@article{rodriguez-cartes_zhang_mayorga_swann_allaire_2024, title={Evaluating the potential impact of rubella-containing vaccine introduction on congenital rubella syndrome in Afghanistan, Dem. Republic of Congo, Ethiopia, Nigeria, and Pakistan: A mathematical modeling study}, url={https://doi.org/10.1371/journal.pgph.0002656}, DOI={10.1371/journal.pgph.0002656}, abstractNote={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.}, journal={PLOS Global Public Health}, author={Rodriguez-Cartes, Sebastian A. and Zhang, Yiwei and Mayorga, Maria E. and Swann, Julie L. and Allaire, Benjamin T.}, editor={Coffee, MeganEditor}, year={2024}, month={Jan} }
 @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={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).}, 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{paret_rodriguez_mayorga_velotti_lodree_2023, title={Agent-Based Simulation of Spontaneous Volunteer Convergence to Improve Disaster Planning}, volume={24}, ISSN={["1527-6996"]}, url={https://doi.org/10.1061/NHREFO.NHENG-1659}, DOI={10.1061/NHREFO.NHENG-1659}, abstractNote={The involvement of spontaneous volunteers (SVs) in disaster response represents a significant resource. However, existing emergency management plans often fail to take spontaneous volunteers into account due to negative perceptions and uncertainty about SV convergence. We developed an agent-based simulation model of spontaneous volunteer convergence to aid the disaster response planning process. The model considers a heterogeneous population of agents, each with unique attributes such as motivation, opinion, and site choice behavior. Model development was informed by the literature as well as interviews with volunteers and volunteer managers, participant observations, and discussions with practitioners. To illustrate the practical value of the model, we present a case study that addressed research questions related to volunteer reception centers and volunteer assignment policies. This transdisciplinary study bridges the gap between operations research and management science and social science, and provides a new decision aid to help improve the integration of spontaneous volunteers in disaster management plans.}, number={2}, journal={NATURAL HAZARDS REVIEW}, author={Paret, Kyle and Rodriguez, Sebastian A. and Mayorga, Maria E. and Velotti, Lucia and Lodree, Emmett J.}, year={2023}, month={May} }
 @article{rodriguez_fuente_aguayo_2021, title={A simulation-optimization approach for the facility location and vehicle assignment problem for firefighters using a loosely coupled spatio-temporal arrival process}, volume={157}, ISSN={["1879-0550"]}, DOI={10.1016/j.cie.2021.107242}, abstractNote={This work proposes a framework to aid the strategic decision making regarding the proper location of fire stations as well as their assignment of vehicles to improve emergency response. We present an iterative simulation–optimization approach that based on some precomputed utilization parameters updates the optimal location of vehicles and fire stations. First, we find an optimal solution by using a robust formulation of the Facility Location and Equipment Emplacement Technique with Expected Coverage (Robust FLEET-EXC) model, which maximizes demand considering vehicles' utilization. Second, we use this solution as an input to a discrete event simulation model to compute utilization parameters. Then, if the obtained parameters deviate less than a desired error, the solution is maintained; otherwise, a new solution is computed with these new parameters. Additionally, the emergencies arrival process is modeled by a spatio-temporal sampling method that loosely couples a Kernel Density Estimator and a non-homogeneous non-renewal arrival process with a Markov-Mixture of Erlangs of Common Order model as base process. Then, the proposed robust model is compared to a deterministic FLEET model that does not account for vehicles' availability, and the FLEET-EXC model with simulated utilization parameters. The main results show that the proposed spatio-temporal sampling method achieves a better representation of the emergency arrival process than those generally used in literature, and the resulting utilization parameters are statistically different than those produced by a Hypercube Queueing Model. On the other hand, the simulation–optimization approach that uses the Robust FLEET-EXC has the best performance, achieving the highest coverage of emergencies in 13 out of 15 experiments. Finally, this model is statistically better than the deterministic FLEET in all but one experiment, resulting in up to 6.42% more coverage.}, journal={COMPUTERS & INDUSTRIAL ENGINEERING}, author={Rodriguez, Sebastian A. and Fuente, Rodrigo A. and Aguayo, Maichel M.}, year={2021}, month={Jul} }
 @article{rodriguez_fuente_aguayo_2020, title={A facility location and equipment emplacement technique model with expected coverage for the location of fire stations in the Concepción province, Chile}, volume={147}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85087394323&partnerID=MN8TOARS}, DOI={10.1016/j.cie.2020.106522}, abstractNote={In this paper, the Facility Location and Equipment Emplacement Technique model with Expected Covering (FLEET-EXC) model is introduced, an emergency facility location problem that maximizes the coverage of expected demand. This model also considers multiple regions, demand types, vehicle types, and region-dependent dispatching rules. This work presents a mixed-integer linear programming model that considers vehicles average utilization to compute expected demand coverage. Because the optimal solution depends on these parameters, we propose an iterative procedure as a solving method, where a Hypercube Queueing Model is used to compute the utilization of the vehicles. The goal of this procedure is to update parameters until the resulting vehicle utilizations from the optimal solution are the same as the ones used to compute the expected demand of the MIP mentioned above model. Finally, a case study on Concepcion province, Chile is presented. A full factorial experiment design is proposed to analyze the effect of locating and relocating fire stations, finding optimal solutions for each experiment. The synergy produced by relocation and location of new facilities notoriously improves the emergency coverage, providing insights for strategic decision making.}, journal={Computers and Industrial Engineering}, author={Rodriguez, S.A. and Fuente, R.A. and Aguayo, M.M.}, year={2020} }