@article{rosenstrom_ivy_mayorga_swann_2024, title={COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina}, volume={46}, ISSN={["1878-0067"]}, url={https://doi.org/10.1016/j.epidem.2024.100752}, DOI={10.1016/j.epidem.2024.100752}, abstractNote={We document the evolution and use of the stochastic agent-based COVID-19 SIMu-lation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e., age, race/ethnicity, high-risk medical status), census tract-level interaction network, disease state network, agent behavior (i.e., masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), and variants. We describe its uses outside of the COVID-19 Scenario Modeling Hub (CSMH), which has focused on the interplay of nonpharmaceutical and pharmaceutical interventions, equitability of vaccine distribution, and supporting local county decision-makers in North Carolina. This work has led to multiple publications and meetings with a variety of local stakeholders. When COVSIM joined the CSMH in January 2022, we found it was a sustainable way to support new COVID-19 challenges and allowed the group to focus on broader scientific questions. The CSMH has informed adaptions to our modeling approach, including redesigning our high-performance computing implementation.}, journal={EPIDEMICS}, author={Rosenstrom, Erik T. and Ivy, Julie S. and Mayorga, Maria E. and Swann, Julie L.}, year={2024}, month={Mar} }
 @article{jung_loo_howerton_contamin_smith_carcelen_yan_bents_levander_espino_et al._2024, title={Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub}, volume={21}, ISSN={["1549-1676"]}, DOI={10.1371/journal.pmed.1004387}, abstractNote={Background Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). Methods and findings The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000–598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. Conclusions COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.}, number={4}, journal={PLOS MEDICINE}, author={Jung, Sung-mok and Loo, Sara L. and Howerton, Emily and Contamin, Lucie and Smith, Claire P. and Carcelen, Erica C. and Yan, Katie and Bents, Samantha J. and Levander, John and Espino, Jessi and et al.}, year={2024}, month={Apr} }
 @article{hamilton_morrow_davis_morgan_ivy_jiang_chi_hilliard_2024, title={Toward a More Diverse and Equitable Food Distribution System: Amplifying Diversity, Equity and Inclusion in Food Bank Operations}, ISSN={["1937-5956"]}, DOI={10.1177/10591478241252691}, abstractNote={This article provides an evidence-based discussion of an ongoing effort within the operations of hunger relief organizations to address diversity, equity, and inclusion (DEI) by sourcing and distributing more culturally relevant food. Through nearly 100 interviews with food bank personnel in diverse roles (from partner agency relations to executives) representing various regions of the United States, we explore the challenges faced by different functional units within the organization. These interviews indicate a shift to more inclusive language, more personalized metrics, and more inclusive operations. We critically analyze the related literature and identify opportunities for infusing DEI practices in the study of hunger relief supply chains.}, journal={PRODUCTION AND OPERATIONS MANAGEMENT}, author={Hamilton, Mikaya and Morrow, Benjamin F. and Davis, Lauren B. and Morgan, Shona and Ivy, Julie S. and Jiang, Steven and Chi, Min and Hilliard, Kyle}, year={2024}, month={May} }
 @article{howerton_contamin_mullany_qin_reich_bents_borchering_jung_loo_smith_et al._2023, title={Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty}, volume={14}, ISSN={["2041-1723"]}, DOI={10.1038/s41467-023-42680-x}, abstractNote={AbstractOur ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.}, number={1}, journal={NATURE COMMUNICATIONS}, author={Howerton, Emily and Contamin, Lucie and Mullany, Luke C. and Qin, Michelle and Reich, Nicholas G. and Bents, Samantha and Borchering, Rebecca K. and Jung, Sung-mok and Loo, Sara L. and Smith, Claire P. and et al.}, year={2023}, month={Nov} }
 @article{paramita_agor_mayorga_ivy_miller_ozaltin_2023, title={Quantifying association and disparities between diabetes complications and COVID-19 outcomes: A retrospective study using electronic health records}, volume={18}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0286815}, abstractNote={
Background
Despite established relationships between diabetic status and an increased risk for COVID-19 severe outcomes, there is a limited number of studies examining the relationships between diabetes complications and COVID-19-related risks. We use the Adapted Diabetes Complications Severity Index to define seven diabetes complications. We aim to understand the risk for COVID-19 infection, hospitalization, mortality, and longer length of stay of diabetes patients with complications.


Methods
We perform a retrospective case-control study using Electronic Health Records (EHRs) to measure differences in the risks for COVID-19 severe outcomes amongst those with diabetes complications. Using multiple logistic regression, we calculate adjusted odds ratios (OR) for COVID-19 infection, hospitalization, and in-hospital mortality of the case group (patients with diabetes complications) compared to a control group (patients without diabetes). We also calculate adjusted mean difference in length of stay between the case and control groups using multiple linear regression.


Results
Adjusting demographics and comorbidities, diabetes patients with renal complications have the highest odds for COVID-19 infection (OR = 1.85, 95% CI = [1.71, 1.99]) while those with metabolic complications have the highest odds for COVID-19 hospitalization (OR = 5.58, 95% CI = [3.54, 8.77]) and in-hospital mortality (OR = 2.41, 95% CI = [1.35, 4.31]). The adjusted mean difference (MD) of hospital length-of-stay for diabetes patients, especially those with cardiovascular (MD = 0.94, 95% CI = [0.17, 1.71]) or peripheral vascular (MD = 1.72, 95% CI = [0.84, 2.60]) complications, is significantly higher than non-diabetes patients. African American patients have higher odds for COVID-19 infection (OR = 1.79, 95% CI = [1.66, 1.92]) and hospitalization (OR = 1.62, 95% CI = [1.39, 1.90]) than White patients in the general diabetes population. However, White diabetes patients have higher odds for COVID-19 in-hospital mortality. Hispanic patients have higher odds for COVID-19 infection (OR = 2.86, 95% CI = [2.42, 3.38]) and shorter mean length of hospital stay than non-Hispanic patients in the general diabetes population. Although there is no significant difference in the odds for COVID-19 hospitalization and in-hospital mortality between Hispanic and non-Hispanic patients in the general diabetes population, Hispanic patients have higher odds for COVID-19 hospitalization (OR = 1.83, 95% CI = [1.16, 2.89]) and in-hospital mortality (OR = 3.69, 95% CI = [1.18, 11.50]) in the diabetes population with no complications.


Conclusions
The presence of diabetes complications increases the risks of COVID-19 infection, hospitalization, and worse health outcomes with respect to in-hospital mortality and longer hospital length of stay. We show the presence of health disparities in COVID-19 outcomes across demographic groups in our diabetes population. One such disparity is that African American and Hispanic diabetes patients have higher odds of COVID-19 infection than White and Non-Hispanic diabetes patients, respectively. Furthermore, Hispanic patients might have less access to the hospital care compared to non-Hispanic patients when longer hospitalizations are needed due to their diabetes complications. Finally, diabetes complications, which are generally associated with worse COVID-19 outcomes, might be predominantly determining the COVID-19 severity in those infected patients resulting in less demographic differences in COVID-19 hospitalization and in-hospital mortality.
}, number={9}, journal={PLOS ONE}, author={Paramita, Ni Luh Putu S. P. and Agor, Joseph K. and Mayorga, Maria E. and Ivy, Julie S. and Miller, Kristen E. and Ozaltin, Osman Y.}, year={2023}, month={Sep} }
 @article{hasnain_walton_odubela_mcconnell_davis_ivy_jiang_coan_islam_mpere_2023, title={Resiliency within the Socio-Ecological System of a Large Food Bank Network: Preparing, mitigating, responding, and recovering from Hurricane Florence}, volume={88}, ISSN={["2212-4209"]}, DOI={10.1016/j.ijdrr.2023.103580}, abstractNote={The network of a food bank consists of a complex web of entities. The entities may include the warehouses and charitable agencies. A food bank relies on the smooth interactions among these entities in distributing the donated food to the food-insecure population. In this study, we theorize that these entities and their complex interactions form a Socio-ecological System (SES). However, such an SES is vulnerable to disruptions, i.e., Hurricanes. We explore the behavior of the SES of our partner food bank, the Food Bank of Central and Eastern North Carolina (FBCENC), during Hurricane Florence, one of the deadliest hurricanes in the Carolinas. Specifically, we adopt a mixed-method research design to study the preparedness, mitigation, response, and recovery of the FBCENC SES over the lifecycle of Hurricane Florence. The design consists of quantitative methods (descriptive and statistical analyses), and qualitative methods (focus groups and semi-structured interviews). Our analysis reveals the preparation of the entities in terms of food flow within the SES, the impact of Hurricane Florence in terms of facility closure and inaccessibility, and the mitigation and response (studied together as “incidence”) of the entities through elevated activities, i.e, increase in received donations and distributions of relief items. Moreover, our analysis also reveals how the SES recovered through cooperation among the entities empowered by social capital. We also observe that new entities and connections were formed to recover from Hurricane Florence, providing a glimpse of how the FBCENC SES has been ”Built-Back-Better” after the hurricane.}, journal={INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION}, author={Hasnain, Tanzid and Walton, Tobin N. and Odubela, Kehinde and McConnell, Sarah and Davis, Lauren and Ivy, Julie and Jiang, Steven and Coan, Danielle and Islam, Md Hafizul and Mpere, Elsie}, year={2023}, month={Apr} }
 @article{dorris_ivy_swann_2022, title={AN APPROACH TO POPULATION SYNTHESIS OF ENGINEERING STUDENTS FOR UNDERSTANDING DROPOUT RISK}, ISSN={["0891-7736"]}, DOI={10.1109/WSC57314.2022.10015440}, abstractNote={Dropping out of STEM remains a critical issue today, and it would be useful for universities to have reliable predictive models to detect students' dropout risks. Generating a synthetic population of the true population could be useful for simulating the system and testing scenarios. We outline an approach for creating a synthetic population of students in STEM and build a microsimulation which simulates students' risk behaviors over time. This process has identified several areas that must be addressed before the synthetic population represents the true population in a simulation.}, journal={2022 WINTER SIMULATION CONFERENCE (WSC)}, author={Dorris, Danika and Ivy, Julie and Swann, Julie}, year={2022}, pages={677–688} }
 @article{rosenstrom_ivy_mayorga_swann_2022, title={COULD EARLIER AVAILABILITY OF BOOSTERS AND PEDIATRIC VACCINES HAVE REDUCED IMPACT OF COVID-19?}, ISSN={["0891-7736"]}, DOI={10.1109/WSC57314.2022.10015236}, abstractNote={The objective is to evaluate the impact of the earlier availability of COVID-19 vaccinations to children and boosters to adults in the face of the Delta and Omicron variants. We employed an agent-based stochastic network simulation model with a modified SEIR compartment model populated with demographic and census data for North Carolina. We found that earlier availability of childhood vaccines and earlier availability of adult boosters could have reduced the peak hospitalizations of the Delta wave by 10% and the Omicron wave by 42%, and could have reduced cumulative deaths by 9% by July 2022. When studied separately, we found that earlier childhood vaccinations reduce cumulative deaths by 2,611 more than earlier adult boosters. Therefore, the results of our simulation model suggest that the timing of childhood vaccination and booster efforts could have resulted in a reduced disease burden and that prioritizing childhood vaccinations would most effectively reduce disease spread.}, journal={2022 WINTER SIMULATION CONFERENCE (WSC)}, author={Rosenstrom, Erik T. and Ivy, Julie S. and Mayorga, Maria E. and Swann, Julie L.}, year={2022}, pages={1092–1103} }
 @article{perera_hey_chen_morello_mcconnell_ivy_2022, title={Checklists in Healthcare: Operational Improvement of Standards using Safety Engineering-Project CHOISSE-A framework for evaluating the effects of checklists on surgical team culture}, volume={103}, ISSN={["1872-9126"]}, url={https://doi.org/10.1016/j.apergo.2022.103786}, DOI={10.1016/j.apergo.2022.103786}, abstractNote={The CHOISSE multi-stage framework for evaluating the effects of electronic checklist applications (e-checklists) on surgical team members' perception of their roles, performance, communication, and understanding of checklists is introduced via a pilot study. A prospective interventional cohort study design was piloted to assess the effectiveness of the framework and the sociotechnical effects of the e-checklist. A Delphi process was used to design the stages of the framework based on literature and expert consensus. The CHOISSE framework was applied to guide the implementation and evaluation of e-checklists on team culture for ten pilot teams across the US over a 24-week period. The pilot results revealed more engagement by surgeons than non-surgeons, and significant increases in surgeons' perception of communication and engagement during surgery with a small sample. Mixed methods analysis of the data and lessons learned were used to identify iterative improvements to the CHOISSE framework and to inform future studies.}, journal={APPLIED ERGONOMICS}, publisher={Elsevier BV}, author={Perera, Gimantha N. and Hey, Lloyd A. and Chen, Karen B. and Morello, Madeline J. and McConnell, Brandon M. and Ivy, Julie S.}, year={2022}, month={Sep} }
