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.

Rapid and sustainable self-questionnaire for large-scale psychological screening in pandemic conditions for healthcare workers.

Background: The pandemic caused by a coronavirus (COVID-19) has shocked healthcare systems worldwide. However, the psychological stressors remain unclear. The objective of this study was to assess the impact of a major pandemic on healthcare workers. We hypothesized that exposure to the virus would be the primary cause of psychological stress perceived by healthcare workers. Methods: A national cross-sectional study conducted via an online questionnaire was distributed between April 9 and April 19, 2020 with a non-probabilistic sample technique. A structural equation model (SEM) was built with the variable "exposure to the virus" and the Psychological Stress and Adaptation at work Score (PSAS). "Exposure to the virus" was defined as the combined factors of 'personal-sphere', "work-related stress" and "hospital characteristics." A generalized linear model (GLM) was also tested. Results: A total of 2,197 participants filled in the questionnaire and were analyzed. The exploratory factor analysis showed statistically significant variables related to the personal-sphere, work-related stress and the hospital's characteristics, although the confirmatory factor analysis showed only the work-related stress factors to be significant. The GLM showed that personal-sphere-related variables (P < .001), stress at work (P < 0.001) and age (P < 0.001) were statistically significant. Conclusion: Physical exposure to the virus is an essential factor that contributes to the psychological impact perceived during the pandemic by healthcare professionals. A combination of personal-sphere variables, work-related stress and hospital characteristics is a significant factor correlating with the degree of stress measured by PSAS, a new and fast instrument to assess stress in healthcare workers.

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.

Clinical Characteristics of COVID-19 hospitalised patients associated with mortality: a cohort study in Spain

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. In this study, we analysed 5,342 patients' recordings and selected a cohort of 177 hospitalised patients with a poor prognosis at an early stage. We assessed during six 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. 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. 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.

COVID-19 psychological impact in 3109 healthcare workers in Spain: The PSIMCOV group.

BACKGROUND: The current coronavirus disease (COVID-19) has a great impact worldwide. Healthcare workers play an essential role and are one of the most exposed groups. Information about the psychosocial impact on healthcare workers is limited. METHODS: 3109 healthcare workers completed a national, internet-based, cross-sectional 45-item survey between 9 and 19 April 2020. The objective is to assess the psychological impact of the COVID-19 pandemic in Spanish healthcare workers. A Psychological Stress and Adaptation at work Score (PSAS) was defined combining four modified versions of validated psychological assessment tests (A) Healthcare Stressful Test, (B) Coping Strategies Inventory, (C) Font-Roja Questionnaire and (D) Trait Meta-Mood Scale. RESULTS: The highest psychosocial impact was perceived in Respiratory Medicine, the mean (S.D.) PSAS was 48.3 (13.6) and Geriatrics 47.6 (16.4). Higher distress levels were found in the geographical areas with the highest incidence of COVID-19 (>245.5 cases per 100 000 people), PSAS 46.8 (15.2); p < 0.001. The least stress respondents were asymptomatic workers PSAS, 41.3 (15.4); p < 0.001, as well as those above 60 years old, PSAS, 37.6 (16); p < 0.001. Workers who needed psychological therapy and did not receive it, were more stressed PSAS 52.5 (13.6) than those who did not need it PSAS 39.7 (13.9); p < 0.001. CONCLUSIONS: The psychological impact in healthcare workers in Spain during COVID-19 emergency has been studied. The stress perceived is parallel to the number of cases per 100 000 people. Psychotherapy could have a major role to mitigate the experimented stress level.

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.