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H. Kravitz et al. / IJOCTA, Vol.15, No.4, pp.750-778 (2025)

            123. Kuhl E. The classical SIR model. In: Compu-  analysis, where she focuses on developing compu-
                tational Epidemiology: Data-Driven Modeling of  tational and statistical techniques to model, ana-
                COVID-19. Cham: Springer International Pub-   lyze and explore complex relational data.
                lishing; 2021:41-59.                           https://orcid.org/0000-0003-2927-3259
                http://dx.doi.org/10.1007/978-3-030-82890-5 3
            124. Lunelli A, Pugliese A. Final attack ratio in SIR
                epidemic models for multigroup populations. Ric
                Mat. 2018;67(1):49-68.
                http://dx.doi.org/10.1007/s11587-017-0349-5
            125. Ridenhour B, Kowalik JM, Shay DK. Unraveling
                R0: Considerations for public health applications.  Bryttani Nieves received her B.S. in Data Sci-
                Am J Public Health. 2014;104(2):e32-e41.      ence from Portland State University in 2025. She
                http://dx.doi.org/10.2105/AJPH.2013.301704
                                                              contributed to the development and implementa-
                                                              tion of computational analyses in this study.
            Hannah Kravitz is an Assistant Professor of
                                                               https://orcid.org/0009-0001-9983-6953
            Mathematics at Portland State University in
            Portland, OR, USA. She received her B.S. from
            The Ohio State University and her Ph.D. from
            The University of Arizona. Her research focuses
            on numerical methods for partial differential equa-
            tions and on the theory and applications of metric
            graphs.                                           Moysey Brio is a Professor of Mathematics at
             https://orcid.org/0000-0003-2998-8043            the University of Arizona. He received his Ph.D.
                                                              in 1984 from UCLA, and held research fellowships
                                                              at UCLA, Rice University, NYU, IMPA (Brazil),
                                                              DTU (Denmark), and INSA de Rouen (France).
                                                              His research focuses on numerical algorithms for
            Christina Dur´on is an Assistant Professor of     partial differential equations with applications in
            Mathematics at Pepperdine University in Malibu,   aerospace, optics, and astrophysics. He is also the
            CA, USA. She received her B.S. from Swarth-       author of the graduate textbook “Numerical Time-
            more College, M.S. from the University of Wash-   Dependent Partial Differential Equations for Sci-
            ington and Ph.D. from Claremont Graduate Uni-     entists and Engineers.”
            versity. Her research lies in network theory and   https://orcid.org/0000-0003-4834-2758








































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