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The epidemic course of the severe acute respiratory syndrome (SARS) has been differently divided according to its transmission pattern and the infection and mortality status. Unfortunately, such efforts for the coronavirus disease 2019 (COVID‐19) have been lacking. Does every epidemic have a unique epidemic course? Can we coordinate two arbitrary courses into an integrated course, which could better reflect a common real‐world progression pattern of the epidemics? To what degree can such arbitrary divisions help predict future trends of the COVID‐19 pandemic and future epidemics? Spatial lifecourse epidemiology provides a new perspective to understand the course of epidemics, especially pandemics, and a new toolkit to predict the course of future epidemics on the basis of big data. In the present data‐driven era, data should be integrated to inform us how the epidemic is transmitting at the present moment, how it will transmit at the next moment, and which interventions would be most cost‐effective to curb the epidemic. Both national and international legislations are needed to facilitate the integration of relevant policies of data sharing and confidentiality protection into the current pandemic preparedness guidelines.

Abstract
In the dynamic relation of "the positive - the middle item - the alien", the middle item is pressed by both ends. The pressure from the positive on the middle item, whose intensity is related to cultural tolerance, is driven by the need to maintain its position. That from the alien on the middle item comes from the claim for the selfoverturn of the subculture as well as the pursuit by art of its own markedness. At different levels of culture, the pressure on the middle item is different. The subtle and complex tension among the three influences the developmental trend of culture

Abstract
This is an interview in which six questions formulated by J. Peng on the contemporary developments in semiotics at the University Tartu are addressed by K. Kull. The questions concern the role of Juri Lotman's semiotics today, its relationship to the works of Jakob von Uexküll and Thomas A. Sebeok, the importance of the concepts semiosphere and umwelt, and more.

This book examines practices on the relationship between sign and meaning in the Pre-Imperial period of China from the semiotics perspective. Although the Chinese civilization did not develop a comprehensive semiotics system in that period, they are highly semiotic in many ways. The thinking and application of signs of Chinese people can be found in many classics, such as The Book of Changes, The Analects of Confucius, Tao De Jing and Zhuangzi. This book begins its study by re-examining the semiotic thoughts contained in The Book of Changes and inquiries into the thoughts of the major philosophers of different schools. It provides insights into the findings of these philosophers concerning the relationship between sign and meaning. In particular, it concentrates on how the prosperity of the various contending semiotic thoughts complemented each other in forming a sign system. In addition, the book also emphasizes the wholeness and associativity of observing things and studying relevant signs of Chinese people. As the first monograph in any language to systematically summarize Chinese semiotic thought in the Pre-Imperial period, this book helps promote understanding of the traditional Chinese culture and mindset

This paper studies the evolution of self-appraisal and social power, for a group of individuals who discuss and form opinions. We consider a modification of the recently proposed DeGroot-Friedkin (DF) model, in which the opinion formation process takes place on the same timescale as the reflected appraisal process; we call this new model the single-timescale DF model. We provide a comprehensive analysis of the equilibria and convergence properties of the model for the settings of irreducible and reducible influence networks. For the setting of irreducible influence networks, the single-timescale DF model has the same behavior as the original DF model, that is, it predicts among other things that the social power ranking among individuals is asymptotically equal to their centrality ranking, that social power tends to accumulate at the top of the centrality ranking hierarchy, and that an autocratic (resp., democratic) power structure arises when the centrality scores are maximally nonuniform (resp., uniform). For the setting of reducible influence networks, the single-timescale DF model behaves differently from the original DF model in two ways. First, an individual, who corresponds to a reducible node in a reducible influence network, can keep all social power in the single-timescale DF model if the initial condition does so, whereas its social power asymptotically vanishes in the original DF model. Second, when the associated network has multiple sinks, the two models behave very differently: the original DF model has a single globally-attractive equilibrium, whereas any partition of social power among the sinks is allowable at equilibrium in the single-timescale DF model.

