This paper analyses the capabilities of different techniques to build a semantic representation of educational digital resources. Educational digital resources are modeled using the Learning Object Metadata (LOM) standard, and these semantic representations can be obtained from different LOM fields, like the title, description, among others, in order to extract the features/characteristics from the digital resources. The feature extraction methods used in this paper are the Best Matching 25 (BM25), the Latent Semantic Analysis (LSA), Doc2Vec, and the Latent Dirichlet allocation (LDA). The utilization of the features/descriptors generated by them are tested in three types of educational digital resources (scientific publications, learning objects, patents), a paraphrase corpus and two use cases: in an information retrieval context and in an educational recommendation system. For this analysis are used unsupervised metrics to determine the feature quality proposed by each one, which are two similarity functions and the entropy. In addition, the paper presents tests of the techniques for the classification of paraphrases. The experiments show that according to the type of content and metric, the performance of the feature extraction methods is very different; in some cases are better than the others, and in other cases is the inverse.

Both cell-wise and case-wise outliers may appear in a real data set at the same time. Few methods have been developed in order to deal with both types of outliers when formulating a regression model. In this work, a robust estimator is proposed based on a three-step method named 3S-regression, which uses the comedian as a highly robust scatter estimate. An intensive simulation study is conducted in order to evaluate the performance of the proposed comedian 3S-regression estimator in the presence of cell-wise and case-wise outliers. In addition, a comparison of this estimator with recently developed robust methods is carried out. The proposed method is also extended to the model with continuous and dummy covariates. Finally, a real data set is analyzed for illustration in order to show potential applications.

Dengue is a disease of high interest for public health in the affected localities. Dengue virus is transmitted by Aedes species and presents hyperendemic behaviors in tropical and subtropical regions. Colombia is one of the countries most affected by the dengue virus in the Americas. Its central-west region is a hot spot in dengue transmission, especially the Department of Antioquia, which has suffered from multiple dengue outbreaks in recent years (2015–2016 and 2019–2020). In this article, we perform a retrospective analysis of the confirmed dengue cases in Antioquia, discriminating by both subregions and dengue severity from 2015 to 2020. First, we conduct an exploratory analysis of the epidemic data, and then a statistical survival analysis is carried out using a Cox regression model. Our findings allow the identification of the hazard and socio-demographic patterns of dengue infections in the Colombian subtropical region of Antioquia from 2015 to 2020.

Many infectious diseases are deadly to humans. The Aedes aegypi mosquito is the principal vector of infectious diseases that include chikungunya, dengue, yellow fever, and zika. Some factors such as survival time and aging are vital in its development and capacity to transmit the pathogens, which in turn are affected by environmental factors such as temperature. In this paper, we consider aging as the biological wear and tear presented in some mosquito populations over time, whereas survival is considered as the maximum time that a mosquito lives. We propose statistical methods that are commonly used in engineering for reliability analysis to compare transmission riskiness among different mosquitoes. We conducted a case study in three Colombian cities: Bello, Riohacha, and Villavicencio. In this study, we detected that the Aedes aegypi female mosquitoes in Bello live longer than in Riohacha and Villavicencio, and the females in Riohacha live longer than those in Villavicencio. Regarding aging, the females from Riohacha age slower than in Villavicencio and the latter age slower than in Bello. Mosquito populations that age slower are considered young and the other ones are old. In addition, we detected that the females from Bello in the temperature range of 27 ${}^{\circ }$C–28 ${}^{\circ }$C age slower than those in Bello at higher temperatures. In general, a young female has a higher risk of transmitting a disease to humans than an old female, regardless of its survival time. These findings have not been previously reported in studies of this type of infectious diseases and contributed to new knowledge in biomedicine.

Arboviral diseases such as dengue pose major public health challenges in endemic regions, notably in Norte de Santander (Colombia), where they place substantial pressure on healthcare services. We analyzed 8,814 confirmed dengue cases reported to the Colombian National Public Health Surveillance System (SIVIGILA) from January 2015 to June 2019 to investigate temporal dynamics and determinants of hospitalization. We applied a dual methodology based on: (i) machine-learning classifiers—logistic regression, random forest, and support vector machines—to predict hospitalization risk from symptom profiles and (ii) Cox models with time-varying coefficients to assess the timing of hospitalization as a function of socio-environmental and clinical predictors, accommodating non-proportional hazards. Our main findings are as follows. On average, patients sought medical attention about four days after symptom onset. Severe and non-severe cases had similar onset-to-hospitalization times, but severe cases were often admitted shortly after the appearance of key warning signs. Abdominal pain and low platelet count markedly increased the risk of hospitalization in classification models and were associated with higher hazards of earlier hospitalization in the time-to-event analysis, with vomiting likewise linked to earlier hospitalization. Among classifiers, random forest achieved the highest predictive accuracy, whereas logistic regression and Cox models yielded interpretable estimates of risk (odds ratios) and timing (time-varying hazard ratios). These findings highlight the value of early recognition of specific symptoms and the integration of machine learning with survival analysis to support proactive, resource-aware dengue management. All analyses were conducted in the R software.

In this work, we introduce a novel methodology for modeling discrete count variables within the framework of stochastic processes. Our approach integrates two statistical areas: Non-Homogeneous Poisson Processes for the estimation and prediction of intensity functions based on explanatory variables and functional data estimation techniques. Through a comprehensive case study focusing on an infectious disease with viral characteristics, we demonstrate the potential of our methodology. We provide empirical evidence that our methodology offers a robust alternative for modeling count variables. Our findings support the utility of our approach in capturing the complex dynamics inherent in count data in infectious disease epidemiological phenomena.