Understanding small-scale COVID-19 transmission dynamics with the Granger causality test.

Mobility patterns have been broadly studied and deeply altered due to the coronavirus disease (COVID-19). In this paper, we study small-scale COVID-19 transmission dynamics in the city of Valencia and the potential role of subway stations and healthcare facilities in this transmission. A total of 2,398 adult patients were included in the analysis. We study the temporal evolution of the pandemic during the first six months at a small-area level. Two Voronoi segmentations of the city (based on the location of subway stations and healthcare facilities) have been considered, and we have applied the Granger causality test at the Voronoi cell level, considering both divisions of the study area. Considering the output of this approach, the so-called 'donor stations' are subway stations that have sent more connections than they have received and are mainly located in interchanger stations. The transmission in primary healthcare facilities showed a heterogeneous pattern. Given that subway interchange stations receive many cases from other regions of the city, implementing isolation measures in these areas might be beneficial for the reduction of transmission.

A mechanistic spatio-temporal modeling of COVID-19 data.

Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used. Point processes are the natural tool to perform such analyses. We analyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19) cases detected in Valencia (Spain) during the first 11 months (February 2020 to January 2021) of the pandemic. In particular, we propose a mechanistic spatio-temporal model for the first-order intensity function of the point process. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while similar studies have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations. The results suggest that there has only been a mild level of spatio-temporal interaction between cases in the study area, which to a large extent corresponds to people living in the same residential location. Extending our proposed model to larger areas could help us gain knowledge on the propagation of COVID-19 across cities with high mobility levels.

A city-level analysis of PM2.5 pollution, climate and COVID-19 early spread in Spain.

Purpuse: The COVID-19 outbreak has escalated into the worse pandemic of the present century. The fast spread of the new SARS-CoV-2 coronavirus has caused devastating health and economic crises all over the world, with Spain being one of the worst affected countries in terms of confirmed COVID-19 cases and deaths per inhabitant. In this situation, the Spanish Government declared the lockdown of the country. Methods: The variations of air pollution in terms of fine particulate matter (PM2.5) levels in seven representative cities of Spain are analyzed here considering the effect of meteorology during the national lockdown. The possible associations of PM2.5 pollution and climate with COVID-19 accumulated cases were also analyzed. Results: While the epidemic curve was flattened, the results of the analysis show that the 4-week Spanish lockdown significantly reduced the PM2.5 levels in only one city despite the drastically reduced human activity. Furthermore, no associations between either PM2.5 exposure or environmental conditions and COVID-19 transmission were found during the early spread of the pandemic. Conclusions: A longer period applying human activity restrictions is necessary in order to achieve significant reductions of PM2.5 levels in all the analyzed cities. No effect of PM2.5 pollution or weather on COVID-19 incidence was found for these pollutant levels and period of time. Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-022-00786-2.

On the association between COVID-19 vaccination levels and incidence and lethality rates at a regional scale in Spain.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19), has led to the deepest global health and economic crisis of the current century. This dramatic situation has forced the public health authorities and pharmaceutical companies to develop anti-COVID-19 vaccines in record time. Currently, almost 80% of the population are vaccinated with the required number of doses in Spain. Thus, in this paper, COVID-19 incidence and lethality rates are analyzed through a segmented spatio-temporal regression model that allows studying if there is an association between a certain vaccination level and a change (in mean) in either the incidence or the lethality rates. Spatial dependency is included by considering the Besag-York-Mollié model, whereas natural cubic splines are used for capturing the temporal structure of the data. Lagged effects between the exposure and the outcome are also taken into account. The results suggest that COVID-19 vaccination has not allowed yet (as of September 2021) to observe a consistent reduction in incidence levels at a regional scale in Spain. In contrast, the lethality rates have displayed a declining tendency which has associated with vaccination levels above 50%.

Clinical characteristics of COVID-19 hospitalized patients associated with mortality: A cohort study in Spain.

Background: The heterogeneity of patients with COVID-19 may explain the wide variation of mortality rate due to the population characteristics, presence of comorbidities and clinical manifestations. Methods: In this study, we analyzed 5342 patients' recordings and selected a cohort of 177 hospitalized patients with a poor prognosis at an early stage. We assessed during 6 months their symptomatology, coexisting health conditions, clinical measures and health assistance related to mortality. Multiple Cox proportional hazards models were built to identify the associated factors with mortality risk. Results: We observed that cough and kidney failure triplicate the mortality risk and both bilirubin levels and oncologic condition are shown as the most associated with the demise, increasing in four and ten times the risk, respectively. Other clinical characteristics such as fever, diabetes mellitus, breathing frequency, neutrophil-lymphocyte ratio, oxygen saturation, and troponin levels, were also related to mortality risk of in-hospital death. Conclusions: The present study shows that some symptomatology, comorbidities and clinical measures could be the target of prevention tools to improve survival rates.