 @article{zhang_mayorga_ivy_lich_swann_2022, title={Modeling the Impact of Nonpharmaceutical Interventions on COVID-19 Transmission in K-12 Schools}, volume={7}, ISSN={["2381-4683"]}, url={https://doi.org/10.1177/23814683221140866}, DOI={10.1177/23814683221140866}, abstractNote={ Background. The novel coronavirus SARS-CoV-2 spread across the world causing many waves of COVID-19. Children were at high risk of being exposed to the disease because they were not eligible for vaccination during the first 20 mo of the pandemic in the United States. While children 5 y and older are now eligible to receive a COVID-19 vaccine in the United States, vaccination rates remain low despite most schools returning to in-person instruction. Nonpharmaceutical interventions (NPIs) are important for controlling the spread of COVID-19 in K-12 schools. US school districts used varied and layered mitigation strategies during the pandemic. The goal of this article is to analyze the impact of different NPIs on COVID-19 transmission within K-12 schools. Methods. We developed a deterministic stratified SEIR model that captures the role of social contacts between cohorts in disease transmission to estimate COVID-19 incidence under different NPIs including masks, random screening, contact reduction, school closures, and test-to-stay. We designed contact matrices to simulate the contact patterns between students and teachers within schools. We estimated the proportion of susceptible infected associated with each intervention over 1 semester under the Omicron variant. Results. We find that masks and reducing contacts can greatly reduce new infections among students. Weekly screening tests also have a positive impact on disease mitigation. While self-quarantining symptomatic infections and school closures are effective measures for decreasing semester-end infections, they increase absenteeism. Conclusion. The model provides a useful tool for evaluating the impact of a variety of NPIs on disease transmission in K-12 schools. While the model is tested under Omicron variant parameters in US K-12 schools, it can be adapted to study other populations under different disease settings. Highlights A stratified SEIR model was developed that captures the role of social contacts in K-12 schools to estimate COVID-19 transmission under different nonpharmaceutical interventions. While masks, random screening, contact reduction, school closures, and test-to-stay are all beneficial interventions, masks and contact reduction resulted in the greatest reduction in new infections among students from the tested scenarios. Layered interventions provide more benefits than implementing interventions independently. }, number={2}, journal={MDM POLICY & PRACTICE}, author={Zhang, Yiwei and Mayorga, Maria E. and Ivy, Julie and Lich, Kristen Hassmiller and Swann, Julie L.}, year={2022} }
 @article{rosenstrom_meshkinfam_ivy_goodarzi_capan_huddleston_romero-brufau_2022, title={Optimizing the First Response to Sepsis: An Electronic Health Record-Based Markov Decision Process Model}, volume={7}, ISSN={["1545-8504"]}, url={https://doi.org/10.1287/deca.2022.0455}, DOI={10.1287/deca.2022.0455}, abstractNote={ Sepsis is considered a medical emergency where delays in initial treatment are associated with increased morbidity and mortality, yet there is no gold standard for identifying sepsis onset and thus treatment timing. We leverage electronic health record (EHR) data with clinical expertise to develop a continuous-time Markov decision process (MDP) optimal stopping model that identifies the optimal first intervention action (anti-infective, fluid, or wait). To study the impact of initial treatment of patients at risk for developing sepsis, we define the delayed treatment population who received delayed treatment upon admission or during hospitalization and serves as an approximation of the natural history of sepsis. We apply the optimal first treatment policy to sample patient visits from the nondelayed treatment population. This analysis indicates the average risk of death could be reduced by approximately 2.2%, the average time until treatment could be reduced by 106 minutes, and the average severity of the treatment state could be reduced by 15.5% compared with the treatment they received in the hospital. We study the properties of the optimal policy to define an easily interpretable initial treatment heuristic that considers a patient’s organ dysfunction, location, and septic shock status. This generalizable framework can inform personalized treatment of patients at risk for sepsis.  History: This paper has been accepted for the Decision Analysis Special Issue on Emerging Topics in Health Decision Analysis.  Funding: This material is based upon work supported by the National Science Foundation [Grant 1522107 (North Carolina State University), 1522106 (Mayo Clinic), and 1833538 (Drexel University)]. }, journal={DECISION ANALYSIS}, author={Rosenstrom, Erik and Meshkinfam, Sareh and Ivy, Julie Simmons and Goodarzi, Shadi Hassani and Capan, Muge and Huddleston, Jeanne and Romero-Brufau, Santiago}, year={2022}, month={Jul} }
 @article{johnson_biddell_hecht_lich_swann_delamater_mayorga_ivy_smith_patel_2022, title={Organizational decision-making during COVID-19: A qualitative analysis of the organizational decision-making system in the United States during COVID-19}, volume={11}, ISSN={["1468-5973"]}, DOI={10.1111/1468-5973.12437}, abstractNote={AbstractThis study sought to understand COVID‐19‐related organizational decisions were made across sectors. To gain this understanding, we conducted semi‐structured interviews with organizational decision‐makers in North Carolina about their experiences responding to COVID‐19. Conventional content analysis was used to analyse the context, inputs, and processes involved in decision‐making. Between October 2020 and February 2021, we interviewed 44 decision‐makers from the following sectors: business (n = 4), community non‐profit (n = 3), county government (n = 4), healthcare (n = 5), local public health (n = 5), public safety (n = 7), religious (n = 6), education (n = 7) and transportation (n = 3). We found that during the pandemic, organizations looked to scientific authorities, the decisions of peer organizations, data about COVID‐19, and their own experience with prior crises. Interpretation of inputs was informed by current political events, societal trends, and organization mission. Decision‐makers had to account for divergent internal opinions and community behaviour. To navigate inputs and contextual factors, organizations decentralized decision‐making authority, formed auxiliary decision‐making bodies, learned to resolve internal conflicts, learned in real time from their crisis response, and routinely communicated decisions with their communities. In conclusion, aligned with systems and contingency theories of decision‐making, decision‐making during COVID‐19 depended on an organization's ‘fit’ within the specifics of their existing system and their ability to orient the dynamics of that system to their own goals.}, journal={JOURNAL OF CONTINGENCIES AND CRISIS MANAGEMENT}, author={Johnson, Karl and Biddell, Caitlin B. B. and Hecht, Hillary K. K. and Lich, Kristen H. H. and Swann, Julie and Delamater, Paul and Mayorga, Maria and Ivy, Julie and Smith, Raymond L. L. and Patel, Mehul D. D.}, year={2022}, month={Nov} }
 @article{patel_rosenstrom_ivy_mayorga_keskinocak_boyce_hassmiller lich_smith_johnson_delamater_et al._2021, title={Association of Simulated COVID-19 Vaccination and Nonpharmaceutical Interventions With Infections, Hospitalizations, and Mortality}, volume={4}, ISSN={["2574-3805"]}, DOI={10.1001/jamanetworkopen.2021.10782}, abstractNote={Key Points Question What is the association of COVID-19 vaccine efficacy and coverage scenarios with and without nonpharmaceutical interventions (NPIs) with SARS-CoV-2 infections, hospitalizations, and deaths? Findings A decision analytical model of North Carolina found that removing NPIs while vaccines were distributed resulted in substantial increases in infections, hospitalizations, and deaths. Furthermore, as NPIs were removed, higher vaccination coverage with less efficacious vaccines contributed to a larger reduction in risk of infection compared with more efficacious vaccines at lower coverage. Meaning These findings highlight the need for high COVID-19 vaccine coverage and continued adherence to NPIs before safely resuming many prepandemic activities.}, number={6}, journal={JAMA NETWORK OPEN}, author={Patel, Mehul D. and Rosenstrom, Erik and Ivy, Julie S. and Mayorga, Maria E. and Keskinocak, Pinar and Boyce, Ross M. and Hassmiller Lich, Kristen and Smith, Raymond L., III and Johnson, Karl T. and Delamater, Paul L. and et al.}, year={2021}, month={Jun} }
 @article{sharma_davis_ivy_chi_2021, title={Data to Donations: Towards In-Kind Food Donation Prediction across Two Coasts}, ISSN={["2377-6919"]}, DOI={10.1109/GHTC53159.2021.9612484}, abstractNote={Our goal in this work is to build effective yet robust models to predict unreliable and inconsistent in-kind donations at both weekly and monthly levels for two food banks across coasts: the Food Bank of Central Eastern North Carolina in North Carolina and Los Angeles Regional Food Bank in California. We explore three factors: model, data length, and window type. For the model, we evaluate a series of classic time-series forecasting models against the state-of-the-art approaches such as Bayesian Structural Time Series modeling (BSTS) and deep learning models; for the data length, we vary training data from 2 weeks to 13 years; for the window type, we compare sliding vs. expanding. Our results show the effectiveness of different models heavily depends on the data length and the window type as well as characteristics of the food bank. Motivated by these findings, we investigate the effectiveness of employing an average of all predictions formed by considering all three factors at both monthly and weekly levels for both food banks. Our results show that this average of predictions significantly and consistently outperforms all classical models, deep learning, and BSTS for the donation prediction at both monthly and weekly levels for both food banks.}, journal={2021 IEEE GLOBAL HUMANITARIAN TECHNOLOGY CONFERENCE (GHTC)}, author={Sharma, Esha and Davis, Lauren and Ivy, Julie and Chi, Min}, year={2021}, pages={281–288} }
 @article{hasnain_sengul orgut_ivy_2021, title={Elicitation of Preference among Multiple Criteria in Food Distribution by Food Banks}, ISSN={["1937-5956"]}, DOI={10.1111/poms.13551}, abstractNote={ The United Nations Sustainable Development Goals provide a road map for countries to achieve peace and prosperity. In this study, we address two of these sustainable development goals: achieving food security and reducing inequalities. Food banks are nonprofit organizations that collect and distribute food donations to food‐insecure populations in their service regions. Food banks consider three criteria while distributing the donated food: equity, effectiveness, and efficiency. The equity criterion aims to distribute food in proportion to the food‐insecure households in a food bank's service area. The effectiveness criterion aims to minimize undistributed food, whereas the efficiency criterion minimizes the total cost of transportation. Models that assume predetermined weights on these criteria may produce inaccurate results as the preference of food banks over these criteria may vary over time, and as a function of supply and demand. In collaboration with our food bank partner in North Carolina, we develop a single‐period, weighted multi‐criteria optimization model that provides the decision‐maker the flexibility to capture their preferences over the three criteria of equity, effectiveness, and efficiency, and explore the resulting trade‐offs. We then introduce a novel algorithm that elicits the inherent preference of a food bank by analyzing its actions within a single‐period. The algorithm does not require direct interaction with the decision‐maker. The non‐interactive nature of this algorithm is especially significant for humanitarian organizations such as food banks which lack the resources to interact with modelers on a regular basis. We perform extensive numerical experiments to validate the efficiency of our algorithm. We illustrate results using historical data from our food bank partner and discuss managerial insights. We explore the implications of different decision‐maker preferences for the criteria on distribution policies. }, journal={PRODUCTION AND OPERATIONS MANAGEMENT}, author={Hasnain, Tanzid and Sengul Orgut, Irem and Ivy, Julie Simmons}, year={2021}, month={Oct} }
 @article{islam_ivy_2021, title={Modeling the role of efficiency for the equitable and effective distribution of donated food}, ISSN={["1436-6304"]}, DOI={10.1007/s00291-021-00634-z}, journal={OR SPECTRUM}, author={Islam, Md Hafizul and Ivy, Julie Simmons}, year={2021}, month={Jun} }
