Evolving infectious disease dynamics shape school-based intervention effectiveness.

The Global Fund: replenishment and future-proofing

Digital phenotyping and the COVID-19 pandemic: Capturing behavioral change in patients with psychiatric disorders

The article quantitatively investigates transformative changes in human behavioral patterns about nature due to using digital forests in relaxation practices. In the era of COVID-19, when physical outings and social interactions have been limited, there has been a growing trend for people to engage in digital interactions with nature. This article aims to quantify the impact of people’s behavior and interaction in the digital space on the change of personal behavioral patterns in the physical natural environment, as well as to analyze the potential effects of digital “bathing in the forest”. Since in work it is advisable to take into account the influence of hidden factors, structural equation modeling was used for the needs of this study. The primary data were the answers of 300 respondents, which were obtained based on the results of a survey of people in the capital region of Japan. A five-point Likert scale was used to evaluate and analyze respondents’ opinions. The internal reliability of the questionnaire scales was tested using Cronbach’s alpha. The work offers an integral indicator for assessing the personal characteristics of individuals, based on which two groups of respondents were formed – extroverts (the index is higher than the median) and introverts (the index is lower than the median). The study analyzed the pathways that lead to changes in human behavior caused by the development of digital natural relaxation methods. The work established a statistically significant relationship between the effect of digital “bathing in the forest” and a change in human behavior (correlation coefficient 0.872). According to the results of structural modeling, it was found that digital “forest bathing” has a significant mediating effect on the promotion of pro-environmental behavior (correlation coefficient is 0.634) and that users who regularly experience nature in cyberspace are more likely to change their behavior concerning nature. It is substantiated that people closer to introversion are more actively involved in the digital space and interact with others, and significantly more often change their behavior towards the environment. This study is a rare contribution demonstrating the potential of enjoying digital “forest bathing” to induce environmentally oriented behavior, even in the physical absence of forests.

This thesis deals with persuasive design. The result of this research was motivated by the quest for understanding the different dimensions or categories of analysis that influence or even determines changes in human behavior through the use of product designs. A transdisciplinary vision was the departure research posture in order to understand the full human behavioral dimensions playing a role in human behavioral changes. This stance recognizes the human behavior as a result of internal forces that shapes the individuals intentionality and external forces that constrain and determines the final behavior. This thesis intent to answer the main question about, which are the conceptual dimensions involved in behavioral change through the use of a product design that persuades people into the new behavior? The hypothesis thus defines the existence of external forces to the product that influence and determines the result of an intended change in human behavior through the use of product design. These external forces are defined as conceptual dimensions that must be taken into account when designing products with the intent of provoking a human behavioral change. The use of the Grounded Theory approach allowed studying a singular phenomenon and generating categories of analysis through theoretical sampling.

In this paper, we address the problem of predicting the time of occurrence of next activity, given the current activity and the context. The models that predict activity and time of occurrence rely on the basic idea that human beings perform sequence of activities at specific times regularly. In other words, the models are dependent on human behavior. However, human behavior changes over time. Also, due to demands and goals to be attained, there may be change in human behavior. Therefore, one of the essential requirements of the predictive models for the given task is autonomous adaptation with time and without undergoing any retraining. Considering the requirement of an adaptive model, we propose an evolving fuzzy rule-based predictive model that can autonomously adapt with changes in the human behavior. The performance of the model is evaluated using real-life data with evolving characteristics and satisfactory results are obtained.

Changes in U.S. air pollution during the COVID-19 pandemic

Human security and universal health coverage: Japan's vision for the G7 Hiroshima Summit

The COVID-19 pandemic has affected air quality due to extreme changes in human behavior. We assessed the air quality response to different emergency levels during different COVID-19 periods and the naught period in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). We obtained the following conclusions: (1) The measures implemented to prevent and control of COVID-19 directly impacted ambient air pollutants. The air quality index and the concentrations of NO2, PM2.5, PM10, and CO in the GBA for 1–19 January 2020 declined 19.4%, 16.7%, 27.5%, 15.8%, and 25.7%, respectively, compared to the same time period in 2016–2019. (2) The reduction in air pollution was strongly associated with the first-level emergency response during this pandemic. The AQI, NO2, PM2.5, PM10, O3, and CO in the GBA decreased by 37.4%, 47.0%, 40.5%, 44.8%, 6.7%, and 24.1%, respectively. We found no statistically significant difference in the concentrations of different pollutants, except for NO2, during the second- and third-level responses. (3) The higher the emergency response level, the greater the NO2 pollutants reduction. The NO2 concentration was reduced by 47.0%, 25.5%, and 12.1% at emergency response levels 1, 2, and 3, respectively. The results highlight the importance of understanding the role of emergency response in air quality, and provide reference for authorities to formulate more scientific and reasonable emergency responses to epidemic prevention and control.

