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1. Discrete-Event Simulation in Healthcare Settings: A Review

   https://doi.org/10.3390/modelling3040027  

   Forbus, John J. ; Berleant, Daniel ( December 2022 , Modelling)

   We review and define the current state of the art as relating to discrete event simulation in healthcare-related systems. A review of published literature over the past five years (2017–2021) was conducted, building upon previously published work. PubMed and EBSCOhost were searched for journal articles on discrete event simulation in healthcare resulting in identification of 933 unique articles. Of these about half were excluded at the title/abstract level and 154 at the full text level, leaving 311 papers to analyze. These were categorized, then analyzed by category and collectively to identify publication volume over time, disease focus, activity levels by country, software systems used, and sizes of healthcare unit under study. A total of 1196 articles were initially identified. This list was narrowed down to 311 for systematic review. Following the schema from prior systematic reviews, the articles fell into four broad categories: health care systems operations (HCSO), disease progression modeling (DPM), screening modeling (SM), and health behavior modeling (HBM). We found that discrete event simulation in healthcare has continued to increase year-over-year, as well as expand into diverse areas of the healthcare system. In addition, this study adds extra bibliometric dimensions to gain more insight into the details and nuances of how and where simulation is being used in healthcare. 

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2. Assessment of Spatio-Temporal Empirical Forecasting Performance of Future Shoreline Positions

   https://doi.org/10.3390/rs14246364  

   Islam, Md Sariful ; Crawford, Thomas W. ( December 2022 , Remote Sensing)

   Coasts and coastlines in many parts of the world are highly dynamic in nature, where large changes in the shoreline position can occur due to natural and anthropogenic influences. The prediction of future shoreline positions is of great importance in the better planning and management of coastal areas. With an aim to assess the different methods of prediction, this study investigates the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Multi-temporal Landsat imagery, from 1988 to 2021, was used to quantify the rates of shoreline movement for different time period. Predictions using the simple extrapolation of the end point rate (EPR), linear regression rate (LRR), weighted linear regression rate (WLR), and the Kalman filter method were used to predict future shoreline positions. Root mean square error (RMSE) was used to assess prediction accuracies. For time depth, our results revealed that the higher the number of shorelines used in calculating and predicting shoreline change rates the better predictive performance was yielded. For the time horizon, prediction accuracies were substantially higher for the immediate future years (138 m/year) compared to the more distant future (152 m/year). Our results also demonstrated that the forecast performance varied temporally and spatially by time period and region. Though the study area is located in coastal Bangladesh, this study has the potential for forecasting applications to other deltas and vulnerable shorelines globally. 

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3. Aging in Flood-Prone Coastal Areas: Discerning the Health and Well-Being Risk for Older Residents

   https://doi.org/10.3390/ijerph15122900  

   Bukvic, Anamaria ; Gohlke, Julia ; Borate, Aishwarya ; Suggs, Jessica ( December 2018 , International Journal of Environmental Research and Public Health)

   Coastal communities are increasingly exposed to more intense and frequent hurricanes, accelerated sea-level rise, and prolonged tidal inundation, yet they are often a preferred retirement destination for older adults vulnerable to flooding and extreme weather events. The unique physical and psychosocial challenges of older population age 65 and over may affect their level of preparedness, capacity to cope with, and ability to respond and recover from a hazard event. Despite the clear vulnerabilities of older residents living in high-risk areas when compared to younger coastal populations, there is a lack of empirical research on the integrated flood risks to this population group in the coastal context. This paper provides a holistic assessment of this emerging problem along the U.S. East Coast by measuring the exposure of older population to sea level rise and storm surge in coastal counties. It further evaluates how age-related vulnerabilities differ between rural and urban settings using the case study approach and geospatial and statistical analysis the paper also conducts a review of scientific literature to identify gaps in the current understanding of health and well-being risks to aging populations in coastal communities. The results show that older populations are unevenly distributed along the U.S. East Coast with some states and counties having significantly higher percent of residents age 65 and older living along the shoreline. Many places with larger older populations have other attributes that further shape the vulnerability of this age group such as older housing stock, disabilities, and lower income and that often differ between rural and urban settings. Lastly, our study found that vast majority of research on aging in high-risk coastal locations has been conducted in relation to major disasters and almost none on the recurrent nuisance flooding that is already affecting many coastal communities. 

