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*THIS BOOK WILL SOON BECOME AVAILABLE AS OPEN ACCESS BOOK* This book is an excellent synthesis of the initial and continuing preparation for Mathematics Teaching in Colombia, Costa Rica, Dominican Republic and Venezuela, from which comparative analyses can be made that show similarities and differences, and highlight various perspectives. In August 2012, a workshop of the Capacity and Networking Project (CANP) of the International Commission on Mathematical Instruction (ICMI) was held in Costa Rica. This CANP brought together for two weeks a group of 66 Mathematics educators, mathematicians, university administrators, and elementary and secondary teachers from Colombia, Venezuela, the Dominican Republic, Panamá and Costa Rica. The goal was to promote progress in Mathematics Education in the region; as such it was a unique experience in the region. One of the most important results of this event was the creation of the Mathematics Education Network of Central America and the Caribbean (REDUMATE). It was organized by persons associated with the Mathematics Education Reform Project in Costa Rica (responsible for the most outstanding and innovative curriculum reform in Latin America) and the Inter-American Committee on Mathematics Education (IACME), which is an official regional multinational organization affiliate of ICMI. This book brings to the international Educational Community an important collection of experiences and ideas in the Mathematics Education of four countries of a region within the heart of the American continent, a region that has been many times forgotten. The dissemination of these results can promote the search for international collaborative actions in a wider scale.

Part I Pre-Disaster -- 1. Impact of Mexican Public Policies in the development of COVID-19 Pandemic -- 2. Clustering of Highly Vulnerable Mexican Municipalities to Develop Humanitarian Public Policies -- 3. Strategies that improve the performance of the humanitarian supply chain -- 4. Water resources in Mexico and their implications in the phenomenon of drought -- 5. A Proposal to the Reduction of Carbon Dioxide Emission in Inventory Replenishment: Mitigating the Climate Change -- 6. Theoretical approach to risk reduction since urban form -- 7. Allocation Model Applied to Preventive Evacuation for Volcanic Risk in Localities Near the Popocatepetl Volcano in Puebla, Mexico -- 8. Identification of homogeneous hydrological administrative regions in Mexico using analysis of variance -- Part II Post-Disaster -- 9. Optimising distribution of limited COVID-19 vaccines: Analysing im-pact in Argentine -- 10. Location of Regional Humanitarian Response Depot (RHRD) in the Seven Regions in the State of Puebla -- 11. Location of Humanitarian Response Distribution Centers for the State of Chiapas -- 12. Distribution of Personal Protective Equipment, derived from the Pres-ence of the COVID-19 Virus in Mexico -- 13. A prediction model to determine a COVID-19 patient's outcome based on its risk factors -- 14. Application of a Markov Decision Process in Collection Center Opera-tions -- 15. Decision-Support Tool for Coordination of Volunteers during Lock-downs -- 16. Facilities Location under Risk Mitigation Concerns -- Part III Multi-criteria approaches -- 17. An Integrated FAHP-based Methodology to Compute a Risk Vulnera-bility Index -- 18. A multi-criteria decision-making framework for the design of the re-lief distribution routes.