The majority of emerging infectious diseases are zoonotic, originating from domesticated animals and wildlife.1 The Covid-19 pandemic is also a zoonosis, with current evidence suggesting that the causative agent –...

On March 23, 2020, a national lockdown was imposed in the UK to limit interpersonal contact and the spread of COVID-19. Human mobility patterns were drastically adjusted as individuals complied with stay-at-home orders, changed their working patterns, and moved increasingly in the proximity of their home. Such behavioural changes brought about many spillover impacts, among which the sharp and immediate reduction in the concentration of nitrogen-based pollutants throughout the country. This work explores the extent to which urban Nitrogen Dioxide (NO2) concentration responds to changes in human behaviour, in particular human mobility patterns and commuting. We model the dynamic and responsive change in NO2 concentration in the period directly following national lockdown and respective opening orders. Using the national urban air quality monitoring network we generate a synthetic NO2 concentration series built from a time series of historic data to compare expected modelled trends to the actual observed patterns in 2020. A series of pre- and post-estimators are modelled to understand the scale of concentration responsiveness to human activity and varying ability of areas across the UK to comply with the lockdown closing and response to openings. Specifically, these are linked to workday commuting times and observed patterns of human mobility change obtained from Google mobility reports. We find a strong and robust co-movement of air pollution concentration and work-related mobility – concentrations of NO2 during typical weekday commuting hours saw a higher relative drop, moving in tandem with patterns of human mobility around workplaces over the course of lockdowns and openings. While NO2 concentrations remained relatively low around the time of reopening, particularly during commuting hours, there is a relatively fast responsiveness rate to concentrations increasing quickly in line with human activity. With one of the key Government advice for workers to take staggered transportation into work and lessen the burden of rush hours and adopting more flexible work-home arrangements, our results would suggest that reductions in NO2 in urban areas are particularly responsive to broader human patterns and dynamics over time as we transitioned towards new working routines.

In this paper, we propose a linguistic summarization procedure for describing long-term trends of change in human behavior. Our objective consists of defining methods that provide information to elders, caregivers, social workers or even family in an understandable language. We adapt a measure that we defined in previous work on soft cluster partition similarity for comparing behaviors that are adapted over time. From that measure, we are able to produce a time series that numerically describes change in behavior over time. In this article, the resulting time series is partitioned and linguistically summarized depending on a user's (caregiver, social worker, etc.) desired time resolution. Simulated resident behavior is used in order to explore a range of different scenarios and the response of the proposed linguistic summarization process is investigated.

School-Based Interventions to Reduce Obesity Risk in Children in High- and Middle-Income Countries.

Simulation’s benefits in medical education are well established. However, its use for pandemic preparedness in obstetrics is lacking. Management of obstetrical patients with suspected COVID-19 infection is a complex task with safety considerations for mother, fetus and healthcare workers. Implementation of new workflow algorithms to ensure safety is critical but is challenging to implement in real-time. We sought to improve pandemic preparedness by designing and deploying a high-fidelity simulation exercise involving the admission of a labouring obstetrical patient with suspected COVID-19 into a labour room, urgent transfer to the operating room and neonatal resuscitation. The creation of the simulation scenario was a multi-disciplinary effort with input from a focus group of key clinical stakeholders from within and outside of our centre to ensure clinical validity. Simulations were performed on the clinical unit during regular work hours so workflow could be observed in real-time with access to the equipment and personnel in which this clinical scenario would occur. We completed a total of 11 simulation sessions involving 42 participants. Feedback, obtained from debrief sessions and anonymous surveys, was categorized based on the human factors framework, and used as part of an iterative process to adapt, revise and improve the simulation scenario. The result of this iterative process was the creation of validated departmental infection control protocols that continue to be implemented through the second wave of the COVID-19 pandemic.

The article presents a novel statistical framework for COVID-19 transmission monitoring and control, which was developed and deployed at The Ohio State University main campus in Columbus during the Autumn term of 2020. Our approach effectively handles prevalence data with interval censoring and explicitly incorporates changes in transmission dynamics and human behaviour. To illustrate the methodology’s usefulness, we apply it to both synthetic and actual student SARS-CoV-2 testing data collected at the OSU Columbus campus in late 2020.

Molloy and Birnbrauer have recently exchanged views in this journal over how how many factors are necessary to provide an adequate account of human behaviour and behaviour change. Their differences apparently reflect alternative conceptualizations of the roles played by physiology and cognition in the analysis of behaviour. The present paper provides some background to these issues, showing that the current cognitive behavioural approach to physiology and cognition is but a reworking of some aspects of traditional mentalism. Following this, several alternatives to this traditional conceptualization are offered by way of (a) an analysis of how functional and structural contextual conditions affect behaviour and (b) distinctions between issues of behavioural process and content-related behavioural substance. For psychology to be a cumulative and progressive enterprise, a natural science approach to issues of physiology and cognition must be maintained, no matter how many factors may be tajcen as germane to human behaviour and behaviour change