A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data.

Establishing proper neighbor relations between a set of spatial units under analysis is essential when carrying out a spatial or spatio-temporal analysis. However, it is usual that researchers choose some of the most typical (and simple) neighborhood structures, such as the first-order contiguity matrix, without exploring other options. In this paper, we compare the performance of different neighborhood matrices in the context of modeling the weekly relative risk of COVID-19 over small areas located in or near Valencia, Spain. Specifically, we construct contiguity-based, distance-based, covariate-based (considering mobility flows and sociodemographic characteristics), and hybrid neighborhood matrices. We evaluate the goodness of fit, the overall predictive quality, the ability to detect high-risk spatio-temporal units, the capability to capture the spatio-temporal autocorrelation in the data, and the goodness of smoothing for a set of spatio-temporal models based on each of the neighborhood matrices. The results show that contiguity-based matrices, some of the distance-based matrices, and those based on sociodemographic characteristics perform better than the matrices based on k-nearest neighbors and those involving mobility flows. In addition, we test the linear combination of some of the constructed neighborhood matrices and the reweighting of these matrices after eliminating weak neighbor relations, without any model improvement.

Trends in Incidence and Transmission Patterns of COVID-19 in Valencia, Spain.

Importance: Limited information on the transmission and dynamics of SARS-CoV-2 at the city scale is available. Objective: To describe the local spread of SARS-CoV-2 in Valencia, Spain. Design, Setting, and Participants: This single-center epidemiological cohort study of patients with SARS-CoV-2 was performed at University General Hospital in Valencia (population in the hospital catchment area, 364 000), a tertiary hospital. The study included all consecutive patients with COVID-19 isolated at home from the start of the COVID-19 pandemic on February 19 until August 31, 2020. Exposures: Cases of SARS-CoV-2 infection confirmed by the presence of IgM antibodies or a positive polymerase chain reaction test result on a nasopharyngeal swab were included. Cases in which patients with negative laboratory results met diagnostic and clinical criteria were also included. Main Outcomes and Measures: The primary outcome was the characterization of dissemination patterns and connections among the 20 neighborhoods of Valencia during the outbreak. To recreate the transmission network, the inbound and outbound connections were studied for each region, and the relative risk of infection was estimated. Results: In total, 2646 patients were included in the analysis. The mean (SD) age was 45.3 (22.5) years; 1203 (46%) were male and 1442 (54%) were female (data were missing for 1); and the overall mortality was 3.7%. The incidence of SARS-CoV-2 cases was higher in neighborhoods with higher household income (ß2 [for mean income per household] = 0.197; 95% CI, 0.057-0.351) and greater population density (ß1 [inhabitants per km2] = 0.228; 95% CI, 0.085-0.387). Correlations with meteorological variables were not statistically significant. Neighborhood 3, where the hospital and testing facility were located, had the most outbound connections (14). A large residential complex close to the city (neighborhood 20) had the fewest connections (0 outbound and 2 inbound). Five geographically unconnected neighborhoods were of strategic importance in disrupting the transmission network. Conclusions and Relevance: This study of local dissemination of SARS-COV-2 revealed nonevident transmission patterns between geographically unconnected areas. The results suggest that tailor-made containment measures could reduce transmission and that hospitals, including testing facilities, play a crucial role in disease transmission. Consequently, the local dynamics of SARS-CoV-2 spread might inform the strategic lockdown of specific neighborhoods to stop the contagion and avoid a citywide lockdown.

The impact of modelling choices on modelling outcomes: a spatio-temporal study of the association between COVID-19 spread and environmental conditions in Catalonia (Spain).

The choices that researchers make while conducting a statistical analysis usually have a notable impact on the results. This fact has become evident in the ongoing research of the association between the environment and the evolution of the coronavirus disease 2019 (COVID-19) pandemic, in light of the hundreds of contradictory studies that have already been published on this issue in just a few months. In this paper, a COVID-19 dataset containing the number of daily cases registered in the regions of Catalonia (Spain) since the start of the pandemic to the end of August 2020 is analysed using statistical models of diverse levels of complexity. Specifically, the possible effect of several environmental variables (solar exposure, mean temperature, and wind speed) on the number of cases is assessed. Thus, the first objective of the paper is to show how the choice of a certain type of statistical model to conduct the analysis can have a severe impact on the associations that are inferred between the covariates and the response variable. Secondly, it is shown how the use of spatio-temporal models accounting for the nature of the data allows understanding the evolution of the pandemic in space and time. The results suggest that even though the models fitted to the data correctly capture the evolution of COVID-19 in space and time, determining whether there is an association between the spread of the pandemic and certain environmental conditions is complex, as it is severely affected by the choice of the model.