This paper presents the generation of a plausible data set related to the needs of COVID-19 patients with severe or critical symptoms. Possible illness' stages were proposed within the context of medical knowledge as of January 2021. The parameters chosen in this data set were customized to fit the population data of the Valencia region (Spain) with approximately 2.5 million inhabitants. They were based on the evolution of the pandemic between September 2020 and March 2021, a period that included two complete waves of the pandemic.Contrary to expectation and despite the European and national transparency laws (BOE-A2013-12887, 2013; European Parliament and Council of the European Union, 2019), the actual COVID-19 pandemic-related data, at least in Spain, took considerable time to be updated and made available (usually a week or more). Moreover, some relevant data necessary to develop and validate hospital bed management models were not publicly accessible. This was either because these data were not collected, because public agencies failed to make them public (despite having them indexed in their databases), the data were processed within indicators and not shown as raw data, or they simply published the data in a format that was difficult to process (e.g., PDF image documents versus CSV tables). Despite the potential of hospital information systems, there were still data that were not adequately captured within these systems.Moreover, the data collected in a hospital depends on the strategies and practices specific to that hospital or health system. This limits the generalization of "real" data, and it encourages working with "realistic" or plausible data that are clean of interactions with local variables or decisions (Gunal, 2012; Marin-Garcia et al., 2020). Besides, one can parameterize the model and define the data structure that would be necessary to run the model without delaying till the real data become available. Conversely, plausible data sets can be generated from publicly available information and, later, when real data become available, the accuracy of the model can be evaluated (Garcia-Sabater and Maheut, 2021).This work opens lines of future research, both theoretical and practical. From a theoretical point of view, it would be interesting to develop machine learning tools that, by analyzing specific data samples in real hospitals, can identify the parameters necessary for the automatic prototyping of generators adapted to each hospital. Regarding the lines of research applied, it is evident that the formalism proposed for the generation of sound patients is not limited to patients affected by SARS-CoV-2 infection. The generation of heterogeneous patients can represent the needs of a specific population and serve as a basis for studying complex health service delivery systems. ; En este trabajo se presenta cómo se ha generado un conjunto de datos verosímiles relacionados con las necesidades de pacientes covid-19 con síntomas severe or critical. Se considerarán las etapas posibles con los conocimientos médicos a fecha de enero de 2021. Los parámetros elegidos en este data set están personalizados para adecuarse a los valores poblacionales de la región de Valencia (Spain), unos 2.5 Millones de habitantes y la evolución de la pandemia entre los meses de septiembre 2020 y marzo 2021, un periodo de tiempo que contemple dos olas completas de pandemia.En contra de lo que cabría esperar, a pesar de la ley de transparencia europea y nacional (BOE-A-2013-12887, 2013; Parlamento Europeo y del Consejo de la Unión Europea, 2019), los datos reales relacionados con la pandemia covid-19, al menos en España, tardan mucho en actualizarse y estar disponibles (normalmente una semana o más días). Además, algunos datos relevantes para trabajar los modelos de gestión de camas de hospital no están accesibles públicamente. Bien porque no se hayan recogido esos datos, o porque los organismos públicos no los ofrecen (a pesar de tenerlos indexados en sus bases de datos), o los ofrecen camuflados en indicadores procesados y no muestran los datos en bruto, o simplemente los publican en un formato de difícil reutilización (por ejemplo, en documentos PDF en lugar de en tablas CSV). A pesar de que los sistemas de información de los hospitales son bastante potentes, siguen existiendo datos que ni siquiera están recogidos adecuadamente en el sistema de información de salud.Por otra parte, los datos recogidos en un hospital dependen de las estrategias y practicas propias de ese hospital o sistema de salud. Este efecto limita la generalización de los datos "reales" y es necesario trabajar con datos "realistas" o verosímiles que están limpios de interacciones con variables o decisiones locales (Gunal, 2012; Marin-Garcia et al., 2020). Por un lado, se puede parametrizar el modelo y definir la estructura de datos que sería necesaria para ejecutar el modelo con datos reales. Por otro lado, se pueden generar conjuntos de datos verosímiles a partir de la información pública disponible y, posteriormente, cuando se disponga de los datos reales evaluar la bondad del modelo (Garcia-Sabater Maheut, 2021).

