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1. Optimal Two-Tier Outpatient Care Network Redesign With a Real-World Case Study of Shanghai

   https://doi.org/10.1109/TASE.2022.3198484  

   Deng, Yewen; Li, Na; Kong, Nan; Jiang, Zhibin; Xie, Xiaoqing (September 2022, IEEE Transactions on Automation Science and Engineering)

   Healthcare capacity shortage contributes to poor access in many countries. Moreover, rapid urbanization often occurring in these countries has exacerbated the imbalance between healthcare capacity and need. One way to address the above challenge is expanding the total capacity and redistributing the capacity spatially. In this research, we studied the problem of locating new hospitals in a two-tier outpatient care system comprising multiple central and district hospitals, and upgrading existing district hospitals to central hospitals. We formulated the problem with a discrete location optimization model. To parameterize the optimization model, we used a multinomial logit model to characterize individual patients’ diverse hospital choice and to quantify the patient arrival rates at each hospital accordingly. To solve the hard nonlinear combinatorial optimization problem, we developed a queueing network model to approximate the impact of hospital locations on patient flows. We then proposed a multi-fidelity optimization approach, which involves both the aforementioned queuing network model as a surrogate and a self-developed stochastic simulation as the high-fidelity model. With a real-world case study of Shanghai, we demonstrated the changes in the care network and examined the impacts on the network design by population center emergence, governmental budget change and considering patients with different age groups or income levels. Note to Practitioners —Our work focuses on improving system-wide care access in a two-tier care network. We believe that our work can lead to effective development of a location analytics tool for city-wide healthcare system planners. We also think the importance of this study is further strengthened by the case studies based on real-world hospital choice experimental data from Shanghai, China, a region suffering from the imbalance between healthcare capacity and need. Our case studies are expected to make recommendations on care facility expansion and dispersion to better align with the spatial distribution of residential communities and patient hospital choice behavior. 

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2. Finding Your Way: Shortest Paths on Networks

   https://doi.org/10.3389/frym.2021.631045  

   Rexin, Teresa; Porter, Mason A. (December 2021, Frontiers for Young Minds)

   Traveling to different destinations is a major part of our lives. We visit a variety of locations both during our daily lives and when we are on vacation. How can we find the best way to navigate from one place to another? Perhaps we can test all of the different ways of traveling between two places, but another method is to use mathematics and computation to find a shortest path between them. In this article, we discuss how to construct shortest paths and introduce Dijkstra’s algorithm to minimize the total cost of a path, where the cost may be the travel distance, the travel time, or some other quantity. We also discuss how to use shortest paths in the real world to save time and increase traveling efficiency. 

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3. Map-matching using shortest paths

   https://doi.org/10.1145/3191801.3191812  

   Chambers, Erin; Fasy, Brittany Terese; Wang, Yusu; Wenk, Carola (January 2018, IWISC)

   We consider several variants of the map-matching problem, which seeks to find a path Q in graph G that has the smallest distance to a given trajectory P (which is likely not to be exactly on the graph). In a typical application setting, P models a noisy GPS trajectory from a person traveling on a road network, and the desired path Q should ideally correspond to the actual path in G that the person has traveled. Existing map-matching algorithms in the literature consider all possible paths in G as potential candidates for Q. We find solutions to the map-matching problem under different settings. In particular, we restrict the set of paths to shortest paths, or concatenations of shortest paths, in G. As a distance measure, we use the Fréchet distance, which is a suitable distance measure for curves since it takes the continuity of the curves into account. 

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4. Investigating post-earthquake hospital transportation for casualties through agent-based modeling

   https://doi.org/10.1177/87552930251328386  

   Mesta, Carlos; Ceferino, Luis; Cremen, Gemma; Galasso, Carmine (April 2025, Earthquake Spectra)

   We develop a methodology for modeling post-earthquake hospital needs and accessibility, coupling a probabilistic regional casualty risk model with a novel agent-based human behavior model. We use it to study hospital transportation in two socio-economically distinct districts of Lima, Peru, after a hypothetical moment magnitude 8.0 event. Our work provides insights into the infrastructure and socio-economic vulnerability factors that determine timely healthcare access, across various hypothetical behavioral and policy contexts/scenarios. In the baseline scenario that assumes households respond to the emergency independently (i.e. transport only their own members to hospital) but there is coordination among hospitals (i.e. ambulance sharing), car ownership is key for rapid healthcare access. Twenty-four percent of non-life-threatening casualties in the district with 57% car ownership will arrive at hospital in the first 45 min; only 6% of such patients in the more economically deprived district (18% car ownership) will reach hospital in that time. If car-owning neighbors help transporting patients without cars, over 96% of non-life-threatening casualties will reach hospital in the first 45 min in both districts, significantly reducing intra-regional disparities in healthcare access. Arrival times would nearly halve compared to the baseline scenario if the number of available ambulances doubled. If hospitals do not share ambulances, arrival times of non-life-threatening and threatening casualties show a notable reduction in one district (by 56% and 42%, respectively) but a significant increase in the other, more economically deprived district (by 109% and 97%, respectively), compared to the baseline case. The arrival times presented in this study are specific to the studied context, and the appropriate assumptions made. We benchmark the performance of the methodology using patient arrival times from the 2023 Turkey-Syria earthquake, yielding an acceptable outcome (i.e. similar temporal trends in the proportion of hospital arrivals). The findings can be leveraged for planning effective post-earthquake emergency response strategies. 

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5. Toward a String-Pulling Approach to Path Smoothing on Grid Graphs

   Han, J.; Uras, T.; Koenig, S. (January 2020, Symposium on Combinatorial Search (SoCS))

   Paths found on grid graphs are often unrealistic looking in the continuous environment that the grid graph represents and often need to be smoothed after a search. The well-known algorithm for path smoothing is greedy in nature and does not necessarily eliminate all heading changes in freespace. We present preliminary research toward a new path-smoothing algorithm based on 'string pulling' and show experimentally that it consistently finds shorter paths than the greedy path-smoothing algorithm and produces paths with no heading changes in freespace. 

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