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. A Bayesian methodology using Integrated nested Laplace approximations with stochastic partial differential equations (SPDE) has been applied in the modeling process. As a novelty, we have introduced SPDE network triangulation to estimate the spatial autocorrelation restricted to the linear network. The resulting risk maps provide information to identify safe routes between source and destination points, and can be useful for accident prevention and multi-disciplinary road safety measures.

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. Adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using a generalized linear mixed model. RESULTS: The consumption of antivitamin K and direct-acting anticoagulants apparently was not associated with the risk of progression to severe COVID-19 (OR 0.93 [95% CI 0.74-1.17] and OR 1.04 [95% CI 0.79-1.36], respectively). Antivitamin K anticoagulants were associated with a significantly lower risk of hospitalization (OR 0.77 [95% CI 0.64-0.93]), which, in part, can be explained by a decreased risk of susceptibility to infection (OR 0.83 [95% CI 0.74-0.92]). The use of direct-acting anticoagulants was not associated with the risk of hospitalization, although it also seems to decrease susceptibility (OR 0.85 [95% CI 0.74-0.98]). It has also been observed that low-molecular-weight heparins were associated with an increased risk of progression to severe COVID-19 (OR 1.25 [95% CI 1.01-1.55]). CONCLUSION: The results of this study have shown that antivitamin K anticoagulants and direct-acting anticoagulants do not increase the risk of progression to more severe stages. Antivitamin K consumption was associated with a lower risk of hospitalization and susceptibility to infection.

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. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model, together with the number of infections and deaths in nearby areas.

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. It also shows on a georeferenced map the evolution of vaccination campaigns representing the share of population with either one or two shots of the vaccine, for populations of different age groups. In addition, the application reports information about environmental and socioeconomic variables and also provides an interactive interface to visualize monthly public mobility before, during, and after the lockdown phases. Finally, we report the smoothed standardized COVID-19 infected cases and mortality rates on maps of basic health areas ABSs and regions of Catalonia. These smoothed rates allow the user to explore geographic patterns in incidence and mortality rates. The visualization of the variables that could have some influence on the spatiotemporal dynamics of the pandemic is demonstrated. CONCLUSIONS: We believe the addition of these new dimensions, which is the key innovation of our project, will improve the current understanding of the spread and the impact of COVID-19 in the community. This application can be used as an open tool for consultation by the public of Catalonia and Spain in general. It could also have implications in facilitating the visualization of public health data, allowing timely interpretation due to the unpredictable nature of the pandemic.

Data on CO2, 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. One of the most commonly used techniques to check for good ventilation has been CO2 monitoring. This work provides a set of 80,000 CO2 concentration records collected by low-cost Internet of Things nodes, primarily located within twelve classrooms in two primary schools. The published observations were collected between 1 May 2020 and 23 June 2021. Additionally, the same dataset includes temperature, air humidity and battery level observations.

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 (PM2.5) and ozone (O3), 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. We implemented a Bayesian method using integrated nested Laplace approximation (INLA) with a stochastic partial differential equation (SPDE). The results emphasize that all the components used in designing the model contribute to improving the predicted values and can be included in designing similar real-world data (RWD) models. We found only a weak association between PM2.5 and ozone concentrations with COVID-19 infected cases. Records of COVID-19 infected cases and other covariates data from March to May 2020 were collected from electronic health records (EHRs) and standard RWD sources.