We present a framework for constructing individual-level metamodels of complex simulations to support rapid, data-driven decision-making. By training models at the individual level and aggregating predictions, our framework enables flexible representation of any target population and facilitates comparison of alternative strategies. We explore regression-based and machine learning-based approaches and demonstrate the framework's utility in a colorectal cancer screening case study spanning diverse scenarios and interventions. Outputs such as cancer cases averted and life years lost are accurately predicted, with metamodels closely reproducing simulation results while substantially reducing computational burden. We further show how metamodel outputs can support cost-effectiveness comparisons and guide intervention selection without additional simulation runs. Although demonstrated in a colorectal cancer screening context, the proposed framework can be used in many domains and can be applied to simulations that produce individual-level outputs. Overall, our framework provides a scalable approach for accelerating policy evaluation and strategy comparison across diverse healthcare and operations environments.

Evacuation planning for disaster preparedness requires making critical decisions under uncertainty before the number and spatial distribution of evacuees are known, including shelter location, evacuation route assignment, and relief supply prepositioning. Because these decisions are highly interdependent, planners must balance the competing objectives of maximizing relief demand coverage and minimizing evacuation time. We propose, to our knowledge, the first adaptive robust evacuation planning model to jointly optimize shelter locations, evacuation route assignments, relief supply prepositioning, and post-disaster relief item distribution. The model minimizes the worst-case weighted sum of unmet demand for relief items across shelters and the congestion-dependent evacuation time. We characterize theoretical complexity drivers of the resulting problem with mixed-integer recourse and develop a partition-and-bound algorithm that maintains tractability by selectively partitioning only the most critical subpartition of the uncertainty set while producing strong upper and lower bounds. To quantify the value of centralized route planning, we also formulate a user route choice alternative in which evacuees choose among acceptable routes. Computational experiments quantify the value of centralized route planning, which reduces worst-case unmet demand and evacuation time by up to 90.6% and 79.3%, respectively, relative to decentralized user route choice. Adaptive post-disaster supply redistribution further improves relief demand coverage. Coordination between evacuation routing and relief distribution creates substantial operational value under uncertainty. Centralized route planning primarily mitigates congestion by coordinating evacuee flows across shelters, whereas adaptive redistribution primarily improves relief demand coverage when relief supplies are scarce or inflexibly prepositioned.

The Colorectal Cancer Control Program (CRCCP) aimed to increase colorectal cancer (CRC) screening among U.S. medically underserved populations through promotion and provision of CRC screening. We used simulation modeling to estimate the lifelong health impact and program cost-effectiveness of direct screening services, typically a single cycle of routine screening/follow-up testing provided through the CRCCP (“intervention”). Data for this study were from CDC’s Colorectal Clinical Data Elements (CCDE), which captured screening and follow-up services received from CRCCP between 2009 and 2020. We used microsimulation to model the evolution of polyps and CRC for average-risk individuals in intervention and “counterfactual” (control) groups, under multiple scenarios. We calculated and compared lifetime CRC outcomes (cases, deaths, life-years) for individuals with and without the CRCCP intervention. Clinical and implementation costs incurred by the CRCCP were used to estimate programmatic/intervention costs. Results are reported overall and by initial screening modality received (colonoscopy or stool testing) and assumed lower vs. higher “background” (non-intervention) screening scenarios. With conservative assumptions, our findings suggest that CRCCP-provided screening averted 806 CRC cases, avoided 392 CRC deaths, and added 5,368 life-years per 100,000 individuals vs. no intervention. Cost-effectiveness analysis revealed that the program’s cost per life-year gained varied by screening modality and scenario assumptions—ranging from 25,740 to27,583 for colonoscopy screening and 70,410 to75,979 for stool testing. CRCCP-provided screening/testing services were found to produce substantial potential health gains. Our analysis estimates the cost-effectiveness of providing one cycle of screening/testing to medically underserved individuals to inform programmatic decisions.

Background. COVID-19 tremendously disrupted the global health system. People of all ages were at risk of becoming infected. Frequent school closures raised concerns about both the physical and mental health of school-age children. Many studies discussed the effectiveness of various interventions, while few focused on optimizing such interventions. Methods. This study aimed to optimize the usage of random screening tests and masking requirements within K-12 schools. We simulated the disease transmission within a school setting and sought to find the most efficient schedules for schools to arrange their weekly screening tests and mask mandates. The goal was to minimize the number of the end-of-semester infections as well as to use the minimum number of resources. We applied the nondominated sorting genetic algorithm, NSGA-II, to solve this multiobjective optimization problem. We also compared results when polymerase chain reaction (PCR) versus rapid antigen tests were used. Results. The NSGA successfully found Pareto solutions when optimizing the end-of-semester infections, the total number of tests, and the total number of weeks masking. The screening tests and masks can serve as alternatives to one another when prioritizing minimizing the number of infections. In addition, due to the faster return of testing results and lower accuracy, the rapid antigen tests had a similar effect as PCR tests. Conclusion. Our study provides policy makers in K-12 schools with valuable insights. The conclusions derived from this research can serve as a solid foundation for making informative decisions regarding random screening tests and universal masking policies. Highlights Our simulation optimization framework was used to design weekly schedules for random screening tests and masking within K-12 schools to mitigate COVID-19 infections. We considered multiple objectives and applied the NSGA-II algorithm to find a Pareto solution set. Based on local context and preferences, decision makers can trade off testing and masking to achieve a similar number of end-of-semester infections. When a few weeks of masks are mandated, it is best to use them at the beginning of a semester.

