Spatial Interactions and the Spread of COVID-19: A Network Perspective

We use unique data on the travel history of confirmed patients at a daily frequency across 31 provinces in China to study how spatial interactions influence the geographic spread of pandemic COVID-19. We develop and simultaneously estimate a structural model of dynamic disease transmission network formation and spatial interaction. This allows us to understand what externalities the disease risk associated with a single place may create for the entire country. We find a positive and significant spatial interaction effect that strongly influences the duration and severity of pandemic COVID-19. And there exists heterogeneity in this interaction effect: the spatial spillover effect from the source province is significantly higher than from other provinces. Further counterfactual policy analysis shows that targeting the key province can improve the effectiveness of policy interventions for containing the geographic spread of pandemic COVID-19, and the effect of such targeted policy decreases with an increase in the time of delay.

The stock market and NO2 emissions effects of COVID‐19 around the world

In this paper, we study the impact of the coronavirus disease 2019 pandemic in estimated panel vector autoregression models for 92 countries. The large cross‐section of countries allows us to shed light on the heterogeneity of the responses of stock markets and nitrogen dioxide emissions as high‐frequency measures of economic activity. We quantify the effect of the number of infections and four dimensions of policy measures: (1) containment and closure, (2) movement restrictions, (3) economic support, and (4) adjustments of health systems. Our main findings show that a surprise increase in the number of infections triggers a drop in our two measures of economic activity. Propping up economic support measures, in contrast, raises stock returns and emissions and, thus, contributes to the economic recovery. We also document vast differences in the responses across subsets of countries and between the first and the second wave of infections.

Learning Strategies for Sensitive Content Detection

Currently, the volume of sensitive content on the Internet, such as pornography and child pornography, and the amount of time that people spend online (especially children) have led to an increase in the distribution of such content (e.g., images of children being sexually abused, real-time videos of such abuse, grooming activities, etc.). It is therefore essential to have effective IT tools that automate the detection and blocking of this type of material, as manual filtering of huge volumes of data is practically impossible. The goal of this study is to carry out a comprehensive review of different learning strategies for the detection of sensitive content available in the literature, from the most conventional techniques to the most cutting-edge deep learning algorithms, highlighting the strengths and weaknesses of each, as well as the datasets used. The performance and scalability of the different strategies proposed in this work depend on the heterogeneity of the dataset, the feature extraction techniques (hashes, visual, audio, etc.) and the learning algorithms. Finally, new lines of research in sensitive-content detection are presented.

Distance traveled in times of pandemic: an endogenous switching regression approach

This paper studies the change in the distance traveled by domestic tourists considering the pre- and post-pandemic outbreak summer periods of 2019 and 2020. Using representative monthly microdata involving more than 31,000 trips conducted by Spanish residents, we examine the heterogeneity in behavioral adaptation to COVID-19 based on sociodemographic and trip-related characteristics. To account for selection effects and the potential change in the population composition of travelers between the two periods, we estimate an endogenous switching regression that conducts separate regressions for the pre- and post-pandemic periods in a unified econometric framework. Our results point to heterogeneous shifts in the distance traveled by domestic travelers after COVID-19 outbreak per sociodemographic group, with notable differences by travel purpose and lower relevance of traditional determinants like income.

Intra-urban vulnerability gap towards urban resilience in Covid19 pandemic (case of Surakarta greater urban, Indonesia)

Urban areas have interaction characteristics that favor the spread of the Covid-19 pandemic. The lifestyle of urban communities with higher close contact influences the speed of the spread of Covid19, which makes cities play an important role in the transmission of Covid19. Surgo Ventures' Covid19 Community Vulnerability Index variable is used to analyze the community vulnerability in Surakarta Greater Urban. Statistics from government agencies were used to collect data on population, heterogeneity, housing conditions, health care systems, and environmental risks, which were then analyzed in the sub-district spatial unit. The findings show a close correlation between the aggregate value of the Covid19 Community Vulnerability Index (CCVI) and the rate of spread of Covid19 both in the city center and the urban fringe. However, the variable with the strongest correlation in the urban area differs from the variable in the urban fringe area. Furthermore, there are differences in vulnerability in urban communities. This demonstrates the need for different Covid19 handling strategies in different communities, despite the fact that they are all part of the same urban service system. The identification of these determinants may subsequently contribute to the design of cities that are better prepared for future pandemics.

Monocyte Differentiation and Heterogeneity: Inter-Subset and Interindividual Differences.