 @article{jazayeri_capan_ivy_arnold_yang_2021, title={Proximity of Cellular and Physiological Response Failures in Sepsis}, volume={25}, ISSN={["2168-2208"]}, DOI={10.1109/JBHI.2021.3098428}, abstractNote={Sepsis is a devastating multi-stage health condition with a high mortality rate. Its complexity, prevalence, and dependency of its outcomes on early detection have attracted substantial attention from data science and machine learning communities. Previous studies rely on individual cellular and physiological responses representing organ system failures to predict health outcomes or the onset of different sepsis stages. However, it is known that organ systems’ failures and dynamics are not independent events. In this study, we identify the dependency patterns of significant proximate sepsis-related failures of cellular and physiological responses using data from 12,223 adult patients hospitalized between July 2013 and December 2015. The results show that proximate failures of cellular and physiological responses create better feature sets for outcome prediction than individual responses. Our findings reveal the few significant proximate failures that play the major roles in predicting patients’ outcomes. This study's results can be simply translated into clinical practices and inform the prediction and improvement of patients’ conditions and outcomes.}, number={11}, journal={IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS}, author={Jazayeri, Ali and Capan, Muge and Ivy, Julie and Arnold, Ryan and Yang, Christopher C.}, year={2021}, month={Nov}, pages={4089–4097} }
 @article{swan_mayorga_ivy_2022, title={The SMART Framework: Selection of Machine Learning Algorithms With ReplicaTions-A Case Study on the Microvascular Complications of Diabetes}, volume={26}, ISSN={["2168-2208"]}, DOI={10.1109/JBHI.2021.3094777}, abstractNote={Over 34 million people in the US have diabetes, a major cause of blindness, renal failure, and amputations. Machine learning (ML) models can predict high-risk patients to help prevent adverse outcomes. Selecting the ‘best’ prediction model for a given disease, population, and clinical application is challenging due to the hundreds of health-related ML models in the literature and the increasing availability of ML methodologies. To support this decision process, we developed the Selection of Machine-learning Algorithms with ReplicaTions (SMART) Framework that integrates building and selecting ML models with decision theory. We build ML models and estimate performance for multiple plausible future populations with a replicated nested cross-validation technique. We rank ML models by simulating decision-maker priorities, using a range of accuracy measures (e.g., AUC) and robustness metrics from decision theory (e.g., minimax Regret). We present the SMART Framework through a case study on the microvascular complications of diabetes using data from the ACCORD clinical trial. We compare selections made by risk-averse, -neutral, and -seeking decision-makers, finding agreement in 80% of the risk-averse and risk-neutral selections, with the risk-averse selections showing consistency for a given complication. We also found that the models that best predicted outcomes in the validation set were those with low performance variance on the testing set, indicating a risk-averse approach in model selection is ideal when there is a potential for high population feature variability. The SMART Framework is a powerful, interactive tool that incorporates various ML algorithms and stakeholder preferences, generalizable to new data and technological advancements.}, number={2}, journal={IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS}, author={Swan, Breanna P. and Mayorga, Maria E. and Ivy, Julie S.}, year={2022}, month={Feb}, pages={809–817} }
 @article{swan_nambiar_koutouan_mayorga_ivy_fransen_2020, title={EVALUATING DIABETIC RETINOPATHY SCREENING INTERVENTIONS IN A MICROSIMULATION MODEL}, ISSN={["0891-7736"]}, DOI={10.1109/WSC48552.2020.9384074}, abstractNote={Diabetic retinopathy (DR) is the leading cause of blindness for working age Americans. Early detection, timely treatment, and appropriate follow-up care reduce the risk of severe vision loss from DR by 95%, yet, less than 50% of people with diabetes adhere to the recommended screening guidelines. Diabetes is a complicated disease for patients and their physicians to manage. We developed a microsimulation integrating the natural history model of DR with a patient’s interaction with the care system. We introduced a DR screening device in primary care, with and without care coordination by a medical professional, in two interventions to the current care path. We found the interventions increased adherence of patients with vision-threatening DR (VTDR) to follow-up eye care, decreased the number of ‘unnecessary’ visits in specialty eye care from patients without VTDR, and decreased the total years spent blind.}, journal={2020 WINTER SIMULATION CONFERENCE (WSC)}, author={Swan, Breanna and Nambiar, Siddhartha and Koutouan, Priscille and Mayorga, Maria E. and Ivy, Julie and Fransen, Stephen}, year={2020}, pages={944–955} }
 @article{agor_ozaltin_ivy_capan_arnold_romero_2019, title={The value of missing information in severity of illness score development}, volume={97}, ISSN={["1532-0480"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85069932839&partnerID=MN8TOARS}, DOI={10.1016/j.jbi.2019.103255}, abstractNote={We aim to investigate the hypothesis that using information about which variables are missing along with appropriate imputation improves the performance of severity of illness scoring systems used to predict critical patient outcomes.We quantify the impact of missing and imputed variables on the performance of prediction models used in the development of a sepsis-related severity of illness scoring system. Electronic health records (EHR) data were compiled from Christiana Care Health System (CCHS) on 119,968 adult patients hospitalized between July 2013 and December 2015. Two outcomes of interest were considered for prediction: (1) first transfer to intensive care unit (ICU) and (2) in-hospital mortality. Five different prediction models were employed. Indicators were utilized in these prediction models to identify when variables were missing and imputed.We observed statistically significant gains in prediction performance when moving from models that did not indicate missing information to those that did. Moreover, this increase was higher in models that use summary variables as predictors compared to those that use all variables.When developing prediction models using longitudinal EHR data, researchers should explore the incorporation of indicators for missing variables along with appropriate imputation.}, journal={JOURNAL OF BIOMEDICAL INFORMATICS}, author={Agor, Joseph and Ozaltin, Osman Y. and Ivy, Julie S. and Capan, Muge and Arnold, Ryan and Romero, Santiago}, year={2019}, month={Sep} }
 @article{hicklin_ivy_payton_viswanathan_myers_2018, title={Exploring the Value of Waiting During Labor}, volume={10}, ISSN={["2164-3970"]}, DOI={10.1287/serv.2018.0205}, abstractNote={ Of the nearly four million births that occur each year in the United States, almost one in three is a cesarean delivery. Despite the increasing C-section rate over the years, there is no evidence that the increase has caused a decrease in neonatal or maternal mortality or morbidity. Bayesian decision analysis is used to model the decision between classifying a patient as “failure-to-progress,” which is cause for a C-section, using current information (prior probability) or information gathered (posterior probability) as labor continues. The Bayesian decision models determine the conditions under which it is appropriate to gather additional information (i.e., take an observation) before deciding to end labor and perform a C-section based on the decision maker’s belief about successful labor. During an observation period, the decision maker learns more about the patient and her medical state and the likelihood of a successful vaginal delivery is updated. This study determines the conditional value of information (conditional on the decision maker’s prior belief) and determines the conditions under which information has positive value. This model can be used to facilitate shared decision making for labor and delivery through communicating beliefs, risk perceptions, and the associated actions.  The online appendix is available at https://doi.org/10.1287/serv.2018.0205 . }, number={3}, journal={SERVICE SCIENCE}, author={Hicklin, Karen and Ivy, Julie S. and Payton, Fay Cobb and Viswanathan, Meera and Myers, Evan}, year={2018}, month={Sep}, pages={334–353} }
 @article{capan_hoover_ivy_miller_arnold_2018, title={Not all organ dysfunctions are created equal - Prevalence and mortality in sepsis}, volume={48}, ISSN={["1557-8615"]}, DOI={10.1016/j.jcrc.2018.08.021}, abstractNote={While organ dysfunctions within sepsis have been widely studied, interaction between measures of organ dysfunction remains an understudied area. The objective of this study is to quantify the impact of organ dysfunction on in-hospital mortality in infected population.Descriptive and multivariate analyses of retrospective data including patients (age ≥ 18 years) hospitalized at the study hospital from July 2013 to April 2016 who met the criteria for an infection visit (62,057 unique visits).The multivariate logistic regression model had an area under the curve of 0.9. Highest odds ratio (OR) associated with increased mortality risk was identified as fraction of inspired oxygen (FiO2) > 21% (OR = 5.8 and 95% Confidence Interval (CI) 1.8-35.6), and elevated lactate >2.0 mmol/L (OR = 2.45 (95% CI = 2.1-2.8)). Most commonly observed measures of organ dysfunction within mortality visits included elevated lactate (> 2.0 mmol/L), mechanical ventilation, and oxygen saturation (SpO2)/FiO2 ratio (< 421) at least once within 48 h prior to or 24 h after anti-infective administration.There exist differences in measures of organ dysfunction occurrence and their association with mortality. These findings support increased clinical efforts to identify sepsis patients to inform diagnostic decisions.}, journal={JOURNAL OF CRITICAL CARE}, author={Capan, Muge and Hoover, Stephen and Ivy, Julie S. and Miller, Kristen E. and Arnold, Ryan}, year={2018}, month={Dec}, pages={257–262} }
 @article{orgut_ivy_uzsoy_hale_2018, title={Robust optimization approaches for the equitable and effective distribution of donated food}, volume={269}, ISSN={["1872-6860"]}, DOI={10.1016/j.ejor.2018.02.017}, abstractNote={Motivated by our eight-year partnership with a local food bank, we present two robust optimization models to support the equitable and effective distribution of donated food over the food bank's service area. Our first model addresses uncertainty in the amount of donated food counties can effectively receive and distribute, which depends on local factors such as budget and workforce that are unknown to the food bank. Assuming that the capacity of each county varies within a range, the model seeks to maximize total food distribution while enforcing a user-specified level of robustness. Our second model uses robust optimization in a nontraditional manner, treating the upper bound on the level of allowed inequity as an uncertain parameter and limiting total deviation from a perfectly equitable distribution over all counties while maximizing total food shipment. We derive structural properties of both models and develop efficient exact solution algorithms. We illustrate our models using historical data obtained from our food bank partner, summarize the policy implications of our results and examine the impact of uncertainty on outcomes and decision making.}, number={2}, journal={EUROPEAN JOURNAL OF OPERATIONAL RESEARCH}, author={Orgut, Irem Sengul and Ivy, Julie S. and Uzsoy, Reha and Hale, Charlie}, year={2018}, month={Sep}, pages={516–531} }
 @article{hicklin_ivy_wilson_cobb payton_viswanathan_myers_2019, title={Simulation model of the relationship between cesarean section rates and labor duration}, volume={22}, ISSN={1386-9620 1572-9389}, url={http://dx.doi.org/10.1007/S10729-018-9449-3}, DOI={10.1007/s10729-018-9449-3}, abstractNote={Cesarean delivery is the most common major abdominal surgery in many parts of the world, and it accounts for nearly one-third of births in the United States. For a patient who requires a C-section, allowing prolonged labor is not recommended because of the increased risk of infection. However, for a patient who is capable of a successful vaginal delivery, performing an unnecessary C-section can have a substantial adverse impact on the patient's future health. We develop two stochastic simulation models of the delivery process for women in labor; and our objectives are (i) to represent the natural progression of labor and thereby gain insights concerning the duration of labor as it depends on the dilation state for induced, augmented, and spontaneous labors; and (ii) to evaluate the Friedman curve and other labor-progression rules, including their impact on the C-section rate and on the rates of maternal and fetal complications. To use a shifted lognormal distribution for modeling the duration of labor in each dilation state and for each type of labor, we formulate a percentile-matching procedure that requires three estimated quantiles of each distribution as reported in the literature. Based on results generated by both simulation models, we concluded that for singleton births by nulliparous women with no prior complications, labor duration longer than two hours (i.e., the time limit for labor arrest based on the Friedman curve) should be allowed in each dilation state; furthermore, the allowed labor duration should be a function of dilation state.}, number={4}, journal={Health Care Management Science}, publisher={Springer Science and Business Media LLC}, author={Hicklin, Karen T. and Ivy, Julie S. and Wilson, James R. and Cobb Payton, Fay and Viswanathan, Meera and Myers, Evan R.}, year={2019}, month={Dec}, pages={635–657} }
