En este trabajo analizamos escenarios hipotéticos para contagios de COVID-19 durante la elección 2021 en México. Del 2 de abril al 2 de junio 2021 se llevarán a cabo elecciones de diputados federales, diputados locales, gubernaturas y presidencias municipales en lo que es considerada como la elección más grande en la historia de México; se estima que las actividades de las campañas electorales y el día de la votación se incrementará la movilidad de las personas y con ello su riesgo de contagio por COVID-19. Usando datos históricos de razones de contagios se define la media de estos datos, su desviación estándar y mediante una distribución t-Student se obtiene un intervalo de 90% de confianza para la media. Se utilizan el centro y ambos extremos de este intervalo como tasas de incremento para simular el crecimiento de casos en dos periodos (primer mes: elección diputados federales; segundo mes: elección gubernaturas, diputados locales y ayuntamientos); se reportan simulaciones usando algoritmos de aprendizaje de máquina a 2 meses pasadas las elecciones.Palabras clave: aprendizaje máquina, proyecciones COVID-19, elección 2021 México.SUMMARYIn this work we analyze hypothetical scenarios for COVID-19 infections during the 2021 election in Mexico; from april 2 to june 2, 2021, elections for federal deputies, local deputies, governorships and municipal presidencies will be held in what is considered the largest election in Mexico´s history; it is estimated that the activities of the electoral campaigns and the election day will increase the mobility of people and with it their risk of contagion by COVID-19. Using historical data on infection rates, the mean of these data is defined, its standard deviation and a t-Student distribution is used to obtain a 90% confidence interval for the mean. The center and both ends of this interval are used as rates of increase to simulate the growth of cases in two periods (first month; election of federal deputies; second month; election of governorships, local deputies and municipalities), simulations are reported using machine learning algorithms 2 monts after the elections.Keywords: machine learning, COVID-19 projections, Mexico 2021 electionINTRODUCCIÓNAl momento de escribir este trabajo, se han confirmado alrededor de 110 millones de casos de
Reverse Cuthill McKee (RCM) reordering can be applied to either edges or elements of unstructured meshes (triangular/tetrahedral) , in accordance to the respective finite element formulation, to reduce the bandwidth of stiffness matrices . Grid generators are mainly designed for nodal based finite elements. Their output is a list of nodes (2d or 3d) and an array describing element connectivity, be it triangles or tetrahedra. However, for edge-defined finite element formulations a numbering of the edges is required. Observations are reported for Triangle/Tetgen Delaunay grid generators and for the sparse structure of the assembled matrices in both edge- and element-defined formulations. The RCM is a renumbering algorithm traditionally applied to the nodal graph of the mesh. Thus, in order to apply this renumbering to either the edges or the elements of the respective finite element formulation, graphs of the mesh were generated. Significant bandwidth reduction was obtained. This translates to reduction in the execution effort of the sparse-matrix-times-vector product. Compressed Sparse Row format was adopted and the matrix-times-vector product was implemented in an OpenMp parallel routine.
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