Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data.

Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model. We compare the performance of the standard LDA topic model with the Gibbs Sampler Dirichlet Multinomial Model (GSDMM) and the Gamma Poisson Mixture Model (GPM), which are specifically designed for sparse data.

Mapping ex ante risks of COVID-19 in Indonesia using a Bayesian geostatistical model on airport network data.

A rapid response to global infectious disease outbreaks is crucial to protect public health. Ex ante information on the spatial probability distribution of early infections can guide governments to better target protection efforts. We propose a two-stage statistical approach to spatially map the ex ante importation risk of COVID-19 and its uncertainty across Indonesia based on a minimal set of routinely available input data related to the Indonesian flight network, traffic and population data, and geographical information.

An iterative topic model filtering framework for short and noisy user-generated data: analyzing conspiracy theories on twitter.

Conspiracy theories have seen a rise in popularity in recent years. Spreading quickly through social media, their disruptive effect can lead to a biased public view on policy decisions and events. We present a novel approach for LDA-pre-processing called Iterative Filtering to study such phenomena based on Twitter data.

The Rhinobiome of Exacerbated Wheezers and Asthmatics: Insights From a German Pediatric Exacerbation Network.

Although the nose, as a gateway for organism-environment interactions, may have a key role in asthmatic exacerbation, the rhinobiome of exacerbated children with asthma was widely neglected to date. The aim of this study is to understand the microbiome, the microbial immunology, and the proteome of exacerbated children and adolescents with wheeze and asthma. Considering that a certain proportion of wheezers may show a progression to asthma, the comparison of both groups provides important information regarding clinical and phenotype stratification.

Unsupervised document classification integrating web scraping, one-class SVM and LDA topic modelling.

Unsupervised document classification for imbalanced data sets poses a major challenge. To obtain accurate classification results, training data sets are often created manually by humans which requires expert knowledge, time and money. Depending on the imbalance of the data set, this approach also either requires human labelling of all of the data or it fails to adequately recognize underrepresented categories.

Prevalence dependent calibration of a predictive model for nasal carriage of methicillin-resistant Staphylococcus aureus.

Background: Published models predicting nasal colonization with Methicillin-resistant Staphylococcus aureus among hospital admissions predominantly focus on separation of carriers from non-carriers and are frequently evaluated using measures of discrimination. In contrast, accurate estimation of carriage probability, which may inform decisions regarding treatment and infection control, is rarely assessed. Furthermore, no published models adjust for MRSA prevalence.

On the 100th anniversary of sterile catgut kuhn: Franz Kuhn (1866-1929) and the epistemology of catgut sterilization.

The long road to effective catgut sterilization began with the work of Lord Joseph Lister (1867) and did not end until 40 years later. At the end of the nineteenth century dozens of different techniques were used to "sterilize" catgut, by immersing the cord in a cold chemical solution, by exposing it to steam, or by a combination of the two techniques, yet none of these approaches offered the ultimate solution. One of the many physicians working on the catgut problem at that time was the German surgeon Franz Kuhn (1866-1929), best known as a pioneer of intubation anesthesia.