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El Ministerio de Sanidad ha coordinado tres estudios que han estimado el impacto de la Estrategia de Vacunación frente a COVID-19 en España. El objetivo era que los modelos ayudaran a establecer los grupos de población prioritarios para la vacunación, en un contex-to inicial de limitación de dosis. A partir de la misma información epidemiológica y de va-cunas se han elaborado tres modelos matemá-ticos distintos cuyos resultados apuntan en la misma dirección: combinada con el distancia-miento físico, la vacunación escalonada, em-pezando por los grupos de mayor riesgo de complicaciones, evitaría el 60% de las infec-ciones, el 42% de las hospitalizaciones y el 60% de la mortalidad en la población. Estos modelos, que pueden adaptarse a la nueva evi-dencia científica disponible, son herramientas dinámicas y potentes para la evaluación y el ajuste de los programas de vacunación, impul-sando el desarrollo de este campo de inves-tigación, y ayudando a lograr resultados más eficientes en salud.

The occurrence of earthquakes has been studied from many aspects. Apparently, earthquakes occur without warning and can devastate entire cities in just a few seconds, causing numerous casualties and huge economic loss. Great e ort has been directed towards being able to predict these natural disasters, and taking precautionary measures. However, simultaneously predicting when, where and the magnitude of the next earthquake, within a limited region and time, seems an almost impossible task. Techniques from the eld of data mining are providing new and important information to researchers. This article reviews the use of arti cial neural networks for earthquake prediction in response to the increasing amount of recently published works and presenting claims of being e ective. Based on an analysis and discussion of recent results, data mining practitioners are encouraged to apply their own techniques in this emerging eld of research.

In this paper, we propose a new diagnostic checking tool for fuzzy rule-based modelling of time series. Through the study of the residuals in the Lagrange Multiplier testing framework we devise a hypothesis test which allows us to determine if the residual time series is homoscedastic or not, that is, if it has the same variance throughout time. This is another important step towards a statistically sound modelling strategy for fuzzy rule-based models., Spanish Ministerio de Ciencia e Innovaci´on (MICINN) under Project grants MICINN TIN2009- 14575 and CIT-460000-2009-46

International audience; In this paper, we propose a new diagnostic checking tool for fuzzy rule-based modelling of time series. Through the study of the residuals in the Lagrange Multiplier testing framework we devise a hypothesis test which allows us to determine if the residual time series is homoscedastic or not, that is, if it has the same variance throughout time. This is another important step towards a statistically sound modelling strategy for fuzzy rule-based models.

In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rule-based models. From these relations, we derive a Lagrange Multiplier linearity test and some properties of the maximum likelihood estimator needed for it. Finally, an empirical study of the goodness of the test is presented.