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The COVID-19 pandemic has created new workforce considerations for emergency management community in addressing cumulative and cascading disasters. This research identifies how emergency management planning for both the changing dynamics of COVID-19 and the upcoming hurricane season may change under a compound threat. Many jurisdictions have faced challenges in providing adequate staffing of shelters before the pandemic. Now, fatigue among staff further exacerbates these challenges as resources are stretched thin. Six workshops, involving 265 national, state, and local leaders, staff, experts, and advocates from 22 states, and a range of disciplines (disaster planning, public health, social services, academia, and healthcare), were convened to identify concerns and potential strategies to address staffing, training, logistics, and support. Strategies proposed to increase the number and skill set of staff available involve increased reliance upon volunteers and nonprofit organizations. Mental health resources, personal protective equipment, sanitation supplies, and defining roles within emergency shelters were recommended to reduce fatigue and redistribute responsibilities. Findings illuminate additional research avenues regarding assessing the underlying stressors contributing to the planning process and effective means of implementing these interventions to bolster emergency management shelter operations during a prolonged pandemic and in the future.

Adaptation to sea level rise (SLR) requires coordination among local, state, and federal entities and collaboration across governments, nonprofits, businesses, and residents. This coordination and ...

Severe catastrophic natural disasters adversely affect housing stock and regional capacity to build and repair houses, with unfavorable impacts on the business continuity of industrial and business organizations resting on that affected community. Restoring industrial capacity and business continuity is crucial for the organizations’ survival in the aftermath of a disaster but the process takes time while the affected region faces an unexpected surge in the demand for housing. Modeling approaches largely used in the supply chain management realm may support humanitarian logistics and policy-making by providing a testbed in which supply chain elements of the recovery process can be examined before implementing. This research presents a system dynamics model that considers the problem of housing recovery from the demand and supply perspective and provides significant insights for policymakers into how the production of permanent housing depends upon the uncertainties and feedback effects of material and labor. In order to highlight its utility, a hypothetical scenario in which a hurricane hits the U.S. Hampton Roads region, which is a major population center along the mid-Atlantic coast, has been configured by using actual data collected from both governmental and academic sources.

The logistics of public-sponsored evacuation include transportation assets, personnel, and infrastructure. Effective orchestration leading up to a severe weather event is a complex undertak...

Planning resiliency and sustainability of port operations and critical infrastructure requires risk assessment of storm surge exposure and potential sea level rise. An approach for rapid, screening-level assessment is developed to estimate the current and future risk of exposure to severe storm surges posed to marine terminal facilities in Norfolk, Virginia. The approach estimates the vertical elevation of local mean sea level fifty years into the future and attendant increases in potential storm surge heights. Inundation models are designed for baseline water levels and storm surges for category 1–3 hurricanes across five precautionary future sea level rise scenarios. In addition, tidal flooding poses an emerging threat because sea level rise will also force tides to higher elevations, suggesting that today’s extreme high tides may be the future mean high tide and today’s “nuisance” tidal flooding may in the future recur with chronic regularity. Potential tidal flooding levels are also modeled for each sea level scenario. This approach allows a port to assess relative risk tolerance across the range from lesser to more severe flooding events. Maps and tabular information in linked scenarios are used to summarize the extent, pattern, and depth of potential flooding. The methodology and data developed in this study may be applied to inform the timing and placement of planned assets and can be leveraged in the broader pursuit of optimization in support of long-term master planning at marine terminals.

Investment in transportation infrastructure has been widely utilized as an instrument for inducing economic growth. Such investment usually leads to job creation and an increase in per capita income that attracts population through migration to the region. This increases the utilization of the transport infrastructure over time, resulting in high levels of congestion. The congestion negatively impacts the attractiveness of the region and the gross regional product (GRP). Regions cyclically invest in transport infrastructure that temporally spurs economic activity and migration, and reduces congestion. This research employs a system dynamics simulation approach to capture and mimic the behavior of these complex and cyclical relationships over time. Our approach suggests the modeling of key demographic, transportation infrastructure, travel behavior, and economic activity components to determine the impact of infrastructure investments on regional growth. Given a set of prospective investment scenarios, the model replicates and projects levels of productivity, travel demand, congestion, GRP, and net migration patterns over time. The model also provides insights into the duration of critical cyclical patterns given these prospective infrastructure investments. The simulation model presented in this paper seeks to be utilized as guidance to support decision-making processes that lead to the execution of more exhaustive transportation studies that organize the execution of such investments.