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4. Coastal Typology: An Analysis of the Spatiotemporal Relationship between Socioeconomic Development and Shoreline Change

   https://doi.org/10.3390/land9070218  

   Mack, Elizabeth A. ; Theuerkauf, Ethan ; Bunting, Erin ( July 2020 , Land)

   null (Ed.)

   Globally, coastal communities are impacted by hazards including storm events, rising water levels, and associated coastal erosion. These hazards destroy homes and infrastructure causing human and financial risks for communities. At the same time, the economic and governance capacity of these communities varies widely, impacting their ability to plan and adapt to hazards. In order to identify locations vulnerable to coastal hazards, knowledge of the physical coastal changes must be integrated with the socio-economic profiles of communities. To do this, we couple information about coastal erosion rates and economic data in communities along the Great Lakes to develop a typology that summarizes physical and economic vulnerability to coastal erosion. This typology classifies communities into one of four categories: (1) High physical and economic vulnerability to coastal erosion, (2) High physical but low economic vulnerability to coastal erosion, (3) Low physical and low economic vulnerability to coastal erosion, and (4) High economic but low physical vulnerability to coastal erosion. An analysis of this typology over three time periods (2005–2010), (2010–2014), and (2014–2018) reveals the dynamic nature of vulnerability over this fourteen year time span. Given this complexity, it can be difficult for managers and decision-makers to decide where to direct limited resources for coastal protection. Our typology provides an analytical tool to proactively address this challenge. Further, it advances existing work on coastal change and associated vulnerability in three ways. One, it implements a regional, analytical approach that moves beyond case study-oriented work and facilitates community analyses in a comparative context. Two, the typology provides an integrated assessment of vulnerability that considers economic vulnerability to coastal erosion, which is a contextual variable that compounds or helps mitigate vulnerability. Three, the typology facilitates community comparisons over time, which is important to identifying drivers of change in Great Lakes coastal communities over time and community efforts to mitigate and adapt to these hazards. 

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5. What evidence exists on the performance of nature-based solutions interventions for coastal protection in biogenic, shallow ecosystems? A systematic map protocol

   https://doi.org/10.1186/s13750-023-00303-4  

   Paxton, Avery B. ; Riley, Trevor N. ; Steenrod, Camille L. ; Smith, Carter S. ; Zhang, Y. Stacy ; Gittman, Rachel K. ; Silliman, Brian R. ; Buckel, Christine A. ; Viehman, T. Shay ; Puckett, Brandon J. ; et al ( May 2023 , Environmental Evidence)

   Anthropogenic pressures and climate change threaten the capacity of ecosystems to deliver a variety of services, including protecting coastal communities from hazards like flooding and erosion. Human interventions aim to buffer against or overcome these threats by providing physical protection for existing coastal infrastructure and communities, along with added ecological, social, or economic co-benefits. These interventions are a type of nature-based solution (NBS), broadly defined as actions working with nature to address societal challenges while also providing benefits for human well-being, biodiversity, and resilience. Despite the increasing popularity of NBS for coastal protection, sometimes in lieu of traditional hardened shorelines (e.g., oyster reefs instead of bulkheads), gaps remain in our understanding of whether common NBS interventions for coastal protection perform as intended. To help fill these knowledge gaps, we aim to identify, collate, and map the evidence base surrounding the performance of active NBS interventions related to coastal protection across a suite of ecological, physical, social, and economic outcomes in salt marsh, seagrass, kelp, mangrove, shellfish reef, and coral reef systems. The resulting evidence base will highlight the current knowledge on NBS performance and inform future uses of NBS meant for coastal protection.

   Searches for primary literature on performance of NBS for coastal protection in shallow, biogenic ecosystems will be conducted using a predefined list of indexing platforms, bibliographic databases, open discovery citation indexes, and organizational databases and websites, as well as an online search engine and novel literature discovery tool. All searches will be conducted in English and will be restricted to literature published from 1980 to present. Resulting literature will be screened against set inclusion criteria (i.e., population, intervention, outcome, study type) at the level of title and abstract followed by full text. Screening will be facilitated by a web-based active learning tool that incorporates user feedback via machine learning to prioritize articles for review. Metadata will be extracted from articles that meet inclusion criteria and summarized in a narrative report detailing the distribution and abundance of evidence surrounding NBS performance, including evidence clusters, evidence gaps, and the precision and sensitivity of the search strategy.

    

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