Diabetic Retinopathy (DR) is a complication related to diabetes that can lead to vision impairment. To assist DR patients, a care management company provides a telephone-based principal care management (PCM) service, which includes care coaching and other services to reduce barriers to care for patients with DR. Despite its benefits, enrollment in the program is suboptimal. This study developed predictive models using call transcripts to investigate factors associated with patient enrollment in the PCM service. We analyzed transcripts of calls made during the enrollment process (prior to enrollment) and feature-engineered the call metadata (i.e., transcript length, number of calls, time between calls, customer and agent sentiment). In addition, we extracted topics discussed in the transcripts using Structural Topic Modeling (STM) and converted them into vector representations. Utilizing call metadata alongside topics, we developed three classification models (call metadata, topic-based, and topic+metadata) to predict patient enrollment, with the latter demonstrating superior performance. The topic+metadata classification model outperformed the other two models in distinguishing between patient enrollment and non-enrollment, with AUC values ranging from 0.81 to 0.99 across models using 3 to 15-topics. The findings suggest that proactively offering to schedule an appointment after the program benefits explanation leads to a higher odds of enrollment. When the scheduling portion of the conversation is not considered, agents should cover all parts of the script over multiple calls. Additionally, agents who explain the program and maintain longer intervals between calls have higher odds of patient enrollment, suggesting that there is value in allowing patients adequate time to reflect between calls. These findings offer valuable insights for agents to evaluate their strategies in patient enrollment. As the first point of contact, enrollment agents play a crucial role in determining whether patients can benefit from care coordination and management programs.

Vaccination is a critical intervention to mitigate the impact of infectious disease outbreaks. However, vaccination decision is complex and influenced by various factors such as individual beliefs, access to vaccines, trust in healthcare systems, and importantly, social norms within communities, the shared understandings and expectations about vaccination behavior. This paper analyzes the impact of social norms on vaccine uptake and subsequent disease transmission by explicitly incorporating these norms into an extension of the agent-based COVID-19 simulation model, COVASIM. We aim to analyze how social norms affect vaccination rates and disease spread. We demonstrate this by implementing community specific vaccination norms that influence agents through the perceived vaccination behaviors of their social networks. Our simulated case study explored targeted communication about vaccination uptake through different age groups. Through this intervention, we examined the effectiveness of adjusting perceptions of community vaccine uptake to better align with its true value.

This paper is motivated by a panel organized by the Healthcare and Life Sciences track at the 2025 Winter Simulation Conference (WSC). We summarize the panelists' perspectives and reflect on current trends and future research directions for simulation applications in healthcare and life sciences. We begin with a brief review of key methodologies and application trends from the past decade of WSC proceedings. We then present expert insights from a range of application areas, including (bio)pharmaceutical manufacturing, hospital operations, public health and epidemiology, and modeling human behavior. The panelists provide diverse perspectives from academia and industry, and highlight emerging challenges, opportunities, and future research directions to advance simulation in healthcare and life sciences.

The modelling of human behavior is a critical component of any simulation tool that aims to represent the spread of an infectious disease throughout a population. However, few modeling approaches attempt to incorporate protective behaviors using models grounded in theories from the behavioral sciences. Here, we demonstrate how to incorporate human behavior accounting for personal beliefs and perceptions by using a commonly known behavioral framework. We implemented the proposed model within an agent-based simulation to drive the agent's decision related to wearing a face mask. We used survey data to characterize a synthetic population, and investigate the effect of policies that aim to modify beliefs with the goal of promoting face mask usage. Our results highlight the importance of incorporating the individual drivers of behavior to better represent adoption of protective actions against health threats, enhancing the ability of simulation tools to quantify the impact of policy interventions.

We assessed the potential impact of introducing rubella-containing vaccine (RCV) on congenital rubella syndrome (CRS) incidence in Afghanistan (AFG), Democratic Republic of Congo (COD), Ethiopia (ETH), Nigeria (NGA), and Pakistan (PAK). We simulated several RCV introduction scenarios over 30 years using a validated mathematical model. Our findings indicate that RCV introduction could avert between 86,000 and 535,000 CRS births, preventing 2.5 to 15.8 million disability-adjusted life years. AFG and PAK could reduce about 90% of CRS births by introducing RCV with current measles routine coverage and executing supplemental immunization activities (SIAs). However, COD, NGA, and ETH must increase their current routine vaccination coverage to reduce CRS incidence significantly. This study showcases the potential benefits of RCV introduction and reinforces the need for global action to strengthen immunization programs.

