data-driven computational methods to quantify and address vulnerabilities in response plans that require the allocation of targeted resources. Data-driven methods to identify and quantify vulnerabilities in response plans are developed as part of this research. Addressing vulnerabilities requires the targeted allocation of appropriate resources to PODs. The problem of resource allocation to PODs during public health emergencies is introduced and the variants of the resource allocation problem such as the spatial allocation, spatio-temporal allocation and optimal resource subset variants are formulated. Generating optimal resource allocation and scheduling solutions can be computationally hard problems. The application of metaheuristic techniques to find near-optimal solutions to the resource allocation problem in response plans is investigated. A vulnerability analysis and resource allocation framework that facilitates the demographic analysis of population data in the context of response plans, and the optimal allocation of resources with respect to the analysis are described.

Water is a vital resource for species inhabiting arid and semi-arid regions and can shape the biotic communities that we observe. Because water is considered a limiting resource for many species in desert environments, there is the potential for competitive interactions between species to occur at or around water sources. For this dissertation I tested hypotheses related to resource competition among different species of wildlife in the Great Basin and Mojave Deserts of western Utah. Chapter one evaluated the influence of feral horses (Equus caballus) on patterns of water use by communities of native birds and mammals. Chapter two determined if feral horses competed with pronghorn (Antilocapra americana) and mule deer (Odocoileus hemionus) for access to water. In chapters one and two, we found evidence that horses compete with native wildlife for water. In chapter one, horses were associated with decreased richness and diversity of native species at water sources. Native species also had fewer visits and spent less time at water sources frequented by horses. In chapter two, we found that pronghorn and mule deer used water sources less often where horse activity was high. There were also significant differences in temporal activity for pronghorn, but not mule deer, at horse-occupied sites versus sites where horses were absent or uncommon. Our results indicated that horses spatially and temporally displaced other species at water sources providing evidence of a negative influence on how communities of native wildlife access a limited resource in an arid environment. Chapter three assessed whether dominant carnivores (coyote (Canis latrans) and bobcat (Lynx rufus)) negatively influenced the spatial use of water sources by the subordinate kit fox (Vulpes macrotis). Our results did not reveal strong negative associations between kit fox visits to water sources and visits by dominant carnivores; in fact, dominant carnivores contributed very little to the use of water by kit foxes. Instead, kit fox visits were more closely associated with habitat features at water sources. Our findings indicate that dominant carnivores are not the primary driver of use of water sources by subordinate carnivores. Chapter four evaluated whether a simulated loss of water due to climate change/increased human use would differentially affect desert bats based on flight morphology and maneuverability. When we experimentally reduced surface area of water sources, larger, less-maneuverable bats experienced a 69% decrease in drinking success and increased competition with smaller, maneuverable bats. Anticipated reductions in the sizes of water sources due to climate change may lead to species with less maneuverability being unable to access water efficiently and facing increased competition from more agile bats.

City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. 3D speed maps have been shown to be a promising methodology for revealing day-to-day regularities of city-level travel times and possibly also for short-term prediction. In this paper, we aim to further evaluate and benchmark the use of 3D speed maps for short-term travel time prediction and to enable scenario-based evaluation of traffic management actions we also evaluate the framework for traffic flow prediction. The 3D speed map methodology is adapted to short-term prediction and benchmarked against historical mean as well as against Probabilistic Principal Component Analysis (PPCA). The benchmarking and analysis are made using one year of travel time and traffic flow data for the city of Stockholm, Sweden. The result of the case study shows very promising results of the 3D speed map methodology for short-term prediction of both travel times and traffic flows. The modified version of the 3D speed map prediction outperforms the historical mean prediction as well as the PPCA method. Further work includes an extended evaluation of the method for different conditions in terms of underlying sensor infrastructure, preprocessing and spatio-temporal aggregation as well as benchmarking against other prediction methods. / <p>QC 20190531</p>