On the Analysis of a Real Dataset of COVID-19 Patients in Alava

COVID-19 has been spread to many countries all over the world in a relatively short period, largely overwhelmed hospitals have been a direct consequence of the explosive increase of coronavirus cases. In this dire situation, the demand for the development of clinical decision support systems based on predictive algorithms has increased, since these predictive technologies may help to alleviate unmanageable stress on healthcare systems. We contribute to this effort by a comprehensive study over a real dataset of covid-19 patients from a local hospital. The collected dataset is representative of the local policies on data gathering implemented during the pandemic, showing high imabalance and large number of missing values. In this paper, we report a descriptive analysis of the data that points out the large disparity of data in terms of severity and age. Furthermore, we report the results of the principal component analysis (PCA) and Logistic Regression (LR) techniques to find out which variables are the most relevant and their respective weight. The results show that there are two very relevant variables for the detection of the most severe cases, yielding promissing results. One of our paper conclussions is a strong recommendation to the local authorities to improve the data gathering protocols. © 2022, Springer Nature Switzerland AG.

Triage Prediction of a Real Dataset of COVID-19 Patients in Alava

The COVID-19 pandemic has increased the pressure on developing clinical decision-making systems based on predictive algorithms, potentially helping to reduce the unmanageable strain on healthcare systems. In an attempt to address this challenging health situation, we attempted to provide a contribution to this endeavour with an in-depth study of a real-life dataset of covid-19 patients from a local hospital. In this paper, we approach the problem as triage prediction problem, formulated as multi-class classification problem, with special care on the age normalization of physiological variables. We report experimental results obtained on a data sample covering COVID-19 patients assisted in a local hospital. To do this, we tried to emulate the triage decisions of the physicians recorded in a dataset containing the measurements of physiological variables and the triage decision. We obtained results that provide encouragement for a real-life application development of the data balancing and classification in the prediction of the triage that the medical doctors will assign the critical patients. © 2022, Springer Nature Switzerland AG.

Counter Intituive COVID-19 Propagation Dynamics in Brazil

There are few studies concerning the propagation of COVID-19 pandemic, besides theoretical models that have produced alarming predictions. These models assume wave-like propagation dynamics on an uniform medium. However, it appears that COVID-19 follows unexpected propagation dynamics. In this paper we consider the state-wise data on COVID-19 mortality in Brazil provided by government sources. Conventional propagation models tell us that the pandemic should propagate over the neighboring states from the initial cases in Sao Paulo. We compute several measures of correlation and prediction by random forests finding that the patterns of propagation do not correlate well with the graph distances defined by the spatial neighborhood of the states. We think that this phenomenon deserves further attention in order to understand COVID-19 pandemic. © 2021, Springer Nature Switzerland AG.