[EN] A Spanish version of the article is provided (see section before references). This paper presents the generation of a plausible data set related to the needs of COVID-19 patients with severe or critical symptoms. Possible illness' stages were proposed within the context of medical knowledge as of January 2021. The parameters chosen in this data set were customized to fit the population data of the Valencia region (Spain) with approximately 2.5 million inhabitants. They were based on the evolution of the pandemic between September 2020 and March 2021, a period that included two complete waves of the pandemic. Contrary to expectation and despite the European and national transparency laws (BOE-A2013-12887, 2013; European Parliament and Council of the European Union, 2019), the actual COVID-19 pandemic-related data, at least in Spain, took considerable time to be updated and made available (usually a week or more). Moreover, some relevant data necessary to develop and validate hospital bed management models were not publicly accessible. This was either because these data were not collected, because public agencies failed to make them public (despite having them indexed in their databases), the data were processed within indicators and not shown as raw data, or they simply published the data in a format that was difficult to process (e.g., PDF image documents versus CSV tables). Despite the potential of hospital information systems, there were still data that were not adequately captured within these systems. Moreover, the data collected in a hospital depends on the strategies and practices specific to that hospital or health system. This limits the generalization of "real" data, and it encourages working with "realistic" or plausible data that are clean of interactions with local variables or decisions (Gunal, 2012; Marin-Garcia et al., 2020). Besides, one can parameterize the model and define the data structure that would be necessary to run the model without delaying till the real data become available. ...

76 115 12 1 ; OJS Wong, G. N., Weiner, Z. J., Tkachenko, A. V., Elbanna, A., Maslov, S., & Goldenfeld, N. (2020). Modeling COVID-19 dynamics in Illinois under non-pharmaceutical interventions. In medRxiv. https://doi.org/10.1101/2020.06.03.20120691 ; Garcia-Sabater, J. P., & Maheut, J. (2021). Introducción al Modelado Matematico, Nota Técnica. RiuNet. Repositorio Institucional UPV. https://doi.org/http://hdl.handle.net/10251/158555 ; [EN] A Spanish version of the article is provided (see section before references). This paper presents the generation of a plausible data set related to the needs of COVID-19 patients with severe or critical symptoms. Possible illness' stages were proposed within the context of medical knowledge as of January 2021. The parameters chosen in this data set were customized to fit the population data of the Valencia region (Spain) with approximately 2.5 million inhabitants. They were based on the evolution of the pandemic between September 2020 and March 2021, a period that included two complete waves of the pandemic. Contrary to expectation and despite the European and national transparency laws (BOE-A2013-12887, 2013; European Parliament and Council of the European Union, 2019), the actual COVID-19 pandemic-related data, at least in Spain, took considerable time to be updated and made available (usually a week or more). Moreover, some relevant data necessary to develop and validate hospital bed management models were not publicly accessible. This was either because these data were not collected, because public agencies failed to make them public (despite having them indexed in their databases), the data were processed within indicators and not shown as raw data, or they simply published the data in a format that was difficult to process (e.g., PDF image documents versus CSV tables). Despite the potential of hospital information systems, there were still data that were not adequately captured within these systems. Moreover, the data collected in a hospital depends on the ...

[EN] This systematic literature review focuses on planning models jointly addressing location and allocation decisions related to the design of intermodal freight transportation networks. Since this body of literature is evolving quickly, a methodology based on a linked two-stage analysis is proposed. The first stage analyses recent surveys to establish the guidelines and criteria that enable the subsequent systematic review. Then, the review concentrates on analysing contributions to the current state of the art on intermodal freight transportation from two close, yet different research streams: transportation networks and supply chain networks. Key features identified in the first stage such as: 1) the research problem's characteristics; 2) the intermodal networks design's particularities; 3) proposed solution techniques, among others, are used to classify and analyse the different contributions. The review identifies current trends, emerging topics and some issues that merit being researched. ; This work was supported by the Bolivar Government and CEIBA Foundation (Colombia); and Spanish Ministry of Economy and Competitiveness under research project ECO2015-65874-P. ; Agamez-Arias, ADM.; García Sabater, JP.; Ruiz, A.; Moyano-Fuentes, J. (2021). A systematic literature review of the design of intermodal freight transportation networks addressing location-allocation decisions. European J of Industrial Engineering. 15(1):1-34. https://doi.org/10.1504/EJIE.2021.113506 ; S ; 1 ; 34 ; 15 ; 1