 @article{nataraj_ivy_payton_norman_2018, title={Diabetes and the hospitalized patient: A cluster analytic framework for characterizing the role of sex, race and comorbidity from 2006 to 2011}, volume={21}, ISSN={["1572-9389"]}, DOI={10.1007/s10729-017-9408-4}, abstractNote={In the US, one in four adults has two or more chronic conditions; this population accounts for two thirds of healthcare spending. Comorbidity, the presence of multiple simultaneous health conditions in an individual, is increasing in prevalence and has been shown to impact patient outcomes negatively. Comorbidities associated with diabetes are correlated with increased incidence of preventable hospitalizations, longer lengths of stay (LOS), and higher costs. This study focuses on sex and race disparities in outcomes for hospitalized adult patients with and without diabetes. The objective is to characterize the impact of comorbidity burden, measured as the Charlson Weighted Index of Comorbidities (WIC), on outcomes including LOS, total charges, and disposition (specifically, probability of routine discharge home). Data from the National Inpatient Sample (2006-2011) were used to build a cluster-analytic framework which integrates cluster analysis with multivariate and logistic regression methods, for several goals: (i) to evaluate impact of these covariates on outcomes; (ii) to identify the most important comorbidities in the hospitalized population; and (iii) to create a simplified WIC score. Results showed that, although hospitalized women had better outcomes than men, the impact of diabetes was worse for women. Also, non-White patients had longer lengths of stay and higher total charges. Furthermore, the simplified WIC performed equivalently in the generalized linear models predicting standardized total charges and LOS, suggesting that this new score can sufficiently capture the important variability in the data. Our findings underscore the need to evaluate the differential impact of diabetes on physiology and treatment in women and in minorities.}, number={4}, journal={HEALTH CARE MANAGEMENT SCIENCE}, author={Nataraj, Nisha and Ivy, Julie Simmons and Payton, Fay Cobb and Norman, Joseph}, year={2018}, month={Dec}, pages={534–553} }
 @inproceedings{zhang_lin_chi_ivy_capan_huddleston_2017, title={LSTM for septic shock: Adding unreliable labels to reliable predictions}, DOI={10.1109/bigdata.2017.8258049}, abstractNote={Sepsis is a leading cause of death over the world and septic shock, the most severe complication of sepsis, reaches a mortality rate as high as 50%. Early diagnosis and treatment can prevent most morbidity and mortality. Nowadays, the increasing availability of the electronic health records (EHRs) has generated great interests in developing models to predict acute medical conditions such as septic shock. However, septic shock prediction faces two major challenges : 1) how to capture the informative progression of septic shock in a long visit to hospital of a patient; and 2) how to obtain reliable predictions without well-established moment-by-moment ground-truth labels for septic shock. In this work, we proposed a generic framework to predict septic shock based on Long-Short Term Memory (LSTM) model, which is capable of memorizing temporal dependencies over a long period. The framework integrates two levels of imperfect yet informative labels to jointly learn the discriminative patterns of septic shock: ICD-9 code as the visit-level label and the clinical criteria designed by domain experts as the moment-by-moment event-level label. We evaluate our method on a real-world data extracted from an EHR system constituted by 12,954 visits and 1,348,625 events, and compare it against multiple baselines. The robustness of the method is validated using three sets of clinician-proposed adjusted ground-truth labels. Also, we explore whether the framework is effective for the early prediction of the patients developing septic shock. The experimental results demonstrate the superiority of our proposed method in the task of septic shock prediction.}, booktitle={2017 IEEE International Conference on Big Data (Big Data)}, author={Zhang, Y. and Lin, C. and Chi, M. and Ivy, J. and Capan, M. and Huddleston, J. M.}, year={2017}, pages={1233–1242} }
 @article{orgut_ivy_uzsoy_2017, title={Modeling for the equitable and effective distribution of food donations under stochastic receiving capacities}, volume={49}, ISSN={["2472-5862"]}, DOI={10.1080/24725854.2017.1300358}, abstractNote={ABSTRACT We present and analyze stochastic models developed to facilitate the equitable and effective distribution of donated food by a regional food bank among the population at risk for hunger. Since demand typically exceeds the donated food supply, the food bank must distribute donated food in an equitable manner while minimizing food waste, leading to conflicting objectives. Distribution to beneficiaries in the service area is carried out by local charitable agencies, whose receiving capacities are stochastic, since they depend on factors (such as their budget and workforce) that vary significantly over time. We develop a single-period, two-stage stochastic model that ensures equitable distribution of food donations when the distribution decisions are made prior to observing capacities at the receiving locations. Shipment decisions made at the beginning of the period can be corrected at an additional cost after the capacities are observed in the second stage. We prove that this model has a newsvendor-type closed-form optimal solution and illustrate our results using historical data from our collaborating food bank.}, number={6}, journal={IISE TRANSACTIONS}, author={Orgut, Irem Sengul and Ivy, Julie and Uzsoy, Reha}, year={2017}, pages={567–578} }
 @article{smith_vila-parrish_ivy_abel_2017, title={A simulation approach for evaluating medication supply chain structures}, volume={4}, ISSN={2330-2674 2330-2682}, url={http://dx.doi.org/10.1080/23302674.2015.1135355}, DOI={10.1080/23302674.2015.1135355}, abstractNote={ABSTRACTHealthcare costs per capita in the United States are one of the most expensive in the world. It may be surprising to note that the second largest expense in a hospital is in inventories. In this paper, we address the need for developing better quantitative models of the hospital medication supply chain system. We develop two simulation models which represent commonly used supply chains: centralised and stockless. We present a case study using meropenem, an antibiotic, to explore the impact of product and demand characteristics on the cost effectiveness of each approach. The results show that while the stockless system is almost always higher performing in terms of cost, it may be at the expense of patient safety. The results of the simulation models suggest developing a strategy using classifications to aid strategic supply chain decisions related to the hospital environment could improve inventory management.}, number={1}, journal={International Journal of Systems Science: Operations & Logistics}, publisher={Informa UK Limited}, author={Smith, Kathryn N. and Vila-Parrish, Anita R. and Ivy, Julie S. and Abel, Steven R.}, year={2017}, month={Jan}, pages={13–26} }
 @article{orgut_brock_davis_ivy_jiang_morgan_uzsoy_hale_middleton_2016, title={Achieving equity, effectiveness, and efficiency in food bank operations: Strategies for feeding America with implications for global hunger relief}, volume={235}, journal={Advances in managing humanitarian operations}, author={Orgut, I. S. and Brock, L. G. and Davis, L. B. and Ivy, J. S. and Jiang, S. and Morgan, S. D. and Uzsoy, R. and Hale, C. and Middleton, E.}, year={2016}, pages={229–256} }
 @inproceedings{pooya_ivy_mosaly_tracton_singh_tracton_singh_2016, title={Assessing the reliability of the radiation therapy care delivery process using discrete event simulation}, booktitle={2016 10th european conference on antennas and propagation (eucap)}, author={Pooya, P. and Ivy, J. and Mosaly, P. and Tracton, G. and Singh, N. and Tracton, G. and Singh, N.}, year={2016}, pages={1233–1244} }
 @article{capan_ivy_wilson_huddleston_2017, title={A stochastic model of acute-care decisions based on patient and provider heterogeneity}, volume={20}, ISSN={["1572-9389"]}, DOI={10.1007/s10729-015-9347-x}, abstractNote={The primary cause of preventable death in many hospitals is the failure to recognize and/or rescue patients from acute physiologic deterioration (APD). APD affects all hospitalized patients, potentially causing cardiac arrest and death. Identifying APD is difficult, and response timing is critical - delays in response represent a significant and modifiable patient safety issue. Hospitals have instituted rapid response systems or teams (RRT) to provide timely critical care for APD, with thresholds that trigger the involvement of critical care expertise. The National Early Warning Score (NEWS) was developed to define these thresholds. However, current triggers are inconsistent and ignore patient-specific factors. Further, acute care is delivered by providers with different clinical experience, resulting in quality-of-care variation. This article documents a semi-Markov decision process model of APD that incorporates patient and provider heterogeneity. The model allows for stochastically changing health states, while determining patient subpopulation-specific RRT-activation thresholds. The objective function minimizes the total time associated with patient deterioration and stabilization; and the relative values of nursing and RRT times can be modified. A case study from January 2011 to December 2012 identified six subpopulations. RRT activation was optimal for patients in "slightly concerning" health states (NEWS > 0) for all subpopulations, except surgical patients with low risk of deterioration for whom RRT was activated in "concerning" states (NEWS > 4). Clustering methods identified provider clusters considering RRT-activation preferences and estimation of stabilization-related resource needs. Providers with conservative resource estimates preferred waiting over activating RRT. This study provides simple practical rules for personalized acute care delivery.}, number={2}, journal={HEALTH CARE MANAGEMENT SCIENCE}, author={Capan, Muge and Ivy, Julie S. and Wilson, James R. and Huddleston, Jeanne M.}, year={2017}, month={Jun}, pages={187–206} }
 @article{capan_ivy_rohleder_hickman_huddleston_2015, title={Individualizing and optimizing the use of early warning scores in acute medical care for deteriorating hospitalized patients}, volume={93}, ISSN={["1873-1570"]}, DOI={10.1016/j.resuscitation.2014.12.032}, abstractNote={AimWhile early warning scores (EWS) have the potential to identify physiological deterioration in an acute care setting, the implementation of EWS in clinical practice has yet to be fully realized. The primary aim of this study is to identify optimal patient-centered rapid response team (RRT) activation rules using electronic medical records (EMR)-derived Markovian models.MethodsThe setting for the observational cohort study included 38,356 adult general floor patients hospitalized in 2011. The national early warning score (NEWS) was used to measure the patient health condition. Chi-square and Kruskal Wallis tests were used to identify statistically significant subpopulations as a function of the admission type (medical or surgical), frailty as measured by the Braden skin score, and history of prior clinical deterioration (RRT, cardiopulmonary arrest, or unscheduled ICU transfer).ResultsStatistical tests identified 12 statistically significant subpopulations which differed clinically, as measured by length of stay and time to re-admission (P < .001). The Chi-square test of independence results showed a dependency structure between subsequent states in the embedded Markov chains (P < .001). The SMDP models identified two sets of subpopulation-specific RRT activation rules for each statistically unique subpopulation. Clinical deterioration experience in prior hospitalizations did not change the RRT activation rules. The thresholds differed as a function of admission type and frailty.ConclusionsEWS were used to identify personalized thresholds for RRT activation for statistically significant Markovian patient subpopulations as a function of frailty and admission type. The full potential of EWS for personalizing acute care delivery is yet to be realized.}, journal={RESUSCITATION}, author={Capan, Muge and Ivy, Julie S. and Rohleder, Thomas and Hickman, Joel and Huddleston, Jeanne M.}, year={2015}, month={Aug}, pages={107–112} }
