Evaluating the policy of closing bars and restaurants in Cataluña and its effects on mobility and COVID19 incidence

An agent based simulation of COVID-19 history in Catalonia using extensive real datasets

During the COVID-19 pandemic, effective public policy interventions have been crucial in combating virus transmission, sparking extensive debate on crisis management strategies and emphasizing the necessity for reliable models to inform governmental decisions, particularly at the local level. Leveraging disaggregated socio-demographic microdata, including social determinants, age-specific strata, and mobility patterns, we design a comprehensive network model of Catalonia's population and, through numerical simulation, assess its response to the outbreak of COVID-19 over the two-year period 2020-21. Our findings underscore the critical importance of timely implementation of broad non-pharmaceutical measures and effective vaccination campaigns in curbing virus spread; in addition, the identification of high-risk groups and their corresponding maps of connections within the network paves the way for tailored and more impactful interventions.

A mixture of mobility and meteorological data provides a high correlation with COVID-19 growth in an infection-naive population: a study for Spanish provinces

Introduction

We use Spanish data from August 2020 to March 2021 as a natural experiment to analyze how a standardized measure of COVID-19 growth correlates with asymmetric meteorological and mobility situations in 48 Spanish provinces. The period of time is selected prior to vaccination so that the level of susceptibility was high, and during geographically asymmetric implementation of non-pharmacological interventions.

Methods

We develop reliable aggregated mobility data from different public sources and also compute the average meteorological time series of temperature, dew point, and UV radiance in each Spanish province from satellite data. We perform a dimensionality reduction of the data using principal component analysis and investigate univariate and multivariate correlations of mobility and meteorological data with COVID-19 growth.

Results

We find significant, but generally weak, univariate correlations for weekday aggregated mobility in some, but not all, provinces. On the other hand, principal component analysis shows that the different mobility time series can be properly reduced to three time series. A multivariate time-lagged canonical correlation analysis of the COVID-19 growth rate with these three time series reveals a highly significant correlation, with a median R-squared of 0.65. The univariate correlation between meteorological data and COVID-19 growth is generally not significant, but adding its two main principal components to the mobility multivariate analysis increases correlations significantly, reaching correlation coefficients between 0.6 and 0.98 in all provinces with a median R-squared of 0.85. This result is robust to different approaches in the reduction of dimensionality of the data series.

Discussion

Our results suggest an important effect of mobility on COVID-19 cases growth rate. This effect is generally not observed for meteorological variables, although in some Spanish provinces it can become relevant. The correlation between mobility and growth rate is maximal at a time delay of 2-3 weeks, which agrees well with the expected 5?10 day delays between infection, development of symptoms, and the detection/report of the case.

Association of emotion recognition ability and interpersonal emotional competence in anorexia nervosa: A study with a multimodal dynamic task

OBJECTIVE

Interpersonal difficulties are evidenced in Anorexia Nervosa (AN) and are thought to contribute to disease onset and maintenance, however, research in the framework of emotional competence is currently limited. Previous studies have often only used static images for emotion recognition tasks, and evidence is lacking on the relationships between performance-based emotional abilities and self-reported intra- and interpersonal emotional traits. This study aimed to test multimodal dynamic emotion recognition ability in AN and analyze its correlation with the psychometric scores of self- and other-related emotional competence.

METHOD

A total of 268 participants (128 individuals with AN and 140 healthy controls) completed the Geneva Emotion Recognition Test, the Profile of Emotional Competence, the Reading the Mind in the Eyes Test, and measures of general and eating psychopathology. Scores were compared between the two groups. Linear mixed effects models were utilized to examine the relationship between emotion recognition ability and self-reported measures and clinical variables.

RESULTS

Individuals with AN showed significantly poorer recognition of emotions of both negative and positive valence and significantly lower scores in all emotional competence dimensions. Beside emotion type and group, linear mixed models evidenced significant effects of interpersonal comprehension on emotion recognition ability.

DISCUSSION

Individuals with AN show impairment in multimodal emotion recognition and report their difficulties accordingly. Notably, among all emotional competence dimensions, interpersonal comprehension emerges as a significant correlate to emotion recognition in others, and could represent a specific area of intervention in the treatment of individuals with AN.

PUBLIC SIGNIFICANCE

In this study, we evidence that the ability to recognize the emotions displayed by others is related to the level of interpersonal emotional competence reported by individuals with anorexia nervosa. This result helps in understanding the social impairments in people with anorexia nervosa and could contribute to advancements in the application of the training of emotional competence in the treatment of this disorder.