This study aims to assess the vulnerability of populations to storm surge flooding in 12 coastal localities of Virginia, USA. Population vulnerability is assessed by way of 3 physical factors (elevation, slope, and storm surge category), 3 built-up components (road availability, access to hospitals, and access to shelters), and 3 household conditions (storm preparedness, financial constraints to recovering from severe weather events, and health fragility). Fuzzy analysis is used to generate maps illustrating variation in several types of population vulnerability across the region. When considering physical factors and household conditions, the most vulnerable neighborhoods to sea level rise and storm surge flooding are largely found in urban areas. However, when considering access to critical infrastructure, we find rural residents to be more vulnerable than nonrural residents. These detailed assessments can inform both local and state governments in catastrophic planning. In addition, the methodology may be generalized to assess vulnerability in other coastal corridors and communities. The originality is highlighted by evaluating socioeconomic conditions at refined scale, incorporating a broader range of human perceptions and predispositions, and employing a geoinformatics approach combining physical, built-up, and socioeconomic conditions for population vulnerability assessment. Integr Environ Assess Manag 2016;12:500-509. © 2015 SETAC.

U.S. regions are expected to follow the national trend towards investment in renewable energy as part of their electricity portfolio. The progress of energy portfolios that typically involves traditional methods, such as centralized nuclear and coal-fired generation, and towards cleaner- and renewable-source generation will impact economic growth and public health. Renewable electricity production must strike a balance among cost, reliability, and compatibility. The economic and health benefits obtained from developing an efficient energy portfolio make renewable energy alternatives an important consideration for regions endowed with natural resources. A portfolio mix of production method that considers the economic benefits while limiting adverse health and environmental impacts is attractive. This research proposes a System Dynamics simulation framework to support policy-making efforts in assessing the impact of energy portfolios. The authors demonstrate the utility of the framework by means of analyzing data that pertain to the U.S. Hampton Roads - Peninsula Region.

The quick recovery and rebuilding of the housing stock after disaster (natural or man-made) is critical for an affected region, from the socio-economic perspective. The recovery efforts demand a considerable amount of time and resources. An efficient emergency management response system needs to be designed, with the intention of allowing a smooth post disaster reconstruction operation. This paper seeks to analyze the impacts of a disaster on labor for housing recovery and rebuilding of a devastated region. A System Dynamics (SD) model is proposed to mimic and explore the issue of labor force (namely, construction workers) management. The model describes the behavior of labor in the housing restoration process. It provides insights on the interactions between the labor force and the housing inventory management.

Despite the non-trivial number of (non-institutionalized) residents with special needs - referred to as the medically fragile population in this paper - there is virtually no research available to guide the hurricane evacuation planning for this population group. One area is their evacuation time choice behavior that can have tremendous implications for their safety as well as the logistics of the evacuation process. In this paper, we fundamentally advance our understanding of the evacuation timing behavior of this heavily understudied, and potentially vulnerable, population group. Analysis indicates that key differences exist between the medically fragile and non-medically fragile population. Language: en

Understand housing recovery process after disaster from material resource viewpoint.System dynamics to study problem at reconstruction material supply level.Shows importance of timing in decision making for supplies in housing recovery.Results in anticipating demand requirements with uncertainties related to disaster. Background/purposeThe occurrence of catastrophic events proves disastrous as they cause significant physical damages, both at the human and material levels. Depending on the magnitude of the event, a natural phenomenon can potentially lead to loss of life, home destruction and alter the economic and social structures of the affected community. The purpose of this study is to gain a deeper insight into the housing recovery process following a catastrophic event from the material resources perspective. MethodA System Dynamics (SD) model is developed in this paper to study the problem at the reconstruction/repair material supply level in an affected area. The model describes the behavior of material resources in the housing reconstruction and recovery planning a catastrophic event. ResultsIt enables deeper understanding of the implications of the occurrence of a disaster on the housing material fluctuations. This model considers, due to the resources shortage created, the amount of material adjustments to make in the aftermath of a highly disruptive event. Theoretical results show satisfaction as the model displays expected results, reflecting the importance of timing in decision making for supplies, in the housing recovery progress. ContributionThe proposed model brings more insight into the types of the housing recovery and the material demands over time. It provides a means to anticipate the demand requirements and alleviate the population's suffering, considering the uncertainties associated with disaster.

This article discusses the dynamics inherent in modeling and simulation (M&S) standards development processes within the context of the competitive struggle for resources and market dominance. A conceptual framework is presented that provides the theoretical logic for the tension between inclusive, consensus-driven standards development processes and exclusive, preferential processes. Standards development is conceptualized as a porous process subject to both cooperative interests and competitive pressures. We elucidate how the tension between these two process ideals shapes the adoption and conformity of M&S standards and, in turn, how these processes facilitate broader system values. Normative questions are explicated about the proper tradeoffs among the desire to realize collective efficiencies gained through standards, the drive to create, own, and financially exploit new, emerging technologies through the concept of intellectual property, and the objective of economic development through fostering innovation. Both the organizational and individual incentives to engage in standards processes are discussed, together with how the politics of participation may shape standards development. This research contributes a compelling theoretical perspective not fully developed in the growing and changing discipline of M&S. The significance of exploring these questions is found in that there is a national interest in M&S standards development activities that contribute to our technological innovation and economic competitiveness.