The three subsets of human monocytes, classical, intermediate, and nonclassical, show phenotypic heterogeneity, particularly in their expression of CD14 and CD16. This has enabled researchers to delve into the functions of each subset in the steady state as well as in disease. Studies have revealed that monocyte heterogeneity is multi-dimensional. In addition, that their phenotype and function differ between subsets is well established. However, it is becoming evident that heterogeneity also exists within each subset, between health and disease (current or past) states, and even between individuals. This realisation casts long shadows, impacting how we identify and classify the subsets, the functions we assign to them, and how they are examined for alterations in disease. Perhaps the most fascinating is evidence that, even in relative health, interindividual differences in monocyte subsets exist. It is proposed that the individual's microenvironment could cause long-lasting or irreversible changes to monocyte precursors that echo to monocytes and through to their derived macrophages. Here, we will discuss the types of heterogeneity recognised in monocytes, the implications of these for monocyte research, and most importantly, the relevance of this heterogeneity for health and disease.

Superspreading, overdispersion and their implications in the SARS-CoV-2 (COVID-19) pandemic: a systematic review and meta-analysis of the literature.

BACKGROUND: A recurrent feature of infectious diseases is the observation that different individuals show different levels of secondary transmission. This inter-individual variation in transmission potential is often quantified by the dispersion parameter k. Low values of k indicate a high degree of variability and a greater probability of superspreading events. Understanding k for COVID-19 across contexts can assist policy makers prepare for future pandemics. METHODS: A literature search following a systematic approach was carried out in PubMed, Embase, Web of Science, Cochrane Library, medRxiv, bioRxiv and arXiv to identify publications containing epidemiological findings on superspreading in COVID-19. Study characteristics, epidemiological data, including estimates for k and R0, and public health recommendations were extracted from relevant records. RESULTS: The literature search yielded 28 peer-reviewed studies. The mean k estimates ranged from 0.04 to 2.97. Among the 28 studies, 93% reported mean k estimates lower than one, which is considered as marked heterogeneity in inter-individual transmission potential. Recommended control measures were specifically aimed at preventing superspreading events. The combination of forward and backward contact tracing, timely confirmation of cases, rapid case isolation, vaccination and preventive measures were suggested as important components to suppress superspreading. CONCLUSIONS: Superspreading events were a major feature in the pandemic of SARS-CoV-2. On the one hand, this made outbreaks potentially more explosive but on the other hand also more responsive to public health interventions. Going forward, understanding k is critical for tailoring public health measures to high-risk groups and settings where superspreading events occur.

Understanding the shared working spaces: a Geography of Co-working supply in Delhi.

The present paper investigates the location pattern of co-working spaces in Delhi which is absent in the existing body of knowledge. Delhi is a political, administrative, educational, scientific and innovation capital that accommodates many co-working spaces in India. We developed Ordinary least squares (OLS) and geographically weighted regression (GWR) models to understand the associations of co-working spaces of digital labourers with other urban socio-economic, services and lifestyle variables in Delhi using secondary data for 117 coworking locations in 280 municipal wards of NCT-Delhi. Model diagnostic suggested that the GWR model provides additional information regarding geographical distribution of coworking spaces, and density of bars, median house rent, fitness centres, metro train stations, restaurants, cinemas, cafés, and creative enterprises are statistically significant parameters to estimate them. The importance of coworking spaces has increased in the post-disaster period, so this study informs public policies to benefit people and companies who choose coworking routes, and recommends urban planners, developers, and real-estate professionals to consider the proximity of creative industries in planning and developing coworking spaces in the future. Also, in the post COVID-19 period, to increase local jobs and long-term place sustainability, a localised policy intervention for coworking spaces in Delhi is highly recommended.

Corrigendum: D-dimer trends elaborate the heterogeneity of risk in hospitalized patients with COVID-19: a multi-national case series from different waves.

[This corrects the article DOI: 10.3389/fmed.2023.1103842.].

A Brief Overview of Neutrophils in Neurological Diseases.

Neutrophils are the most abundant leukocyte in circulation and are the first line of defense after an infection or injury. Neutrophils have a broad spectrum of functions, including phagocytosis of microorganisms, the release of pro-inflammatory cytokines and chemokines, oxidative burst, and the formation of neutrophil extracellular traps. Traditionally, neutrophils were thought to be most important for acute inflammatory responses, with a short half-life and a more static response to infections and injury. However, this view has changed in recent years showing neutrophil heterogeneity and dynamics, indicating a much more regulated and flexible response. Here we will discuss the role of neutrophils in aging and neurological disorders; specifically, we focus on recent data indicating the impact of neutrophils in chronic inflammatory processes and their contribution to neurological diseases. Lastly, we aim to conclude that reactive neutrophils directly contribute to increased vascular inflammation and age-related diseases.