Changes in air pollution during COVID-19 lockdown in Spain: A multi-city study.

The COVID-19 pandemic has escalated into one of the largest crises of the 21st Century. The new SARS-CoV-2 coronavirus, responsible for COVID-19, has spread rapidly all around the world. The Spanish Government was forced to declare a nationwide lockdown in view of the rapidly spreading virus and high mortality rate in the nation. This study investigated the impact of short-term lockdown during the period from March 15th to April 12th 2020 on the atmospheric levels of CO, SO2, PM10, O3, and NO2 over 11 representative Spanish cities. The possible influence of several meteorological factors (temperature, precipitation, wind, sunlight hours, minimum and maximum pressure) on the pollutants' levels were also considered. The results obtained show that the 4-week lockdown had significant impact on reducing the atmospheric levels of NO2 in all cities except for the small city of Santander as well as CO, SO2, and PM10 in some cities, but resulted in increase of O3 level.

Injectable Gel Form of a Decellularized Bladder Induces Adipose-Derived Stem Cell Differentiation into Smooth Muscle Cells In Vitro.

Biologic scaffolds composed of extracellular matrix components have been proposed to repair and reconstruct a variety of tissues in clinical and pre-clinical studies. Injectable gels can fill and conform any three-dimensional shape and can be delivered to sites of interest by minimally invasive techniques. In this study, a biological gel was produced from a decellularized porcine urinary bladder by enzymatic digestion with pepsin. The enzymatic digestion was confirmed by visual inspection after dissolution in phosphate-buffered saline solution and Fourier-transform infrared spectroscopy. The rheological and biological properties of the gel were characterized and compared to those of the MatrigelTM chosen as a reference material. The storage modulus G' reached 19.4 ± 3.7 Pa for the 30 mg/mL digested decellularized bladder gels after ca. 3 h at 37 °C. The results show that the gel formed of the porcine urinary bladder favored the spontaneous differentiation of human and rabbit adipose-derived stem cells in vitro into smooth muscle cells to the detriment of cell proliferation. The results support the potential of the developed injectable gel for tissue engineering applications to reconstruct for instance the detrusor muscle part of the human urinary bladder.

Accuracy of SCORTEN and ABCD-10 to predict mortality and the influence of renal function in Stevens-Johnson syndrome/toxic epidermal necrolysis.

Epidermal necrolysis (EN) compromises a spectrum of life-threatening dermatoses (Stevens-Johnson Syndrome [SJS], overlap syndrome and toxic epidermal necrolysis [TEN]). Currently, no active therapeutic regimen with unequivocal benefit exists for SJS/TEN. SCORTEN is the widely-used prognostic scale specific for SJS/TEN. Nevertheless, a new prognostic scale, the ABCD-10, has been recently proposed. In this context, acute renal failure (ARF) seems to be an important comorbidity that could influence prognosis in SJS/TEN patients more than it is assumed by these two scales. Our objectives were to compare the accuracy of the SCORTEN and ABCD-10 scales in predicting the mortality in SJS/TEN, and to investigate the influence of renal failure on prognosis. The prognostic results of 18 patients with EN treated in two referral centers between 2013 and 2018 are presented. SCORTEN, ABCD-10 and renal function values were retrospectively collected for all patients. Out of the 18 patients who were analyzed, nine (50%) received only supportive therapy, four were treated with etanercept 50 mg in a single dose (22.2%) and five with corticosteroids (27.8%). Five patients developed ARF. Predicted mortality was 3.48 for SCORTEN and 2.33 for ABCD-10. Eventually, four patients died (22.2%), all had ARF and none of them received active treatment. Despite study limitations and in the absence of active treatment of choice, SCORTEN behaved as a reliable predictor of mortality in patients with EN, outperforming the newer ABCD-10. ARF was an early event associated with a poor prognosis, which could represent a prognostic marker to consider in the future.

Changing delta hepatitis patient profile: A single center experience in Valencia region, Spain.