The treatment and management of chronic diseases currently comprise a major fraction of the United States' healthcare expenditures. These expenses are projected to increase as the US population ages. Utilization of the ambulatory healthcare system stemming from chronic conditions has been seen as contributory factor in the rising expenditures. Efforts to better manage chronic conditions ought to result in better health outcomes and, by extension, savings through lower utilization of ambulatory services. The longer-term financial consequences of such interventions, however, are more uncertain. This study offers a System Dynamics simulation framework that identifies and models the critical relationships associated with health outcomes and longer-term financial consequences. This framework is demonstrated through a comparison between groups with a similar generic chronic condition, but one group is subjected to a management intervention and the other group is not. The framework provides constructive insights into how the initial intervention cost estimates, the resulting savings, and the health status may change depending on uncertainties, feedback effects, and cost structures.

Transportation has been identified as a major barrier to healthcare access, particularly, within vulnerable population groups. The level of healthcare access that most population segments have in traditional transit systems may be increased with new initiatives that involve complex and large investments in transit oriented developments (TOD) projects. However, the increasing attractiveness of neighborhoods affected by TOD initiatives may result in the gentrification of vulnerable population segments. These vulnerable segments are likely to be relocated into less attractive neighborhoods characterized by inadequate transit systems. This relocation increases the probabilities of reducing healthcare access for these underserved groups leading to an increase in health disparities. The present discussion calls for research to explore relevant factors that affects these dynamics. A framework that enables the identification of individual factors that affect gentrification processes under TOD initiatives as well as quantifying the effects from these processes is suggested in this paper. A system dynamics framework that allows the understanding of the dynamics associated with this system is suggested in this paper. Critical areas for empirical research are highlighted. These are prerequisites for the effective deployment of initiatives that ensure the mitigation of possible negative impacts on vulnerable populations.

Natural disaster events impact both the short- and long-term health of a region’s population. Due to variation in the vulnerability among population segments, a severe storm event can be expected over time to have a greater public health impact upon traditionally underserved and medically fragile populations. This research illustrates the causal relationships leading to a change over time in the prevalence of chronic conditions among black and non-black populations within U.S. Hampton Roads. Using a system dynamics approach, the authors develop and integrate a macro model that captures change in regional economic and demographic profiles with a micro model that focuses on access to health services and the ability to respond within the context of the changing regional environment. The authors’ study finds that: (1) the disparity in the prevalence of chronic conditions increases over time following the event, (2) the growth in health disparity may be slowed by regional resiliency intervention policies, and (3) mitigation efforts result in greater reductions in growth of chronic conditions among the black population relative the non-black general population. Knowledge of the disparate impact that such an event will have on the long-term health of underserved and medically fragile populations may be used to inform mitigation investments.

Chronic Disease is defined as a long lasting health condition, which can develop and/or worsen over an extended time, but which can also be controlled. The monetary and budgetary toll due to its persistent nature has become unsustainable and requires pressing actions to limit their incidence and burden. This paper demonstrates the utility of the System Dynamics approach to simulate the behavior of key factors involved in the implementation of chronic disease management. We model the patient flow diversion from emergency departments (ED) to patient-centered medical homes (PCMH), with emphasis on the visit rates, as well as the effect of insurance coverage, in an effort to assure continuity of quality care for Asthma patients at lower costs. The model is used as an evaluative method to identify conditions of a maintained health status through adequate policy planning, in terms of resources and capacity. This approach gives decision makers the ability to track the level of implementation of the intervention and generate knowledge about dynamics between population demands and the intervention effectiveness. The functionality of the model is demonstrated through the consideration of hypothetical scenarios executed using sensitivity analysis.

Background This research offers two exploratory frameworks, one for medical regimen compliance and one for medical immediacy. The first classifies compliance awareness, compliance mitigation, and financial limitation for those patients that exhibit nonadherence with a medical regimen. The second classifies medical immediacy and characterizes avoidable utilization. Methods Representative sampling of adult patients presenting at an emergency department (62,000/ppy) triaged as low acuity; emergency department physician assessment of noncompliance with medical regimen for those patients with a complaint related to a chronic condition; and emergency department physician assessment of medical immediacy and avoidable utilization. Results Physicians report 48.3% (95% confidence interval (CI) 43.5% to 53.1%) of patients with at least a single chronic condition are presenting with symptoms or complaint related to a chronic condition, and 39.6% (CI 31.7% to 47.4%) of these exhibit noncompliance with the medical regimen associated with that chronic condition. 16.4% (CI 6.6% to 26.1%) of the patients exhibit pseudo compliance, a belief that the medical regimen is in compliance when in fact it is not. If the patient had been in compliance, 85.9% (CI 77.0% to 94.8%) of the presenting conditions may have been mitigated. Noncompliance cases (34.5% (CI 22.0% to 47.1%)) are partly attributable to financial constraints. Further, 19.1% (CI 15.7% to 22.5%) are assessed as requiring no medical intervention and 3.4% (CI 1.8% to 4.9%) require immediate stabilization. Conclusions A large portion of low-acuity presentations are related to a chronic condition and noncompliance with the associated medical regimen contributes to the need to seek medical services. Interventions addressing literacy and financial constraints may increase compliance and decrease utilization.