 @article{orgut_ivy_uzsoy_wilson_2016, title={Modeling for the equitable and effective distribution of donated food under capacity constraints}, volume={48}, ISSN={["1545-8830"]}, DOI={10.1080/0740817x.2015.1063792}, abstractNote={Abstract Mathematical models are presented and analyzed to facilitate a food bank's equitable and effective distribution of donated food among a population at risk for hunger. Typically exceeding the donated supply, demand is proportional to the poverty population within the food bank's service area. The food bank seeks to ensure a perfectly equitable distribution of food; i.e., each county in the service area should receive a food allocation that is exactly proportional to the county's demand such that no county is at a disadvantage compared to any other county. This objective often conflicts with the goal of maximizing effectiveness by minimizing the amount of undistributed food. Deterministic network-flow models are developed to minimize the amount of undistributed food while maintaining a user-specified upper bound on the absolute deviation of each county from a perfectly equitable distribution. An extension of this model identifies optimal policies for the allocation of additional receiving capacity to counties in the service area. A numerical study using data from a large North Carolina food bank illustrates the uses of the models. A probabilistic sensitivity analysis reveals the effect on the models' optimal solutions arising from uncertainty in the receiving capacities of the counties in the service area.}, number={3}, journal={IIE TRANSACTIONS}, author={Orgut, Irem Sengul and Ivy, Julie and Uzsoy, Reha and Wilson, James R.}, year={2016}, pages={252–266} }
 @article{yaylali_ivy_uzsoy_samoff_meyer_maillard_2016, title={Modeling the effect of public health resources and alerting on the dynamics of pertussis spread}, volume={5}, ISSN={["2047-6973"]}, DOI={10.1057/hs.2015.6}, abstractNote={We consider the response of a local health department (LHD) to a pertussis outbreak using a composite discrete event simulation model with a stochastic branching process. The model captures the effect of epidemiologic spread of disease as a function of the health alert levels and the resource availability of the LHD. The primary response mode in the model is contact tracing that is assumed to be a resource-based delay with an iterative tracing policy. The effect of the threshold for initiating contact tracing and its relationship with the resource availability of the LHD is explored. The model parameters associated with contact tracing are estimated using North Carolina (NC), U.S.A. pertussis case data and data from the NC Public Health Information Network. The infectivity parameters are derived from literature. The results suggest that the time to initiate contact tracing significantly affects the magnitude and duration of the outbreak. The resource levels for contact tracing have less significant impact on the outbreak outcomes. However, when the nurse schedule is constrained, that is, if the total hours devoted to contact tracing a week is restricted, the effect of the resource level becomes significant. In fact, some outbreaks could not be controlled within the 1-year time limit of simulation.}, number={2}, journal={HEALTH SYSTEMS}, author={Yaylali, Emine and Ivy, Julie S. and Uzsoy, Reha and Samoff, Erika and Meyer, Anne Marie and Maillard, Jean Marie}, year={2016}, month={Jun}, pages={81–97} }
 @article{mazur_chera_mosaly_taylor_tracton_johnson_comitz_adams_pooya_ivy_et al._2015, title={The association between event learning and continuous quality improvement programs and culture of patient safety}, volume={5}, ISSN={1879-8500}, url={http://dx.doi.org/10.1016/J.PRRO.2015.04.010}, DOI={10.1016/J.PRRO.2015.04.010}, abstractNote={Purpose To present our approach and results from our quality and safety program and to report their possible impact on our culture of patient safety. Methods and materials We created an event learning system (termed a "good catch" program) and encouraged staff to report any quality or safety concerns in real time. Events were analyzed to assess the utility of safety barriers. A formal continuous quality improvement program was created to address these reported events and make improvements. Data on perceptions of the culture of patient safety were collected using the Agency for Health Care Research and Quality survey administered before, during, and after the initiatives. Results Of 560 good catches reported, 367 could be ascribed to a specific step on our process map. The calculated utility of safety barriers was highest for those embedded into the pretreatment quality assurance checks performed by physicists and dosimetrists (utility score 0.53; 93 of 174) and routine checks done by therapists on the initial day of therapy. Therapists and physicists reported the highest number of good catches (24% each). Sixty-four percent of events were caused by performance issues (eg, not following standardized processes, including suboptimal communications). Of 31 initiated formal improvement events, 26 were successfully implemented and sustained, 4 were discontinued, and 1 was not implemented. Most of the continuous quality improvement program was conducted by nurses (14) and therapists (7). Percentages of positive responses in the patient safety culture survey appear to have increased on all dimensions (p < .05). Conclusions Results suggest that event learning and continuous quality improvement programs can be successfully implemented and that there are contemporaneous improvements in the culture of safety.}, number={5}, journal={Practical Radiation Oncology}, publisher={Elsevier BV}, author={Mazur, Lukasz and Chera, Bhishamjit and Mosaly, Prithima and Taylor, Kinley and Tracton, Gregg and Johnson, Kendra and Comitz, Elizabeth and Adams, Robert and Pooya, Pegah and Ivy, Julie and et al.}, year={2015}, month={Sep}, pages={286–294} }
 @article{reamer_ivy_vila-parrish_young_2015, title={Understanding the evolution of mathematics performance in primary education and the implications for STEM learning: A Markovian approach}, volume={47}, ISSN={["1873-7692"]}, DOI={10.1016/j.chb.2014.09.037}, abstractNote={National reports have documented deficiencies in the vertical alignment of mathematical learning in K-12 education. Many students fail to master requisite concepts before advancing to more complex ideas, leaving them ill-prepared to succeed in higher level Science, Technology, Engineering, and Mathematics (STEM) coursework. In this paper, we model elementary and middle school students’ performance in mathematics over time as a stochastic process to forecast their proficiency by the eighth grade. We conduct an extensive examination of tens of thousands of student records and extract useful information. We use this data to present a longitudinal analysis of student performance on the North Carolina End-of-Grade mathematics exam and use Markov chain models to probabilistically characterize the movement of students’ scores from one grade level to the next. This work is the first step in developing a framework to forecast individual students’ development of mathematical knowledge over time.}, journal={COMPUTERS IN HUMAN BEHAVIOR}, author={Reamer, Amy Craig and Ivy, Julie S. and Vila-Parrish, Anita R. and Young, Robert E.}, year={2015}, month={Jun}, pages={4–17} }
 @article{ivy_horney_maillard_2015, title={Using systems modeling to enhance public health preparedness}, volume={4}, ISSN={2047-6965 2047-6973}, url={http://dx.doi.org/10.1057/HS.2014.31}, DOI={10.1057/HS.2014.31}, abstractNote={As global public health threats increase in number and worldwide impact, we must explore novel and more advanced approaches to address the complex challenges associated with public health preparedness. Public health outbreaks do not adhere to municipal, state, or national boundaries, which makes the role of systems modeling all the more critical for developing effective and efficient public health preparedness strategies. In response to the need for global and multifaceted preparedness and response efforts, this special issue highlights novel systems modeling approaches that can be applied to public health preparedness. Through these illustrative papers, one goal of this special issue is to serve as a medium for communicating systems methods to public health practice and to increase practitioners’ awareness of the role that systems methods can play in addressing complex planning and implementation issues associated with public health preparedness.}, number={1}, journal={Health Systems}, publisher={Informa UK Limited}, author={Ivy, Julie Simmons and Horney, Jennifer and Maillard, Jean-Marie}, year={2015}, month={Mar}, pages={1–4} }
 @inproceedings{goodarzi_mckenzie_nataraj_ivy_mayorga_mason_tejada_2016, title={A Framework for modeling the complex interaction between breast cancer and diabetes}, DOI={10.1109/wsc.2014.7019981}, abstractNote={In 2010, over 200,000 women in the U.S. were diagnosed with invasive breast cancer, and an estimated 17% of those women died from the disease, according to the Centers for Disease Control and Prevention (CDC). Also in 2010, the CDC reported that 12.6 million women had diabetes, the seventh leading cause of death in the U.S. Recent medical literature provides conflicting evidence regarding a link between insulin resistance and breast cancer risk. Although models have characterized these prevalent diseases individually, little research has been conducted regarding the interaction between breast cancer and diabetes. We build a simulation model framework that explores this complex relationship, with an initial goal of assessing the prognosis for women diagnosed with diabetes considering their breast cancer risk. Using data from national survey and surveillance consortium studies, we estimate morbidity and mortality. This framework could be extended to study other diseases that interact with breast cancer.}, booktitle={2016 10th european conference on antennas and propagation (eucap)}, author={Goodarzi, S. H. and McKenzie, K. and Nataraj, N. and Ivy, J. S. and Mayorga, Maria and Mason, J. and Tejada, J.}, year={2016}, pages={1245–1256} }
 @article{tejada_ivy_wilson_ballan_diehl_yankaskas_2015, title={Combined DES/SD model of breast cancer screening for older women, I: Natural-history simulation}, volume={47}, ISSN={["1545-8830"]}, DOI={10.1080/0740817x.2014.959671}, abstractNote={Two companion articles develop and exploit a simulation modeling framework to evaluate the effectiveness of breast cancer screening policies for U.S. women who are at least 65 years old. This first article examines the main components in the breast cancer screening-and-treatment process for older women; then it introduces a two-phase simulation approach to defining and modeling those components. Finally this article discusses the first-phase simulation, a natural-history model of the incidence and progression of untreated breast cancer for randomly sampled individuals from the designated population of older U.S. women. The companion article details the second-phase simulation, an integrated screening-and-treatment model that uses information about the genesis of breast cancer in the sampled individuals as generated by the natural-history model to estimate the benefits of different policies for screening the designated population and treating the women afflicted with the disease. Both simulation models are composed of interacting sub-models that represent key aspects of the incidence, progression, screening, treatment, survival, and cost of breast cancer in the population of older U.S. women as well as the overall structure of the system for detecting and treating the disease.}, number={6}, journal={IIE TRANSACTIONS}, author={Tejada, Jeremy J. and Ivy, Julie S. and Wilson, James R. and Ballan, Matthew J. and Diehl, Kathleen M. and Yankaskas, Bonnie C.}, year={2015}, month={Jun}, pages={600–619} }
 @article{vila-parrish_ivy_he_2015, title={Impact of the influenza season on a hospital from a pharmaceutical inventory management perspective}, volume={4}, ISSN={2047-6965 2047-6973}, url={http://dx.doi.org/10.1057/HS.2014.13}, DOI={10.1057/HS.2014.13}, abstractNote={The outbreak of an infectious disease may put significant pressure on a healthcare system, especially when there is a surge of patients. In this paper, we develop two simulation models: (1) disease outbreak model and (2) a medication inventory model. These two models are used to identify inventory policies for managing medication during disease outbreaks. Specifically, we use historical influenza data as an input to the inventory simulation model, which incorporates the impact of disease spread, patients’ health conditions, and medication shelf life. We formulate a dynamic program and use a reduced version of this model to provide inputs to our inventory simulation model. We compare three different simulation-based policies and perform sensitivity analysis on several parameters such as the gross attack rate and inventory holding cost parameters. Our results provide insight regarding the management of perishable medication inventory at hospitals during an outbreak of an infectious disease.}, number={1}, journal={Health Systems}, publisher={Informa UK Limited}, author={Vila-Parrish, Anita R and Ivy, Julie Simmons and He, Beixiang}, year={2015}, month={Mar}, pages={12–28} }
