@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{runge_shea_howerton_yan_hochheiser_rosenstrom_probert_borchering_marathe_lewis_et al._2024, title={Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design}, volume={47}, ISSN={["1878-0067"]}, DOI={10.1016/j.epidem.2024.100775}, abstractNote={Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.}, journal={EPIDEMICS}, author={Runge, Michael C. and Shea, Katriona and Howerton, Emily and Yan, Katie and Hochheiser, Harry and Rosenstrom, Erik and Probert, William J. M. and Borchering, Rebecca and Marathe, Madhav V. and Lewis, Bryan and et al.}, year={2024}, month={Jun} }
 @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{runge_shea_howerton_yan_hochheiser_rosenstrom_probert_borchering_marathe_lewis_et al._2023, title={Scenario Design for Infectious Disease Projections: Integrating Concepts from Decision Analysis and Experimental Design}, url={https://doi.org/10.1101/2023.10.11.23296887}, DOI={10.1101/2023.10.11.23296887}, abstractNote={AbstractAcross many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, value of information, situational awareness, horizon scanning, and forecasting) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.}, author={Runge, Michael C. and Shea, Katriona and Howerton, Emily and Yan, Katie and Hochheiser, Harry and Rosenstrom, Erik and Probert, William J.M. and Borchering, Rebecca and Marathe, Madhav V. and Lewis, Bryan and et al.}, year={2023}, month={Oct} }
 @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{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{rosenstrom_mele_ivy_mayorga_patel_lich_delamater_smith_swann_2022, title={Vaccinating children against COVID-19 is crucial to protect schools and communities}, url={https://doi.org/10.1093/pnasnexus/pgac081}, DOI={10.1093/pnasnexus/pgac081}, abstractNote={Abstract
               To evaluate the joint impact of childhood vaccination rates and school masking policies on community transmission and severe outcomes due to COVID-19, we utilized a stochastic, agent-based simulation of North Carolina to test 24 health policy scenarios. In these scenarios, we varied the childhood (ages 5 to 19) vaccination rate relative to the adult's (ages 20 to 64) vaccination rate and the masking relaxation policies in schools. We measured the overall incidence of disease, COVID-19-related hospitalization, and mortality from 2021 July 1 to 2023 July 1. Our simulation estimates that removing all masks in schools in January 2022 could lead to a 31% to 45%, 23% to 35%, and 13% to 19% increase in cumulative infections for ages 5 to 9, 10 to 19, and the total population, respectively, depending on the childhood vaccination rate. Additionally, achieving a childhood vaccine uptake rate of 50% of adults could lead to a 31% to 39% reduction in peak hospitalizations overall masking scenarios compared with not vaccinating this group. Finally, our simulation estimates that increasing vaccination uptake for the entire eligible population can reduce peak hospitalizations in 2022 by an average of 83% and 87% across all masking scenarios compared to the scenarios where no children are vaccinated. Our simulation suggests that high vaccination uptake among both children and adults is necessary to mitigate the increase in infections from mask removal in schools and workplaces.}, journal={PNAS Nexus}, author={Rosenstrom, Erik T and Mele, Jessica and Ivy, Julie S and Mayorga, Maria E and Patel, Mehul D and Lich, Kristen Hassmiller and Delamater, Paul L and Smith, Raymond L, III and Swann, Julie L}, editor={Galea, SandroEditor}, year={2022}, month={Jul} }
 @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{mele_rosenstrom_ivy_mayorga_patel_swann_2021, title={Mask Interventions in K12 Schools Can Also Reduce Community Transmission in Fall 2021}, volume={9}, url={https://doi.org/10.1101/2021.09.11.21263433}, DOI={10.1101/2021.09.11.21263433}, abstractNote={ABSTRACTThe dominance of the COVID-19 Delta variant has renewed questions about the impact of K12 school policies, including the role of masks, on disease burden.1 A recent study showed masks and testing could reduce infections in students, but failed to address the impact on the community,2 while another showed masking is critical to slow disease spread in communities, but did not consider school openings under Delta.3 We project the impact of school-masking on the community, which can inform policy decisions, and support healthcare system planning. Our findings indicate that the implementation of masking policies in school settings can reduce additional infections post-school opening by 23-36% for fully-open schools, with an additional 11-13% reduction for hybrid schooling, depending on mask quality and fit. Masking policies and hybrid schooling can also reduce peak hospitalization need by 71% and result in the fewest additional deaths post-school opening. We show that given the current vaccination rates within the community, the best option for children and the general population is to employ consistent high-quality masking, and use social distancing where possible.}, publisher={Cold Spring Harbor Laboratory}, author={Mele, Jessica and Rosenstrom, Erik and Ivy, Julie and Mayorga, Maria and Patel, Mehul D. and Swann, Julie}, year={2021}, month={Sep} }