A meta-analysis on the effects of product scarcity

Product scarcity can influence purchase decisions, but this relationship is multifaceted due to the influence of various cues. This study aims to integrate knowledge of this subject through a meta-analysis. The findings suggest that the likelihood of purchasing a scarce product is greater under (i) scarcity conditions of excessive demand (rather than restricted supply) and variety (rather than a category), but not urgency (limited quantity and limited time) scarcity, and (ii) product conditions of enduring luxuries (as opposed to transitory luxuries) and the presence (rather than absence) of social signaling and seasonality. From a theoretical standpoint, this study offers a typology of product and scarcity cues and employs a meta-analysis to enhance our understanding of the relationships between product scarcity, product and scarcity cues, and purchase decisions, resulting in the establishment of a heterogeneous theory of product scarcity. From a managerial standpoint, the study suggests that product scarcity can affect purchase decisions and can be ethically utilized as a marketing strategy.

COVID-19 and the Science of Where

Geographic location plays a crucial role in many aspects of research about the COVID-19 pandemic. Yet measurement of geographic location is necessarily imperfect, providing one of many sources of uncertainty in geospatial analysis. The ecological fallacy and the modifiable areal unit problem may lead to false inferences from such analysis. Spatial dependence and spatial heterogeneity are empirical properties of geospatial data that also impact inference and generalizability. Data provenance is a growing issue given the many ways in which data can be manipulated in preparation for analysis. The chapter ends with a discussion of critical spatial thinking as an umbrella term that encompasses all of these issues. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Heterogeneous influence of individuals' behavior on mask efficacy in gathering environments

Wearing masks is an easy way to operate and popular measure for preventing epidemics. Although masks can slow down the spread of viruses, their efficacy in gathering environments involving heterogeneous person-to-person contacts remains unknown. Therefore, we aim to investigate the epidemic prevention effect of masks in different real-life gathering environments. This study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering environments. The transmission of COVID-19 is simulated using the Monte Carlo simulation method. The heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general society. Furthermore, the heterogeneity of gathering environments causes the epidemic prevention effect of masks to differ. Wearing masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools, high schools, and hospitals. However, the use of masks alone in primary schools and hospitals cannot control outbreaks. In high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse, masks can meet the need for prevention. Given the heterogeneity of individual behavior, if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing, the epidemic prevention effect of masks can be improved. Finally, asymptomatic infection has varying effects on the prevention effect of masks in different environments. The effect can be weakened or eliminated by increasing the usage rate of masks in high schools and workplaces. However, the effect on primary schools and hospitals cannot be weakened. This study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention. © 2022, Higher Education Press.

The COVID-19 shock and services trade decline: potential for digitalization matters

Global services trade declined by 20% during 2020 with significant heterogeneity across countries, geographical regions and sectors. We present stylized facts and provide hypotheses and empirical analysis seeking to explain this heterogeneity. The decline is found to be correlated with COVID-19 case and mortality rates;stringency of imposed lockdowns;the decline in merchandise trade;and with different ways of transacting services trade. The latter depends on the sectoral composition of services trade across countries, which in turn emanates from more fundamental determinants of comparative advantage in services, generating testable hypotheses to explain the observed heterogeneity in services trade decline. Focusing on attributes of digitalization and the role of value-chains, we find that human-capital-intensive countries with favourable digital-trade policies and greater ability to leverage ICT infrastructure were associated with relatively smaller declines. Moreover, the expected role of GVC-integration in accentuating the services trade decline finds little support in empirical results across sectors providing evidence instead for the GVC-resilience narrative. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

A Theoretical Linguistic Fuzzy Rule-Based Compartmental Modeling for the COVID-19 Pandemic

The COVID-19 pandemic has affected the whole world quite seriously. The number of new infectious cases and death cases are rapidly increasing over time. In this study, a theoretical linguistic fuzzy rule-based susceptible-exposed-infectious-isolated-recovered (SEIIsR) compartmental model has been proposed to predict the dynamics of the transmission of COVID-19 over time considering population immunity and infectiousness heterogeneity based on viral load in the model. The model's equilibrium points have been calculated, and stability analysis of the model's equilibrium points has been conducted. Consequently, the fuzzy basic reproduction number, R0f, of the fuzzy model has been formulated. Finally, the temporal dynamics of different compartmental populations with immunity and infectiousness heterogeneity using the fuzzy Mamdani model are delineated, and some disease control policies have been suggested to get over the infection in no time. Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Learning Peer Recommendation Based on Weighted Heterogeneous Information Networks on Online Learning Platforms