 @article{yarmand_ivy_denton_lloyd_2014, title={Optimal two-phase vaccine allocation to geographically different regions under uncertainty}, volume={233}, ISSN={["1872-6860"]}, DOI={10.1016/j.ejor.2013.08.027}, abstractNote={In this article, we consider a decision process in which vaccination is performed in two phases to contain the outbreak of an infectious disease in a set of geographic regions. In the first phase, a limited number of vaccine doses are allocated to each region; in the second phase, additional doses may be allocated to regions in which the epidemic has not been contained. We develop a simulation model to capture the epidemic dynamics in each region for different vaccination levels. We formulate the vaccine allocation problem as a two-stage stochastic linear program (2-SLP) and use the special problem structure to reduce it to a linear program with a similar size to that of the first stage problem. We also present a Newsvendor model formulation of the problem which provides a closed form solution for the optimal allocation. We construct test cases motivated by vaccine planning for seasonal influenza in the state of North Carolina. Using the 2-SLP formulation, we estimate the value of the stochastic solution and the expected value of perfect information. We also propose and test an easy to implement heuristic for vaccine allocation. We show that our proposed two-phase vaccination policy potentially results in a lower attack rate and a considerable saving in vaccine production and administration cost.}, number={1}, journal={EUROPEAN JOURNAL OF OPERATIONAL RESEARCH}, author={Yarmand, Hamed and Ivy, Julie S. and Denton, Brian and Lloyd, Alun L.}, year={2014}, month={Feb}, pages={208–219} }
 @article{davis_sengul_ivy_brock_miles_2014, title={Scheduling food bank collections and deliveries to ensure food safety and improve access}, volume={48}, ISSN={0038-0121}, url={http://dx.doi.org/10.1016/J.SEPS.2014.04.001}, DOI={10.1016/J.SEPS.2014.04.001}, abstractNote={Food banks are privately-owned non-profit organizations responsible for the receipt, processing, storage, and distribution of food items to charitable agencies. These charitable agencies in turn distribute food to individuals at risk of hunger. Food banks receive donated food from national and local sources, such as The Emergency Food Assistance Program (TEFAP) and supermarkets. Local sources with frequent high-volume donations justify the use of food bank vehicles for collection. Food bank vehicles are also used to deliver food to rural charitable agencies that are located beyond a distance safe for perishable food to travel without spoilage. Due to limited funds, food banks can only afford to sparingly use their capital on non-food items. This requires exploring more cost effective food delivery and collection strategies. The goal of this paper is to develop transportation schedules that enable the food bank to both (i) collect food donations from local sources and (ii) to deliver food to charitable agencies. We identify satellite locations, called food delivery points (FDPs), where agencies can receive food deliveries. A set covering model is developed to determine the assignment of agencies to an FDP. Both vehicle capacity and food spoilage constraints are considered during assignment. Using the optimal assignment of agencies to FDPs, we identify a weekly transportation schedule that addresses collection and distribution of donated food and incorporates constraints related to food safety, operator workday, collection frequency, and fleet capacity.}, number={3}, journal={Socio-Economic Planning Sciences}, publisher={Elsevier BV}, author={Davis, Lauren B. and Sengul, Irem and Ivy, Julie S. and Brock, Luther G., III and Miles, Lastella}, year={2014}, month={Sep}, pages={175–188} }
 @inproceedings{hicklin_ivy_myers_kulkarni_viswanathan_2016, title={Simulation of labor: a study of the relationship between cesarean section rates and the time spent in labor}, DOI={10.1109/wsc.2014.7019983}, abstractNote={Cesarean delivery is the most common major abdominal surgery in many parts of the world. As of October 2012, the cesarean section rate in the United States was reported to be 32.8% in 2011, rising from 4.5% in 1970. Cesarean sections are associated with an increased risk of neonatal respiratory morbidity, increased risk of a hysterectomy and can cause major complications in subsequent pregnancies, such as uterine rupture. To evaluate the current cesarean delivery rate due to a “failure to progress” diagnosis, our goal was to replicate the delivery process for women undergoing a trial of labor. In this simulation we evaluate the Friedman Curve and other labor progression rules to identify circumstances in which the cesarean rate can be decreased through the analysis of the total length of time a woman spends in labor as well as the duration of time a woman remains in a cervical dilation stage.}, booktitle={2016 10th european conference on antennas and propagation (eucap)}, author={Hicklin, K. and Ivy, J. S. and Myers, E. R. and Kulkarni, V. and Viswanathan, M.}, year={2016}, pages={1269–1280} }
 @article{yaylali_ivy_taheri_2014, title={Systems Engineering Methods for Enhancing the Value Stream in Public Health Preparedness: The Role of Markov Models, Simulation, and Optimization}, volume={129}, ISSN={["0033-3549"]}, DOI={10.1177/00333549141296s419}, abstractNote={Objectives. Large-scale incidents such as the 2009 H1N1 outbreak, the 2011 European Escherichia coli outbreak, and Hurricane Sandy demonstrate the need for continuous improvement in emergency preparation, alert, and response systems globally. As questions relating to emergency preparedness and response continue to rise to the forefront, the field of industrial and systems engineering (ISE) emerges, as it provides sophisticated techniques that have the ability to model the system, simulate, and optimize complex systems, even under uncertainty. Methods. We applied three ISE techniques—Markov modeling, operations research (OR) or optimization, and computer simulation—to public health emergency preparedness. Results. We present three models developed through a four-year partnership with stakeholders from state and local public health for effectively, efficiently, and appropriately responding to potential public health threats: ( 1) an OR model for optimal alerting in response to a public health event, ( 2) simulation models developed to respond to communicable disease events from the perspective of public health, and ( 3) simulation models for implementing pandemic influenza vaccination clinics representative of clinics in operation for the 2009–2010 H1N1 vaccinations in North Carolina. Conclusions. The methods employed by the ISE discipline offer powerful new insights to understand and improve public health emergency preparedness and response systems. The models can be used by public health practitioners not only to inform their planning decisions but also to provide a quantitative argument to support public health decision making and investment. }, journal={PUBLIC HEALTH REPORTS}, author={Yaylali, Emine and Ivy, Julie Simmons and Taheri, Javad}, year={2014}, pages={145–153} }
 @article{yarmand_ivy_2013, title={Analytic solution of the susceptible-infective epidemic model with state-dependent contact rates and different intervention policies}, volume={89}, ISSN={["1741-3133"]}, DOI={10.1177/0037549713479052}, abstractNote={ We consider the susceptible-infective (SI) epidemiological model, a variant of the Kermack–McKendrick models, and let the contact rate be a function of the number of infectives, an indicator of disease spread during the course of the epidemic. We represent the resultant model as a continuous-time Markov chain. The result is a pure death (or birth) process with state-dependent rates, for which we find the probability distribution of the associated Markov chain by solving the Kolmogorov forward equations. This model is used to find the analytic solution of the SI model as well as the distribution of the epidemic duration. We use the maximum likelihood method to estimate contact rates based on observations of inter-infection time intervals. We compare the stochastic model to the corresponding deterministic models through a numerical experiment within a typical household. We also incorporate different intervention policies for vaccination, antiviral prophylaxis, isolation, and treatment considering both full and partial adherence to interventions among individuals. }, number={6}, journal={SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL}, author={Yarmand, Hamed and Ivy, Julie S.}, year={2013}, month={Jun}, pages={703–721} }
 @article{zhang_payton_ivy_2013, title={Characterizing the impact of mental disorders on HIV patient length of stay and total charges}, volume={3}, ISSN={1948-8300 1948-8319}, url={http://dx.doi.org/10.1080/19488300.2013.820238}, DOI={10.1080/19488300.2013.820238}, abstractNote={There are over one million people in the United States living with HIV/AIDS, 20% of whom are undiagnosed, increasing the risk of transmission and the burden on the healthcare system. Those with comorbid diseases may be particularly vulnerable. This paper studies the impact of comorbidities, with a particular focus on mental disorders, on HIV patient outcomes as measured by patient length of stay (LOS) and total charges. Generalized linear models (gamma models) allowing heteroscedasticity are developed to characterize the effects of selected comorbidities on HIV patient outcomes in the adult 2006 National Inpatient Sample. Comorbid HIV patients experience different LOS and total charges. In particular, having mental disorders resulted in a decrease in both LOS (19%) and total charges (15%) for HIV patients. To characterize the role of individual mental disorders, principal component and cluster analyses on ICD-9 codes are used to study the impact of mental disorder, and eight conditions are found to be most strongly associated with HIV. Gamma models with these identified mental disorders as independent variables are then developed. The results have shown different effects on LOS and charges for each condition, and special attention should be given to those mental disorders (e.g., drug dependence) that increased LOS and charges when present.}, number={3}, journal={IIE Transactions on Healthcare Systems Engineering}, publisher={Informa UK Limited}, author={Zhang, Shengfan and Payton, Fay Cobb and Ivy, Julie Simmons}, year={2013}, month={Jul}, pages={139–146} }
 @article{tejada_ivy_king_wilson_ballan_kay_diehl_yankaskas_2014, title={Combined DES/SD model of breast cancer screening for older women, II: screening-and-treatment simulation}, volume={46}, ISSN={0740-817X 1545-8830}, url={http://dx.doi.org/10.1080/0740817X.2013.851436}, DOI={10.1080/0740817x.2013.851436}, abstractNote={In the second article of a two-article sequence, the focus is on a simulation model for screening and treatment of breast cancer in U.S. women of age 65+. The first article details a natural-history simulation model of the incidence and progression of untreated breast cancer in a representative simulated population of older U.S. women, which ultimately generates a database of untreated breast cancer histories for individuals in the simulated population. Driven by the resulting database, the screening-and-treatment simulation model is composed of discrete-event simulation (DES) and system dynamics (SD) submodels. For each individual in the simulated population, the DES submodel simulates screening policies and treatment procedures to estimate the resulting survival rates and the costs of screening and treatment. The SD submodel represents the overall structure and operation of the U.S. system for detecting and treating breast cancer. The main results and conclusions are summarized, including a final recommendation for annual screening between ages 65 and 80. A discussion is also presented on how both the natural-history and screening-and-treatment simulations can be used for performance comparisons of proposed screening policies based on overall cost-effectiveness, the numbers of life-years and quality-adjusted life-years saved, and the main components of the total cost incurred by each policy.}, number={7}, journal={IIE Transactions}, publisher={Informa UK Limited}, author={Tejada, Jeremy J. and Ivy, Julie S. and King, Russell E. and Wilson, James R. and Ballan, Matthew J. and Kay, Michael G. and Diehl, Kathleen M. and Yankaskas, Bonnie C.}, year={2014}, month={Mar}, pages={707–727} }
 @article{zhang_ivy_wilson_diehl_yankaskas_2014, title={Competing risks analysis in mortality estimation for breast cancer patients from independent risk groups}, volume={17}, ISSN={["1572-9389"]}, DOI={10.1007/s10729-013-9255-x}, abstractNote={This study quantifies breast cancer mortality in the presence of competing risks for complex patients. Breast cancer behaves differently in different patient populations, which can have significant implications for patient survival; hence these differences must be considered when making screening and treatment decisions. Mortality estimation for breast cancer patients has been a significant research question. Accurate estimation is critical for clinical decision making, including recommendations. In this study, a competing risks framework is built to analyze the effect of patient risk factors and cancer characteristics on breast cancer and other cause mortality. To estimate mortality probabilities from breast cancer and other causes as a function of not only the patient's age or race but also biomarkers for estrogen and progesterone receptor status, a nonparametric cumulative incidence function is formulated using data from the community-based Carolina Mammography Registry. Based on the log(-log) transformation, confidence intervals are constructed for mortality estimates over time. To compare mortality probabilities in two independent risk groups at a given time, a method with improved power is formulated using the log(-log) transformation.}, number={3}, journal={HEALTH CARE MANAGEMENT SCIENCE}, author={Zhang, Shengfan and Ivy, Julie S. and Wilson, James R. and Diehl, Kathleen M. and Yankaskas, Bonnie C.}, year={2014}, month={Sep}, pages={259–269} }