Objectives First, to test a model of the drivers of frequent emergency department utilization conceptualized as falling within predisposing, enabling, and need dimensions. Second, to extend the model to include social networks and service quality as predictors of frequent utilization. Third, to illustrate the variation in thresholds that define frequent utilization in terms of the number of emergency department encounters by the predictors within the model. Data Source Primary data collection over an eight week period within a level-1 trauma urban hospital’s emergency department. Study Design Representative randomized sample of 1,443 adult patients triaged ESI levels 4–5. Physicians and research staff interviewed patients as they received services. Relationships with the outcome variable, utilization, were tested using logistic regression to establish odds-ratios. Principal Findings 70.6 percent of patients have two or more, 48.3 percent have three or more, 25.3 percent have four or more, and 14.9 percent have five or more emergency department visits within 12 months. Factors associated with frequent utilization include gender, race, poor mental health, mental health drugs, prescription drug abuse, social networks, employment, perceptions of service quality, seriousness of condition, persistence of condition, and previous hospital admittance. Conclusions Interventions targeting associated factors will change global emergency department encounters, although the mutability varies. Policy interventions to address predisposing factors such as substance abuse or access to mental health treatment as well as interventions that speak to enabling factors such as promoting the resiliency of social networks may result in decreased frequency of emergency department utilization.

Chronic conditions, disability limitations (mobility, cognitive, and sensory), and the need for assistance with activities of daily living are characteristics of elderly and medically fragile populations. Theory suggests that households with these vulnerability attributes are more likely to suffer storm-induced adverse and prolonged health consequences and, therefore, ought to evidence an increased propensity to evacuate prior to a severe storm event. Yet despite being more sensitive to storm disruption, the elderly and medically fragile populations are only slightly more likely to evacuate in the face of impending storms. This suggests, for these groups, there may be other factors such as income, transportation, and social and familial networks that may be attenuating the propensity to evacuate. The public health significance is found in that the propensity to shelter in place, rather than evacuate, may contribute to disparate health outcomes. Data illustrating the prevalence of these conditions and the propensity to shelter in place are derived from a sampling of Hampton Roads households following the 2011 Hurricane Irene. Language: en

This article demonstrates the utility of the system dynamics approach to model and simulate U.S. demand for ambulatory health care service both for the general population and for specific cohort subpopulations over the 5-year period, from 2003 to 2008. A system dynamics approach that is shown to meaningfully project demand for services has implications for health resource planning and for generating knowledge that is critical to assessing interventions.The study uses a cohort-component method in combination with structural modeling to simulate ambulatory health care utilization. Data are drawn from the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey.The simulation of the total population requiring ambulatory services between 2003 and 2008 is performed to test the functionality and validate the model. Results show a close agreement between the simulated and actual data; the percent error between the two is relatively low, 1.5% on average. In addition, simulations of purposively selected population subsets are executed (men, 18-24 years of age, white, African American, Hispanic, and insurance coverage), resulting in error between simulated and actual data, which is 7.05% on average.The proposed model demonstrates that it is possible to represent and mimic, with reasonable accuracy, the demand for health care services by the total ambulatory population and the demand by selected population subsets. This model and its simulation demonstrate how these techniques can be used to identify disparities among population subsets and a vehicle to test the impact of health care interventions on ambulatory utilization. A system dynamics approach may be a useful tool for policy and strategic planners.

This paper studies a stochastic inventory system exposed to demands that exhibit serial-correlation components. The relationship between different levels of serial-correlation and inventory cost components as well as reorder points and order quantities are analysed in this study. Understanding these relationships provides opportunities for managers to leverage logistic and cross-functional supply chain levers which may be used to inform managerial risk mitigation plans. We consider a lost-sale inventory system with continuous monitoring while applying an enhanced version of simulated annealing that incorporates pattern search, ranking and selection to generate sub-optimal solutions to this problem. We employ an experimental design methodology to quantify the magnitude and significance of main effects and interactions. The analysis of the variance demonstrates that holding costs and the order quantity become substantial and significant as the auto-correlation increases in the studied setting. Total costs and service levels are compared between solutions that ignore autocorrelation factors and those that account for these dependencies.