With the development of online education, there is an urgent need to solve the problem of the low completion rate of online learning courses. Although learning peer recommendation can effectively address this problem, prior studies of learning peer-recommendation methods extract only a portion of the interaction information and fail to take into account the heterogeneity of the various types of objects (e.g., students, teachers, videos, exercises, and knowledge points). To better motivate students to complete online learning courses, we propose a novel method to recommend learning peers based on a weighted heterogeneous information network. First, we integrate the above different objects, various relationships between objects, and the attribute values to links in a weighted heterogeneous information network. Second, we propose a method for automatically generating all meaningful weighted meta-paths to extract and identify meaningful meta-paths. Finally, we use the Bayesian Personalized Ranking (BPR) optimization framework to discover the personalized weights of target students on different meaningful weighted meta-paths. We conducted experiments using three real datasets, and the experimental results demonstrate the effectiveness and interpretability of the proposed method.

Social Distancing, Vaccination and Evolution of COVID-19 Transmission Rates in Europe

This paper provides estimates of COVID-19 transmission rates and explains their evolution for selected European countries since the start of the pandemic taking account of changes in voluntary and government mandated social distancing, incentives to comply, vaccination and the emergence of new variants. Evidence based on panel data modeling indicates that the diversity of outcomes that we document may have resulted from the nonlinear interaction of mandated and voluntary social distancing and the economic incentives that governments provided to support isolation. The importance of these factors declined over time, with vaccine uptake driving heterogeneity in country experiences in 2021. Our approach also allows us to identify the basic reproduction number, R0, which is precisely estimated around 5, which is much larger than the values in the range of 2.4–3.9 assumed in the extant literature.

Spatial differentiation of urban economic resilience and its influencing factors: evidence from Baidu migration big data

Under the development pattern of the "double cycle”, optimizing urban economic resilience is tremendously meaningful to improving a city's affordability and the adaptability of the economy and to promoting the Chinese economy to develop with high quality. Based on Baidu migration big data perspective, exploratory spatial data analysis (ESDA) and multi-scale geographical weighted regression (MGWR) model were used to analyze the spatial characteristics and driving factors of economic resilience in 287 Chinese cities in 2019. The results show that (1) the number of low-level economically resilient cities is the largest and distributed continuously, while the number of high-level economically resilient cities is the lowest and distributed in clusters and blocks;(2) compared with the Pearl River Delta and Yangtze River Delta, the population accumulation characteristic of the Beijing- Tianjin-Hebei region is relatively slow;(3) Both net inflow of population after spring festival and daily flow scale are significantly correlated with urban economic resilience, and the former will affect urban economic resilience;and (4) the spatial heterogeneity of each factor driving is significant, and they have different impact scales. The impact intensity is as follows: net population inflow > innovation ability > public financial expenditure > financial efficiency > urban size.

Spatial effects of public health laboratory emergency testing institutions under COVID-19 in China.

BACKGROUND: The transmission of 2019 novel coronavirus (COVID-19) has caused global panic in the past three years. Countries have learned an important lesson in the practice of responding to COVID-19 pandemic: timely and accurate diagnosis is critical. As an important technology of virus diagnosis, nucleic acid testing (NAT) is also widely used in the identification of other infectious diseases. However, geographic factors often constrain the provision of public health services such as NAT services, and the spatial nature of their resource allocation is a significant problem. METHODS: We used OLS, OLS-SAR, GWR, GWR-SAR, MGWR, and MGWR-SAR models to identify the determinants of spatial difference and spatial heterogeneity affecting NAT institutions in China. RESULTS: Firstly, we identify that the distribution of NAT institutions in China shows a clear spatial agglomeration, with an overall trend of increasing distribution from west to east. There is significant spatial heterogeneity in Chinese NAT institutions. Secondly, the MGWR-SAR model results show that city level, population density, number of tertiary hospitals and number of public health emergency outbreaks are important factors influencing the spatial heterogeneity of NAT institutions in China. CONCLUSIONS: Therefore, the government should allocate health resources rationally, optimise the spatial layout of testing facilities, and improve the ability to respond to public health emergencies. Meanwhile, third-party testing facilities need to focus on their role in the public health emergency response system as a market force to alleviate the inequitable allocation of health resources between regions. By taking these measures to prepare adequately for possible future public health emergencies.