Classification of wildfires in relation to land cover types and associated variables by applying cluster analysis: a case study in the Iberian Peninsula.

Wildfires are a major environmental problem that have both economic and ecological impacts. Wildfires typically spread in a particular pattern, determined by factors such as the elements on the ground that catch fire or their geographic location. This study reports and discusses how wildfires in the Valencian Community, Spain, have been spatially grouped in recent years (from 2016 to 2020).

Effect of Previous Anticoagulant Treatment on Risk of COVID-19.

Introduction: Little is known about the role played by anticoagulants in COVID-19.

Objective: The aim of this study was to assess the impact of previous anticoagulant treatment on risk of hospitalization due to COVID-19, progression to severe COVID-19 and susceptibility to COVID-19 infection.

Methods: We conducted a multiple population-based case-control study in northwest Spain, in 2020, to assess (1) risk of hospitalization: cases were all patients admitted due to COVID-19 with PCR confirmation, and controls were a random matched sample of subjects without a positive PCR; (2) progression: cases were hospitalized COVID-19 subjects, and controls were all non-hospitalized COVID-19 patients; and (3) susceptibility: cases were patients with a positive PCR (hospitalized and non-hospitalized), and the controls were the same as for the hospitalization model.

Spatio-temporal modeling of traffic accidents incidence on urban road networks based on an explicit network triangulation.

Traffic deaths and injuries are one of the major global public health concerns. The present study considers accident records in an urban environment to explore and analyze spatial and temporal in the incidence of road traffic accidents. We propose a spatio-temporal model to provide predictions of the number of traffic collisions on any given road segment, to further generate a risk map of the entire road network.

PandemonCAT: Monitoring the COVID-19 Pandemic in Catalonia, Spain.

Background: The principal objective of this paper is to introduce an online interactive application that helps in real-time monitoring of the COVID-19 pandemic in Catalonia, Spain (PandemonCAT).

Methods: This application is designed as a collection of user-friendly dashboards using open-source R software supported by the Shiny package.

Results: PandemonCAT reports accumulated weekly updates of COVID-19 dynamics in a geospatial interactive platform for individual basic health areas (ABSs) of Catalonia.

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases.

Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We propose a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units.

Data on CO, temperature and air humidity records in Spanish classrooms during the reopening of schools in the COVID-19 pandemic.

In order to reduce the advance of the pandemic produced by COVID-19, many actions and restrictions have been applied and the field of education has been no exception. In Spain, during the academic year 2020-2021, face-to-face teaching generally continued in both primary and secondary schools. Throughout the year, different measures have been taken to reduce the likelihood of contagion in classrooms, one of which was to improve ventilation by opening windows and doors.

Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State.

The principal objective of this article is to assess the possible association between the number of COVID-19 infected cases and the concentrations of fine particulate matter (PM) and ozone (O), atmospheric pollutants related to people's mobility in urban areas, taking also into account the effect of meteorological conditions. We fit a generalized linear mixed model which includes spatial and temporal terms in order to detect the effect of the meteorological elements and COVID-19 infected cases on the pollutant concentrations. We consider nine counties of the state of New York which registered the highest number of COVID-19 infected cases.