 @article{yarmand_ivy_roberts_2013, title={Identifying optimal mitigation strategies for responding to a mild influenza epidemic}, volume={89}, ISSN={["1741-3133"]}, DOI={10.1177/0037549713505334}, abstractNote={ Mathematical models have been developed to simulate influenza epidemics to help public health officials evaluate different control policies. In these models, often severe influenza epidemics with a considerable mortality rate are considered. However, as was the case for the 2009 H1N1 pandemic, some of the influenza epidemics are mild with insignificant mortality rates. In the case of a mild epidemic, the cost of different control policies becomes an important decision factor in addition to disease-related outcomes such as the attack rate.  We develop a continuous-time simulation model for the spread of a mild influenza epidemic based on the SEIR model (an epidemiological model with four classes: susceptible, exposed, infective, and recovered) which includes different interventions. To determine the epidemic mitigation policy with the minimum cost, we also develop an optimization model with two decision variables, vaccination and self-isolation fractions, and an upper-bound constraint for the attack rate. We use this model to evaluate the cost-effectiveness of different mitigation policies. Furthermore, we integrate the simulation and optimization models to identify the optimal mitigation policy. Finally, we conduct sensitivity analysis on the key input parameters to ensure robust results.  The optimal policy depends on the target population and, as our results show, in general is a combination of vaccination and self-isolation. Further, for low (high) levels of intervention, vaccination (self-isolation) is incrementally more cost-effective. Therefore, public health officials should concentrate on vaccination at the beginning of the epidemic. However, if the epidemic continues to spread, they should promote self-isolation as a more effective intervention. }, number={11}, journal={SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL}, author={Yarmand, Hamed and Ivy, Julie S. and Roberts, Stephen D.}, year={2013}, month={Nov}, pages={1400–1415} }
 @article{yarmand_ivy_2013, title={Optimal intervention strategies for an epidemic: A household view}, volume={89}, ISSN={["1741-3133"]}, DOI={10.1177/0037549713505333}, abstractNote={ In this research, we identify optimal intervention strategies at the household level in case of an epidemic. We consider an affected household (a household with one initial infective member) and model the effect of different intervention policies, which involve vaccination, antiviral prophylaxis, isolation, and treatment, on disease spread using a variation of Kermack–McKendrick (KM) models. Both full and partial adherence to interventions are considered. An implementation cost is assumed for each intervention policy. We refer to a collection of intervention policies as an intervention strategy. A reward is associated with susceptible members who remain uninfected. We define the effect of the implemented intervention strategy as the total reward earned by all members over the time horizon. We then identify the most cost-effective intervention strategies. In addition, we incorporate a budgetary constraint for the household and find the efficient frontier for the total reward over different upper bounds on the household budget. }, number={12}, journal={SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL}, author={Yarmand, Hamed and Ivy, Julie S.}, year={2013}, month={Dec}, pages={1505–1522} }
 @article{vila-parrish_ivy_king_abel_2012, title={Patient-based pharmaceutical inventory management: a two-stage inventory and production model for perishable products with Markovian demand}, volume={1}, ISSN={2047-6965 2047-6973}, url={http://dx.doi.org/10.1057/hs.2012.2}, DOI={10.1057/hs.2012.2}, abstractNote={Drug shortages have increased over the past decade, tripling since 2006. Pharmacy material managers are challenged with developing inventory policies given changing demand, limited suppliers, and regulations affecting supply. Pharmaceutical inventory management and patient care are inextricably linked; suboptimal control impacts both patient treatment and the cost of care. We study a perishable inventory problem motivated by challenges in pharmaceutical management. Inpatient hospital pharmacies stock medications in two stages, raw material and finished good (e.g. intravenous). While both stages of material are perishable, the finished form is highly perishable. Pharmacy demand depends on the population and patient conditions. We use a stochastic 'demand state' as a surrogate for patient condition and develop a Markov decision process to determine optimal, state-dependent two-stage inventory and production policies. We define two ordering and production scenarios, prove the existence of optimal solutions for both scenarios, and apply this framework to the management of Meropenem.}, number={1}, journal={Health Systems}, publisher={Informa UK Limited}, author={Vila-Parrish, Ana R and Ivy, Julie S and King, Russell E and Abel, Steven R}, year={2012}, month={Jun}, pages={69–83} }
 @article{winter_ivy_horney_2012, title={Simulating Public Health Capacity to Measure Performance:E. coliO157 at the North Carolina State Fair}, volume={3}, ISSN={1944-4079}, url={http://dx.doi.org/10.1002/RHC3.12}, DOI={10.1002/RHC3.12}, abstractNote={AbstractDespite the investment of billions of dollars in federal funding for emergency preparedness and response initiatives, broadly accepted performance measures for determining the efficacy of these systems have yet to be established. The inability to accurately capture this information hinders the ability to measure the true degree of preparedness. The North Carolina Public Health Information Network (NC PHIN), a key component of North Carolina's public health system, has promise as a means to measure emergency preparedness and response. In this paper, we look at how NC PHIN has increased emergency preparedness and response capacity by presenting a simulation of the 2004 State Fair Escherichia coli outbreak. We found that although the capacity exists within NC PHIN to increase emergency preparedness and response, some factors limit NC PHIN's effectiveness. Our findings suggest that proper resource allocation will be necessary in order to realize the potential of NC PHIN.}, number={3}, journal={Risk, Hazards & Crisis in Public Policy}, publisher={Wiley}, author={Winter, Sharolyn A. and Ivy, Julie S. and Horney, Jennifer A.}, year={2012}, month={Sep}, pages={69–82} }
 @article{zhang_ivy_diehl_yankaskas_2013, title={The association of breast density with breast cancer mortality in African American and white women screened in community practice}, volume={137}, ISSN={["1573-7217"]}, DOI={10.1007/s10549-012-2310-3}, abstractNote={The effect of breast density on survival outcomes for American women who participate in screening remains unknown. We studied the role of breast density on both breast cancer and other cause of mortality in screened women. Data for women with breast cancer, identified from the community-based Carolina Mammography Registry, were linked with the North Carolina cancer registry and NC death tapes for this study. Cause-specific Cox proportional hazards models were developed to analyze the effect of several covariates on breast cancer mortality-namely, age, race (African American/White), cancer stage at diagnosis (in situ, local, regional, and distant), and breast density (BI-RADS( ® ) 1-4). Two stratified Cox models were considered controlling for (1) age and race, and (2) age and cancer stage, respectively, to further study the effect of density. The cumulative incidence function with confidence interval approximation was used to quantify mortality probabilities over time. For this study, 22,597 screened women were identified as having breast cancer. The non-stratified and stratified Cox models showed no significant statistical difference in mortality between dense tissue and fatty tissue, while controlling for other covariate effects (p value = 0.1242, 0.0717, and 0.0619 for the non-stratified, race-stratified, and cancer stage-stratified models, respectively). The cumulative mortality probability estimates showed that women with dense breast tissues did not have significantly different breast cancer mortality than women with fatty breast tissue, regardless of age (e.g., 10-year confidence interval of mortality probabilities for whites aged 60-69 white: 0.056-0.090 vs. 0.054-0.083). Aging, African American race, and advanced cancer stage were found to be significant risk factors for breast cancer mortality (hazard ratio >1.0). After controlling for cancer incidence, there was not a significant association between mammographic breast density and mortality, adjusting for the effects of age, race, and cancer stage.}, number={1}, journal={BREAST CANCER RESEARCH AND TREATMENT}, author={Zhang, Shengfan and Ivy, Julie S. and Diehl, Kathleen M. and Yankaskas, Bonnie C.}, year={2013}, month={Jan}, pages={273–283} }
 @article{wu_viswanathan_ivy_2012, title={A Conceptual Framework for Future Research on Mode of Delivery}, volume={16}, ISSN={["1092-7875"]}, DOI={10.1007/s10995-011-0910-x}, abstractNote={Our goal was to develop a comprehensive conceptual research framework on mode of delivery and to identify research priorities in this topic area through a Delphi process. We convened a multidisciplinary team of 16 experts (North Carolina Collaborative on Mode of Delivery) representing the fields of obstetrics and gynecology, neonatology, midwifery, epidemiology, psychometrics, decision sciences, bioethics, health care engineering, health economics, health disparities, and women's studies. We finalized the conceptual framework after multiple iterations, including revisions during a one-day in-person conference. The conceptual framework illustrates the causal pathway for mode of delivery and the complex interplay and relationships among patient, fetal, family, provider, cultural, and societal factors as drivers of change from intended to actual mode of delivery. This conceptual framework on mode of delivery will help put specific research ideas into a broader context and identify important knowledge gaps for future investigation.}, number={7}, journal={MATERNAL AND CHILD HEALTH JOURNAL}, author={Wu, Jennifer M. and Viswanathan, Meera and Ivy, Julie S.}, year={2012}, month={Oct}, pages={1447–1454} }
 @article{yarmand_ivy_roberts_bengtson_bengtson_2010, title={COST-EFFECTIVENESS ANALYSIS OF VACCINATION AND SELF-ISOLATION IN CASE OF H1N1}, ISSN={["0891-7736"]}, DOI={10.1109/wsc.2010.5678918}, abstractNote={In this research, we have conducted a cost-effectiveness analysis to examine the relative importance of vaccination and self-isolation, with respect to the current H1N1 outbreak. We have developed a continuous-time simulation model for the spread of H1N1 which allows for three types of interventions: antiviral prophylaxis and treatment, vaccination, and self-isolation and mandatory quarantine. The optimization model consists of two decision variables: vaccination fraction and self-isolation fraction among infectives. By considering the relative marginal costs associated with each of these decision variables, we have a linear objective function representing the total relative cost for each control policy. We have also considered upper bound constraints for maximum number of individuals under treatment (which is related to surge capacity) and percentage of infected individuals (which determines the attack rate). We have used grid search to obtain insight into the model, find the feasible region, and conduct the cost-effectiveness analysis.}, journal={PROCEEDINGS OF THE 2010 WINTER SIMULATION CONFERENCE}, author={Yarmand, Hamed and Ivy, Julie S. and Roberts, Stephen D. and Bengtson, Mary W. and Bengtson, Neal M.}, year={2010}, pages={2199–2210} }
 @misc{xu_ivy_patel_patel_smith_ransom_fenner_delancey_2010, title={Pelvic Floor Consequences of Cesarean Delivery on Maternal Request in Women with a Single Birth: A Cost-effectiveness Analysis}, volume={19}, ISSN={["1931-843X"]}, DOI={10.1089/jwh.2009.1404}, abstractNote={BACKGROUND
The potential benefit in preventing pelvic floor disorders (PFDs) is a frequently cited reason for requesting or performing cesarean delivery on maternal request (CDMR). However, for primigravid women without medical/obstetric indications, the lifetime cost-effectiveness of CDMR remains unknown, particularly with regard to lifelong pelvic floor consequences. Our objective was to assess the cost-effectiveness of CDMR in comparison to trial of labor (TOL) for primigravid women without medical/obstetric indications with a single childbirth over their lifetime, while explicitly accounting for the management of PFD throughout the lifetime.


METHODS
We used Monte Carlo simulation of a decision model containing 249 chance events and 101 parameters depicting lifelong maternal and neonatal outcomes in the following domains: actual mode of delivery, emergency hysterectomy, transient maternal morbidity and mortality, perinatal morbidity and mortality, and the lifelong management of PFDs. Parameter estimates were obtained from published literature. The analysis was conducted from a societal perspective. All costs and quality-adjusted life-years (QALYs) were discounted to the present value at childbirth.


RESULTS
The estimated mean cost and QALYs were $14,259 (95% confidence interval [CI] $8,964-$24,002) and 58.21 (95% CI 57.43-58.67) for CDMR and $13,283 (95% CI $7,861-$23,829) and 57.87 (95% CI 56.97-58.46) for TOL over the combined lifetime of the mother and the child. Parameters related to PFDs play an important role in determining cost and quality of life.