BACKGROUND: Delta hepatitis is a rare infection with an aggressive disease course. For almost three decades, however, there have been no epidemiological studies in our traditionally endemic area. AIM: To investigate the prevalence of delta hepatitis in a sample of patients with chronic hepatitis B virus (HBV) infection followed at a Hepatology Unit in Valencia, Spain. METHODS: Retrospective evaluation of anti-hepatitis D virus-immunoglobulin G seroprevalence among patients with chronic HBV infection (n = 605) followed at a reference Hepatology Unit in Spain. RESULTS: The prevalence of anti-hepatitis D virus-immunoglobulin G among HBV-infected patients was 11.5%: Male (63%) and median age of 52 years. The majority were born in Spain (67%) and primarily infected through intravenous drug use. However, a significant percent (24.5%), particularly those diagnosed in more recent years, were migrants presumably nosocomially infected. Comorbidities such as diabetes (8.5%), obesity/overweight (55%), and alcohol consumption (34%) were frequent. A high proportion of patients developed liver complications such as cirrhosis (77%), liver decompensation (81%), hepatocellular carcinoma (HCC) (16.5%), or required liver transplantation (LT) (59.5%). Diabetes was associated with progression to cirrhosis, LT, and death. Male sex, increasing age, and alcohol were associated with LT and HCC. Compared to HBV mono-infected patients, delta individuals developed cirrhosis and liver decompensation more frequently, with no differences in HCC rates. CONCLUSION: Patients infected in the 1980's were mostly locals infected through intravenous drug use, whereas those diagnosed recently are frequently non-Spanish natives from endemic areas. Regardless of their origin, patients are predominantly male with significant comorbidities, which potentially play a major role in disease progression. We confirm a high rate of subsequent liver complications.

A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain.

The new SARS-CoV-2 coronavirus, which causes the COVID-19 disease, was reported in Wuhan, China, in December 2019. This new pathogen has spread rapidly around more than 200 countries, in which Spain has one of the world's highest mortality rates so far. Previous studies have supported an epidemiological hypothesis that weather conditions may affect the survival and spread of droplet-mediated viral diseases. However, some contradictory studies have also been reported in the same research line. In addition, many of these studies have been performed considering only meteorological factors, which can limit the reliability of the results. Herein, we report a spatio-temporal analysis for exploring the effect of daily temperature (mean, minimum and maximum) on the accumulated number of COVID-19 cases in the provinces of Spain. Non-meteorological factors such as population density, population by age, number of travellers and number of companies have also been considered for the analysis. No evidence suggesting a reduction in COVID-19 cases at warmer mean, minimum and maximum temperatures has been found. Nevertheless, these results need to be interpreted cautiously given the existing uncertainty about COVID-19 data, and should not be extrapolated to temperature ranges other than those analysed here for the early evolution period.

Estimating the occurrence of traffic accidents near school locations: A case study from Valencia (Spain) including several approaches.

Traffic safety around school locations is a topic of particular interest given the large number of vulnerable users, such as pedestrians or cyclists, that commute to them at certain times of the day. A dataset of traffic accidents recorded in Valencia (Spain) during 2014 and 2015 is analyzed in order to estimate the effects that school locations produce on traffic risk within their surroundings. The four typologies of school in this city according to the academic levels they offer (All-level, Preschool, Primary, Secondary) are distinguished and taken into consideration for the analysis. Two time windows comprising the starting time in the morning and the evening time once day school has ended are analyzed independently. Several statistical methods are used, including observed vs expected ratios, macroscopic conditional autoregressive modelling, logistic regression in the context of a case-control study design and risk modelling in relation to several school locations. The distances to each type of school and a set of environmental, traffic-related, demographic and socioeconomic covariates are employed for the analysis. The macroscopic modelling of accident counts and the modelling of risk as a function of the distance to each type of school serves to confirm that proximity to a school has an effect on the incidence of traffic accidents in particular time windows. Specifically, school types coexisting in Valencia show differential behaviour in this regard. In addition, several covariates have displayed a positive (bus stop density, complex intersections, main road length) and negative (land use entropy) association with accident counts in the time windows investigated. Finally, the definition of a case-control study design enabled us to observe some differences undetected by the macroscopic approaches that would require further research.

Identification of differential risk hotspots for collision and vehicle type in a directed linear network.

Traffic accidents can take place in very different ways and involve a substantially distinct number and types of vehicles. Thus, it is of interest to know which parts of a road structure present an overrepresentation of a specific type of traffic accident, specially for some typologies of collisions and vehicles that tend to trigger more severe consequences for the users being involved. In this study, a spatial approach is followed to estimate the risk that different types of collisions and vehicles present in the central area of Valencia (Spain), considering the accidents observed in this city during the period 2014-2017. A directed spatial linear network representing the non-pedestrian road structure of the area of interest was employed to guarantee an accurate analysis of the point pattern. A kernel density estimation technique was used to approximate the probability of risk along the network for each collision and vehicle type. A procedure based on these estimates and the sample size locally available within the network was designed and tested to determine a set of differential risk hotspots for each typology of accident considered. A Monte Carlo based simulation process was then defined to assess the statistical significance of each of the differential risk hotspots found, allowing the elaboration of rankings of importance and the possible rejection of the least significant ones.