BACKGROUND: Infectious causes of childhood deaths in the world have decreased substantially in the 21st century. This trend has exposed accidental deaths as an increasingly important future challenge. Presently, little is known about the cause structure of accidental childhood deaths in low- and middle-income country (LMIC) settings. In this paper, we aim to establish cause structure for accidental deaths in children aged 0-4 years in China in the year 2010. METHODS: In this paper, we explored the database of 208 multi-cause child mortality studies in Chinese that formed a basis for the first published estimate of the causes of child deaths in China (for the year 2008). Only five of those studies identified specific causes of accidental deaths. Because of this, we searched the Chinese medical literature databases CNKI and WanFang for single-cause mortality studies that were focused on accidental deaths. We identified 71 further studies that provided specific causes for accidental deaths. We used epidemiological modeling to estimate the number of accidental child deaths in China in 2010 and to assign those deaths to specific causes. RESULTS: In 2010, we estimated 314 581 deaths in children 0-4 years in China, of which 31 633 (10.1%) were accidental. Accidental deaths contributed 7240 (4.0%) of all deaths in neonatal period, 8838 (10.5%) among all post-neonatal infant deaths, and 15 554 (31.7%) among children with 1-4 years of age. Among four tested models, the most predictive was used to establish the likely cause structure of accidental deaths in China. We estimated that asphyxia caused 9490 (95% confidence interval (CI) 8224-11 072), drowning 5694 (95% CI 5061-6327), traffic accidents 3796 (95% CI 3163-4745), poisoning 3163 (95% CI 2531-3796) and falls 2531 (95% CI 2214-3163) deaths. Based on medians from a few rare studies, we also predict 633 (95% CI 316-1265) deaths to be due to burns and 316 (95% CI 0-633) due to falling objects. Together, these 7 causes explain more than 80% of all accidental deaths when modeling is primarily used, and more than 95% when the analysis is based purely on medians from the 76 available studies. CONCLUSIONS: Reduction in global child mortality is a leading political priority and accidental deaths will soon emerge as one of the main challenges. In this paper we provided a detailed breakdown of causes of these deaths in a large middle-income country. We noted that, wherever the share of accidental deaths among all child deaths is increased, drowning is more likely to be the leading cause; asphyxia seems to be equally important in all contexts, while traffic accidents, poisoning and falls are relatively more important in contexts where the overall share of accidents to all child deaths is low.

China was attacked by a serious influenza A (H7N9) virus in 2013. The first human infection case was confirmed in Shanghai City and soon spread across most of eastern China. Using the methods of Geographic Information Systems (GIS) and ecological niche modeling (ENM), this research quantitatively analyzed the relationships between the H7N9 occurrence and the main environmental factors, including meteorological variables, human population density, bird migratory routes, wetland distribution, and live poultry farms, markets, and processing factories. Based on these relationships the probability of the presence of H7N9 was predicted. Results indicated that the distribution of live poultry processing factories, farms, and human population density were the top three most important determinants of the H7N9 human infection. The relative contributions to the model of live poultry processing factories, farms and human population density were 39.9%, 17.7% and 17.7%, respectively, while the maximum temperature of the warmest month and mean relative humidity had nearly no contribution to the model. The paper has developed an ecological niche model (ENM) that predicts the spatial distribution of H7N9 cases in China using environmental variables. The area under the curve (AUC) values of the model were greater than 0.9 (0.992 for the training samples and 0.961 for the test data). The findings indicated that most of the high risk areas were distributed in the Yangtze River Delta. These findings have important significance for the Chinese government to enhance the environmental surveillance at multiple human poultry interfaces in the high risk area.

AbstractObjectiveThis study aimed to investigate the longitudinal cross‐lagged association between family mutuality, depression, and anxiety among Chinese adolescents before and after the COVID‐19 lockdown in 2020.BackgroundLimited attention has been paid to the longitudinal links between family mutuality, depression, and anxiety in the context of the COVID‐19 pandemic.MethodWe used self‐administered questionnaires to collect data from three high schools and two middle schools in Chengdu City at two time points: Time 1 (T1), December 23, 2019–January 13, 2020; Time 2 (T2), June 16–July 8, 2020. The sample consisted of 7,958 participants who completed two wave surveys before and after the COVID‐19 lockdown. We analyzed the data using cross‐lagged structural equation modeling.ResultsThe longitudinal cross‐lagged model showed family mutuality at T1 significantly predicted depression, anxiety, and family mutuality at T2. We observed a decreasing prevalence of depression and anxiety after the COVID‐19 lockdown.ConclusionFamily mutuality plays an important role in mitigating long‐term mental health disorders, such as depression and anxiety. More family‐centered psychological interventions could be developed to alleviate mental health disorders during lockdowns.ImplicationsImproving family mutuality (e.g., mutual support, interaction, and caring among family members) could be beneficial for reducing mental health disorders among Chinese adolescents during the COVID‐19 pandemic.