@article{hadley_rhea_jones_li_stoner_bobashev_2022, title={Enhancing the prediction of hospitalization from a COVID-19 agent-based model: A Bayesian method for model parameter estimation}, volume={17}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0264704}, DOI={10.1371/journal.pone.0264704}, abstractNote={Agent-based models (ABMs) have become a common tool for estimating demand for hospital beds during the COVID-19 pandemic. A key parameter in these ABMs is the probability of hospitalization for agents with COVID-19. Many published COVID-19 ABMs use either single point or age-specific estimates of the probability of hospitalization for agents with COVID-19, omitting key factors: comorbidities and testing status (i.e., received vs. did not receive COVID-19 test). These omissions can inhibit interpretability, particularly by stakeholders seeking to use an ABM for transparent decision-making. We introduce a straightforward yet novel application of Bayes’ theorem with inputs from aggregated hospital data to better incorporate these factors in an ABM. We update input parameters for a North Carolina COVID-19 ABM using this approach, demonstrate sensitivity to input data selections, and highlight the enhanced interpretability and accuracy of the method and the predictions. We propose that even in tumultuous scenarios with limited information like the early months of the COVID-19 pandemic, straightforward approaches like this one with discrete, attainable inputs can improve ABMs to better support stakeholders.}, number={3}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Hadley, Emily and Rhea, Sarah and Jones, Kasey and Li, Lei and Stoner, Marie and Bobashev, Georgiy}, editor={de Sire, AlessandroEditor}, year={2022}, month={Mar}, pages={e0264704} }
 @article{preiss_hadley_jones_stoner_kery_baumgartner_bobashev_tenenbaum_carter_clement_et al._2022, title={Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID-19 pandemic}, volume={7}, ISSN={2468-0427}, url={http://dx.doi.org/10.1016/j.idm.2022.01.003}, DOI={10.1016/j.idm.2022.01.003}, abstractNote={Public health decision makers rely on hospitalization forecasts to inform COVID-19 pandemic planning and resource allocation. Hospitalization forecasts are most relevant when they are accurate, made available quickly, and updated frequently. We rapidly adapted an agent-based model (ABM) to provide weekly 30-day hospitalization forecasts (i.e., demand for intensive care unit [ICU] beds and non-ICU beds) by state and region in North Carolina for public health decision makers. The ABM was based on a synthetic population of North Carolina residents and included movement of agents (i.e., patients) among North Carolina hospitals, nursing homes, and the community. We assigned SARS-CoV-2 infection to agents using county-level compartmental models and determined agents' COVID-19 severity and probability of hospitalization using synthetic population characteristics (e.g., age, comorbidities). We generated weekly 30-day hospitalization forecasts during May-December 2020 and evaluated the impact of major model updates on statewide forecast accuracy under a SARS-CoV-2 effective reproduction number range of 1.0-1.2. Of the 21 forecasts included in the assessment, the average mean absolute percentage error (MAPE) was 7.8% for non-ICU beds and 23.6% for ICU beds. Among the major model updates, integration of near-real-time hospital occupancy data into the model had the largest impact on improving forecast accuracy, reducing the average MAPE for non-ICU beds from 6.6% to 3.9% and for ICU beds from 33.4% to 6.5%. Our results suggest that future pandemic hospitalization forecasting efforts should prioritize early inclusion of hospital occupancy data to maximize accuracy.}, number={1}, journal={Infectious Disease Modelling}, publisher={Elsevier BV}, author={Preiss, Alexander and Hadley, Emily and Jones, Kasey and Stoner, Marie C.D. and Kery, Caroline and Baumgartner, Peter and Bobashev, Georgiy and Tenenbaum, Jessica and Carter, Charles and Clement, Kimberly and et al.}, year={2022}, month={Mar}, pages={277–285} }
 @article{rhea_hilscher_rineer_munoz_jones_endres-dighe_dibiase_sickbert-bennett_weber_macfarquhar_et al._2019, title={Creation of a Geospatially Explicit, Agent-based Model of a Regional Healthcare Network with Application to Clostridioides difficile Infection}, volume={17}, ISSN={2326-5094 2326-5108}, url={http://dx.doi.org/10.1089/hs.2019.0021}, DOI={10.1089/hs.2019.0021}, abstractNote={Agent-based models (ABMs) describe and simulate complex systems comprising unique agents, or individuals, while accounting for geospatial and temporal variability among dynamic processes. ABMs are increasingly used to study healthcare-associated infections (ie, infections acquired during admission to a healthcare facility), including Clostridioides difficile infection, currently the most common healthcare-associated infection in the United States. The overall burden and transmission dynamics of healthcare-associated infections, including C difficile infection, may be influenced by community sources and movement of people among healthcare facilities and communities. These complex dynamics warrant geospatially explicit ABMs that extend beyond single healthcare facilities to include entire systems (eg, hospitals, nursing homes and extended care facilities, the community). The agents in ABMs can be built on a synthetic population, a model-generated representation of the actual population with associated spatial (eg, home residence), temporal (eg, change in location over time), and nonspatial (eg, sociodemographic features) attributes. We describe our methods to create a geospatially explicit ABM of a major regional healthcare network using a synthetic population as microdata input. We illustrate agent movement in the healthcare network and the community, informed by patient-level medical records, aggregate hospital discharge data, healthcare facility licensing data, and published literature. We apply the ABM output to visualize agent movement in the healthcare network and the community served by the network. We provide an application example of the ABM to C difficile infection using a natural history submodel. We discuss the ABM's potential to detect network areas where disease risk is high; simulate and evaluate interventions to protect public health; adapt to other geographic locations and healthcare-associated infections, including emerging pathogens; and meaningfully translate results to public health practitioners, healthcare providers, and policymakers.}, number={4}, journal={Health Security}, publisher={Mary Ann Liebert Inc}, author={Rhea, Sarah and Hilscher, Rainer and Rineer, James I. and Munoz, Breda and Jones, Kasey and Endres-Dighe, Stacy M. and DiBiase, Lauren M. and Sickbert-Bennett, Emily E. and Weber, David J. and MacFarquhar, Jennifer K. and et al.}, year={2019}, month={Aug}, pages={276–290} }
 @article{bates_leonenko_rineer_bobashev_2019, title={Using synthetic populations to understand geospatial patterns in opioid related overdose and predicted opioid misuse}, volume={25}, ISSN={["1572-9346"]}, DOI={10.1007/s10588-018-09281-2}, number={1}, journal={COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY}, author={Bates, Savannah and Leonenko, Vasiliy and Rineer, James and Bobashev, Georgiy}, year={2019}, month={Mar}, pages={36–47} }