CONCLUSIONS
When a woman without medical/obstetric indications has only one childbirth in her lifetime, cost-effectiveness analysis does not reveal a clearly preferable mode of delivery.}, number={1}, journal={JOURNAL OF WOMENS HEALTH}, author={Xu, Xiao and Ivy, Julie S. and Patel, Divya A. and Patel, Sejal N. and Smith, Dean G. and Ransom, Scott B. and Fenner, Dee and DeLancey, John O. L.}, year={2010}, month={Jan}, pages={147–160} }
 @article{kuhl_ivy_lada_steiger_wagner_wilson_2010, title={Univariate input models for stochastic simulation}, volume={4}, ISSN={1747-7778 1747-7786}, url={http://dx.doi.org/10.1057/jos.2009.31}, DOI={10.1057/jos.2009.31}, abstractNote={Techniques are presented for modelling and then randomly sampling many of the continuous univariate probabilistic input processes that drive discrete-event simulation experiments. Emphasis is given to the generalized beta distribution family, the Johnson translation system of distributions, and the Bézier distribution family because of the flexibility of these families to model a wide range of distributional shapes that arise in practical applications. Methods are described for rapidly fitting these distributions to data or to subjective information (expert opinion) and for randomly sampling from the fitted distributions. Also discussed are applications ranging from pharmaceutical manufacturing and medical decision analysis to smart-materials research and health-care systems analysis.}, number={2}, journal={Journal of Simulation}, publisher={Informa UK Limited}, author={Kuhl, M E and Ivy, J S and Lada, E K and Steiger, N M and Wagner, M A and Wilson, J R}, year={2010}, month={Jun}, pages={81–97} }
 @article{zhang_ivy_payton_diehl_2010, title={Modeling the impact of comorbidity on breast cancer patient outcomes}, volume={13}, ISSN={["1572-9389"]}, DOI={10.1007/s10729-009-9119-6}, abstractNote={The objective of this paper is to model the impact of comorbidity on breast cancer patient outcomes (e.g., length of stay and disposition). Previous studies suggest that comorbidities may significantly affect mortality risks for breast cancer patients. The 2006 AHRQ Nationwide Inpatient Sample (NIS) is used to analyze the relationships among comorbidities (e.g., hypertension, diabetes, obesity, and mental disorder), total charges, length of stay, and patient disposition as a function of age and race. A multifaceted approach is used to quantify these relationships. A causal study is performed to explore the effect of various comorbidities on patient outcomes. Least squares regression models are developed to evaluate and compare significant factors that influence total charges and length of stay. Logistic regression is used to study the factors that may cause patient mortality or transferring. In addition, different survival models are developed to study the impact of comorbidity on length of stay with censoring information. This study shows the interactions and relationship among various comorbidities and breast cancer. It shows that certain hypertension may not increase length of stay and total charges; diabetes behaves differently among general population and breast cancer patients; mental disorder has an impact on patient disposition that affects true length of stay and charges, and obesity may have limited effect on patient outcomes. Moreover, this study will help to better understand the expenditure patterns for population subgroups with several chronic conditions and to quantify the impact of comorbidities on patient outcomes. Lastly, it also provides insight for breast cancer patients with comorbidities as a function of age and race.}, number={2}, journal={HEALTH CARE MANAGEMENT SCIENCE}, author={Zhang, Shengfan and Ivy, Julie Simmons and Payton, Fay Cobb and Diehl, Kathleen M.}, year={2010}, month={Jun}, pages={137–154} }
 @article{simmons ivy_black nembhard_baran_2009, title={Quantifying the Impact of Variability and Noise on Patient Outcomes in Breast Cancer Decision Making}, volume={21}, ISSN={0898-2112 1532-4222}, url={http://dx.doi.org/10.1080/08982110902762634}, DOI={10.1080/08982110902762634}, abstractNote={ABSTRACT There are many factors that can affect the breast cancer decision-making process. This article addresses issues of uncertainty. Specifically, we seek to answer two questions: (1) What are the major contributors to false positive test results for patients? (2) How does variability between different radiologists affect outcomes for patients? We develop a simulation-based model that combines statistical process control (SPC) with a partially observable Markov decision process (POMDP) to incorporate uncertainty, the inherent variability between radiologists, and system noise (i.e., screening characteristics such as different densities of breast tissue, inherent variability between different women, and imperfections in the mammogram and technology) to determine the impact on the breast cancer monitoring decision. When compared to population-based noise, we find that the variability among different radiologists in the ability to correctly interpret a mammogram has the most significant impact on whether a woman will receive incorrect results. Variability within the population of radiologists significantly increases in the number of false-positive mammogram results a woman receives. This suggests that reducing the variability between radiologists should be a primary concern to improve health care for women.}, number={3}, journal={Quality Engineering}, publisher={Informa UK Limited}, author={Simmons Ivy, Julie and Black Nembhard, Harriet and Baran, Kimberly}, year={2009}, month={Jun}, pages={319–334} }
 @article{vila-parrish_ivy_king_2008, title={A SIMULATION-BASED APPROACH FOR INVENTORY MODELING OF PERISHABLE PHARMACEUTICALS}, ISBN={["978-1-4244-2707-9"]}, DOI={10.1109/wsc.2008.4736234}, abstractNote={Pharmaceutical expenditures are increasing for hospital systems nationwide. We model the inventory and ordering policies for perishable drugs in the setting of an inpatient hospital pharmacy. We consider two stages of inventory: raw material and finished good (e.g. intravenous). We use a two-phased approach to explore policy structures that could be implemented in the hospital pharmacy. We develop a policy which is based on the idea that hospitals can improve both costs and patient demand fulfillment by using knowledge of patient mix to guide their drug inventory and preparation decisions. We compare this policy to a simpler stationary base stock policy. The policies are evaluated on the basis of (1) shortage cost, (2) outdating cost (expirations), and (3) holding cost through a range of cost scenarios.}, journal={2008 WINTER SIMULATION CONFERENCE, VOLS 1-5}, author={Vila-Parrish, Ana R. and Ivy, Julie Simmons and King, Russell E.}, year={2008}, pages={1532–1538} }
 @article{maillart_ivy_ransom_diehl_2008, title={Assessing Dynamic Breast Cancer Screening Policies}, volume={56}, ISSN={["0030-364X"]}, DOI={10.1287/opre.1080.0614}, abstractNote={ Questions regarding the relative value and frequency of mammography screening for premenopausal women versus postmenopausal women remain open due to the conflicting age-based dynamics of both the disease (increasing incidence, decreasing aggression) and the accuracy of the test results (increasing sensitivity and specificity). To investigate these questions, we formulate a partially observed Markov chain model that captures several of these age-based dynamics not previously considered simultaneously. Using sample-path enumeration, we evaluate a broad range of policies to generate the set of “efficient” policies, as measured by a lifetime breast cancer mortality risk metric and an expected mammogram count, from which a patient may select a policy based on individual circumstance. We demonstrate robustness with respect to small changes in the input data and conclude that, in general, to efficiently achieve a lifetime risk comparable to the current risk among U.S. women, screening should start relatively early in life and continue relatively late in life regardless of the screening interval(s) adopted. The frontier also exhibits interesting patterns with respect to policy type, where policy type is defined by the relationship between the screening interval prescribed in younger years and that prescribed later in life. }, number={6}, journal={OPERATIONS RESEARCH}, author={Maillart, Lisa M. and Ivy, Julie Simmons and Ransom, Scott and Diehl, Kathleen}, year={2008}, pages={1411–1427} }
 @article{zhou_djurdjanovic_ivy_ni_2007, title={Integrated reconfiguration and age-based preventive maintenance decision making}, volume={39}, ISSN={0740-817X 1545-8830}, url={http://dx.doi.org/10.1080/07408170701291779}, DOI={10.1080/07408170701291779}, abstractNote={The use of manufacturing system reconfiguration in conjunction with maintenance operations has not been previously reported in the literature. This research attempts to incorporate reconfiguration into Preventive Maintenance (PM) actions for improved system performance in terms of reduced total cost. This paper presents an Integrated Reconfiguration and Age-Based Maintenance (IRABM) policy and applies it to a parallel-serial manufacturing system. The expected total cost of implementing the IRABM policy is estimated and minimized through a simulation-based heuristic optimization procedure. Using this method, it is possible to systematically identify the conditions under which the integration of reconfiguration into maintenance is cost effective. In addition, numerical examples demonstrate that the manufacturing system could have a higher probability of fulfilling production requirements at a lower cost under the IRABM policy compared to the conventional age-based PM policy. The influences of the input parameters associated with reconfiguration, production, and reliability on the performance of IRABM policy also are studied.}, number={12}, journal={IIE Transactions}, publisher={Informa UK Limited}, author={Zhou, Jing and Djurdjanovic, Dragan and Ivy, Julie and Ni, Jun}, year={2007}, month={Oct}, pages={1085–1102} }
 @article{patel_xu_thomason_ransom_ivy_delancey_2006, title={Childbirth and pelvic floor dysfunction: An epidemiologic approach to the assessment of prevention opportunities at delivery}, volume={195}, ISSN={0002-9378}, url={http://dx.doi.org/10.1016/j.ajog.2006.01.042}, DOI={10.1016/j.ajog.2006.01.042}, abstractNote={Female pelvic floor dysfunction is integral to the woman's role in the reproductive process, largely because of the unique anatomic features that facilitate vaginal birth and also because of the trauma that can occur during that event. Interventions such as primary elective cesarean delivery have been discussed for the primary prevention of pelvic floor dysfunction; however, existing data about potentially causal factors limit our ability to evaluate such strategies critically. Here we consider the conceptual principles of epidemiologic function and the availability of data that are necessary to make informed recommendations about prevention opportunities for pelvic floor dysfunction at delivery. Available epidemiologic data on pelvic floor dysfunction suggest that there may be substantial opportunities for the primary prevention of pelvic organ prolapse at delivery. Although definitive recommendations await further epidemiologic studies of the potential risk and benefits of obstetric practice change, it is hoped that this discussion will provide a novel, quantitative framework for the assessment of pelvic floor dysfunction prevention opportunities. Female pelvic floor dysfunction is integral to the woman's role in the reproductive process, largely because of the unique anatomic features that facilitate vaginal birth and also because of the trauma that can occur during that event. Interventions such as primary elective cesarean delivery have been discussed for the primary prevention of pelvic floor dysfunction; however, existing data about potentially causal factors limit our ability to evaluate such strategies critically. Here we consider the conceptual principles of epidemiologic function and the availability of data that are necessary to make informed recommendations about prevention opportunities for pelvic floor dysfunction at delivery. Available epidemiologic data on pelvic floor dysfunction suggest that there may be substantial opportunities for the primary prevention of pelvic organ prolapse at delivery. Although definitive recommendations await further epidemiologic studies of the potential risk and benefits of obstetric practice change, it is hoped that this discussion will provide a novel, quantitative framework for the assessment of pelvic floor dysfunction prevention opportunities.}, number={1}, journal={American Journal of Obstetrics and Gynecology}, publisher={Elsevier BV}, author={Patel, Divya A. and Xu, Xiao and Thomason, Angela D. and Ransom, Scott B. and Ivy, Julie S. and DeLancey, John O.L.}, year={2006}, month={Jul}, pages={23–28} }
 @article{ivy_nembhard_2005, title={A Modeling Approach to Maintenance Decisions Using Statistical Quality Control and Optimization}, volume={21}, ISSN={0748-8017 1099-1638}, url={http://dx.doi.org/10.1002/qre.616}, DOI={10.1002/qre.616}, abstractNote={Maintenance concerns impact systems in every industry and effective maintenance policies are important tools. We present a methodology for maintenance decision making for deteriorating systems under conditions of uncertainty that integrates statistical quality control (SQC) and partially observable Markov decision processes (POMDPs). We use simulation to develop realistic maintenance policies for real-world environments. Specifically, we use SQC techniques to sample and represent real-world systems. These techniques help define the observation distributions and structure for a POMDP. We propose a simulation methodology for integrating SQC and POMDPs in order to develop and valuate optimal maintenance policies as a function of process characteristics, system operating and maintenance costs. A two-state machine replacement problem is used as an example of how the method can be applied. A simulation program developed using Visual Basic for Excel yields results on the optimal probability threshold and on the accuracy of the decisions as a function of the initial belief about the condition of the machine. This work lays a foundation for future research that will help bring maintenance decision models into practice. Copyright © 2005 John Wiley & Sons, Ltd.}, number={4}, journal={Quality and Reliability Engineering International}, publisher={Wiley}, author={Ivy, Julie Simmons and Nembhard, Harriet Black}, year={2005}, pages={355–366} }