Investigation of the consequences of the modifiable areal unit problem in macroscopic traffic safety analysis: A case study accounting for scale and zoning.

Traffic safety analysis at the macroscopic level usually relies on previously defined areal traffic analysis zones (TAZs) that are used as the units of investigation. Hence, statistical inference is made on the basis of such units, implying that the consideration of a certain TAZ configuration may influence the results and conclusions achieved. Regarding this, the modifiable areal unit problem (MAUP) is a well-known issue in the field of spatial statistics, which refers to the effects that arise in statistical properties and estimations when there is a change in areal units of analysis. In this paper, the consequences of MAUP have been investigated through a dataset of traffic crashes that occurred in Valencia within the years 2014 and 2015 and two common statistical models: a conditional autoregressive model and a geographically weighted regression. In the absence of an established TAZ scheme for the city, four classes of basic spatial units (BSUs) were considered: census tracts, hexagonal units and two types with construction based on the structure of main roads and intersections of the city. Each of these BSU types was specified at different levels of spatial aggregation. The main research objective was to investigate the final effects that changes in BSU type and scale have on model parameter estimations, but also the specific alterations that MAUP causes to data in terms of the distributional characteristics of the response, multicollinearity among the covariates and covariates' spatial autocorrelation. The results showed the presence and severity of MAUP for the dataset and area that were analysed. Although effects from scale variations were more moderate, changing the BSU type affected the results severely. The joint use of hexagonal units and a conditional autoregressive model achieved the best performance among all the possibilities explored, but the choice of a proper BSU unit should rely on more factors. Despite MAUP effects, educational centres showed a consistent (and negative) association with traffic crashes, a fact possibly related to their distribution across the whole city. Other covariates revealed a positive correlation with crash counts, but these findings were more uncertain given the discrepancies found at different scales and zonings.

Noise Disturbances and Calls for Police Service in València (Spain): A Logistic Model with Spatial and Temporal Effects.

The purpose of this paper is to explore the presence of spatial and temporal effects on the calls for noise disturbance service reported to the Local Police of València (Spain) in the time period from 2014 to 2015, and investigate how some socio-demographic and environmental variables affect the noise phenomenon. The analysis is performed at the level of València's boroughs. It has been carried out using a logistic model after dichotomization of the noise incidence variable. The spatial effects consider first- and second-order neighbors. The temporal effects are included in the model by means of one- and two-week temporal lags. Our model confirms the presence of strong spatio-temporal effects. We also find significant associations between noise incidence and specific age groups, socio-economic status, land uses, and recreational activities, among other variables. The results suggest that there is a problem of "social" noise in València that is not exclusively a consequence of coexistence between local residents. External factors such as the increasing number of people on the streets during weekend nights or during summer months severely increase the chances of expecting a noise incident.

Spatial analysis of traffic accidents near and between road intersections in a directed linear network.

Although most of the literature on traffic safety analysis has been developed over areal zones, there is a growing interest in using the specific road structure of the region under investigation, which is known as a linear network in the field of spatial statistics. The use of linear networks entails several technical complications, ranging from the accurate location of traffic accidents to the definition of covariates at a spatial micro-level. Therefore, the primary goal of this study was to display a detailed analysis of a dataset of traffic accidents recorded in Valencia (Spain), which were located into a linear network representing more than 30 km of urban road structure corresponding to one district of the city. A set of traffic-related covariates was constructed at the road segment level for performing the analysis. Several issues and methodological approaches that are inherent to linear networks have been shown and discussed. In particular, the network was defined in a way that allowed the explicit investigation of traffic accidents around road intersections and the consideration of traffic flow directionality. Zero-inflated negative binomial count models accounting for spatial heterogeneity were used. Traffic safety at road intersections was specifically taken into account in the analysis by considering the higher variability and number of zeros that can be observed at these road entities and the differential contribution of the covariates depending on the proximity of a road intersection. To complement the results obtained from the count models fitted, coldspots and hotspots along the network were also detected, with explanatory objectives. The models confirmed that spatial heterogeneity, overdispersion and the close presence of road intersections explain the accident counts observed in the road network analyzed. Hotspot detection revealed that several covariates whose contribution was unclear in the modelling approaches may also be affecting accident counts at the road segment level.