Colorectal cancer (CRC) prevention is dependent on increasing screening rates, a strategy proven effective in reducing cancer cases and potential life years lost. Simulation models of CRC can be used to project expected outcomes associated with different evidence-based interventions. However, traditional simulation for each population of interest is computationally intensive and requires a model expert. To address this, we proposed a metamodeling approach, considering various techniques such as linear regression and random forest. By creating a metamodel of the simulation, decision makers can generate both individual and population-level estimates directly and instantaneously. We aimed to create a metamodel of an existing CRC simulation model that can be adapted for different interventions and populations to predict cancer cases averted and life years lost.

We document the evolution and use of the stochastic agent-based COVID-19 simulation 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.

Sexually oriented establishments across the United States often pose as massage businesses and force victim workers into a hybrid of sex and labor trafficking, simultaneously harming the legitimate massage industry. Stakeholders with varied goals and approaches to dismantling the illicit massage industry all report the need for multi-source data to clearly and transparently identify the worst offenders and highlight patterns in behaviors. We utilize findings from primary stakeholder interviews with law enforcement, regulatory bodies, legitimate massage practitioners, and subject-matter experts from nonprofit organizations to identify data sources and potential indicators of illicit massage businesses (IMBs). We focus our analysis on data from open sources in Texas and Florida including customer reviews and business data from Yelp.com, the U.S. Census, and GIS files such as truck stop, highway, and military base locations. We build two interpretable prediction models, risk scores and optimal decision trees, to determine the risk that a given massage establishment is an IMB. The proposed multi-source data-based approach and interpretable models can be used by stakeholders at all levels to save time and resources, serve victim-workers, and support well informed regulatory efforts.

Click to increase image sizeClick to decrease image size AcknowledgmentsWe appreciate the work of Cole Smith on this special issue in coordinating the review process, especially the work done well after his term as the Focus Issue Editor of Operations Engineering and Analytics came to an end. We would also like to acknowledge the contributions of the reviewers of papers submitted to this special issue.

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.

Evidence has shown that random screening tests are effective in reducing COVID-19 infections in schools. However, test administration may be hindered due to a limited budget or low participation caused by pandemic fatigue. Thus, we seek to balance the number of tests administered with end-of-semester infections. To do this we use an SEIR model to simulate SARS-CoV-2 transmissions within K-12 schools, design a multi-objective simulation optimization problem, and tune an NSGA-II algorithm to find the best testing schedules. We find the Pareto front of optimal schedules of screening tests, which can be used by stakeholders to inform test administration strategies. We discuss insights about the characteristics of optimal strategies, for example, when there are limited number of tests available or a desire to use few tests, the optimal plan is to perform the tests earlier in the semester and at higher intensity.

Diabetic Retinopathy (DR) is the main contributor to adult blindness in America. When detected on time, treatment can avoid severe sight loss 95% of the time (Fong et al., 2004). However, only 50% of people with diabetes get screened yearly, making early intervention difficult (Lee, et al., 2003). There is a need to understand how the systems for DR screenings can be designed to comply with the patient's needs, for which it is necessary to understand the user and the factors that affect their behavior. We created a questionnaire from barriers and motivators found in interviews with persons with diabetes regarding their yearly screenings (Salas, et.al., 2022) based on Ajzen’s (2006a) Constructing a Theory of Planned Behavior Questionnaire. The questionnaire measured the influence of attitudes, social norms, and perceived control on screening for DR. This study will add to the current body of literature by helping to identify where to focus efforts when creating systems for DR screening.

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.

This 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.

Problem definition: Approximately 11,000 alleged illicit massage businesses (IMBs) exist across the United States hidden in plain sight among legitimate businesses. These illicit businesses frequently exploit workers, many of whom are victims of human trafficking, forced or coerced to provide commercial sex. Academic/practical relevance: Although IMB review boards like Rubmaps.ch can provide first-hand information to identify IMBs, these sites are likely to be closed by law enforcement. Open websites like Yelp.com provide more accessible and detailed information about a larger set of massage businesses. Reviews from these sites can be screened for risk factors of trafficking. Methodology: We develop a natural language processing approach to detect online customer reviews that indicate a massage business is likely engaged in human trafficking. We label data sets of Yelp reviews using knowledge of known IMBs. We develop a lexicon of key words/phrases related to human trafficking and commercial sex acts. We then build two classification models based on this lexicon. We also train two classification models using embeddings from the bidirectional encoder representations from transformers (BERT) model and the Doc2Vec model. Results: We evaluate the performance of these classification models and various ensemble models. The lexicon-based models achieve high precision, whereas the embedding-based models have relatively high recall. The ensemble models provide a compromise and achieve the best performance on the out-of-sample test. Our results verify the usefulness of ensemble methods for building robust models to detect risk factors of human trafficking in reviews on open websites like Yelp. Managerial implications: The proposed models can save countless hours in IMB investigations by automatically sorting through large quantities of data to flag potential illicit activity, eliminating the need for manual screening of these reviews by law enforcement and other stakeholders. Funding: This work was supported by the National Science Foundation [Grant 1936331]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1196 .