An Online Solution Focused Brief Therapy for Adolescent Anxiety during the COVID-19 pandemic: A randomized controlled trial

In this randomized clinical trial, we investigated the efficacy of an online solution focused brief therapy (SFBT) for adolescents' anxiety symptoms during the COVID-19 period. Eligible participants were between the ages of 11 and 18 years, scored a 10 or above on the Generalized Anxiety Disorder-7 (GAD-7). The results found that compared to adolescents who did not receive any treatment, the intervention yielded significant results in alleviating adolescents' anxiety and depressive symptoms while promoting problem oriented coping strategies at immediate post-intervention. The therapeutic benefit has persisted, as shown in our results from the 1-month follow-up.

An online solution focused brief therapy for adolescent anxiety : A randomized controlled trial.

In this randomized clinical trial, we investigated the efficacy of an online solution focused brief therapy (SFBT) for adolescents' anxiety symptoms during the COVID-19 period. Eligible participants were between the ages of 11 and 18 years, scored a 10 or above on the Generalized Anxiety Disorder-7 (GAD-7). The results found that compared to adolescents who did not receive any treatment, the intervention yielded significant results in alleviating adolescents' anxiety and depressive symptoms while promoting problem oriented coping strategies at immediate post-intervention. The therapeutic benefit has persisted, as shown in our results from the 1-month follow-up.

PaCAR: COVID-19 Pandemic Control Decision Making via Large-Scale Agent-Based Modeling and Deep Reinforcement Learning.

BACKGROUND: Policy makers are facing more complicated challenges to balance saving lives and economic development in the post-vaccination era during a pandemic. Epidemic simulation models and pandemic control methods are designed to tackle this problem. However, most of the existing approaches cannot be applied to real-world cases due to the lack of adaptability to new scenarios and micro representational ability (especially for system dynamics models), the huge computation demand, and the inefficient use of historical information. METHODS: We propose a novel Pandemic Control decision making framework via large-scale Agent-based modeling and deep Reinforcement learning (PaCAR) to search optimal control policies that can simultaneously minimize the spread of infection and the government restrictions. In the framework, we develop a new large-scale agent-based simulator with vaccine settings implemented to be calibrated and serve as a realistic environment for a city or a state. We also design a novel reinforcement learning architecture applicable to the pandemic control problem, with a reward carefully designed by the net monetary benefit framework and a sequence learning network to extract information from the sequential epidemiological observations, such as number of cases, vaccination, and so forth. RESULTS: Our approach outperforms the baselines designed by experts or adopted by real-world governments and is flexible in dealing with different variants, such as Alpha and Delta in COVID-19. PaCAR succeeds in controlling the pandemic with the lowest economic costs and relatively short epidemic duration and few cases. We further conduct extensive experiments to analyze the reasoning behind the resulting policy sequence and try to conclude this as an informative reference for policy makers in the post-vaccination era of COVID-19 and beyond. LIMITATIONS: The modeling of economic costs, which are directly estimated by the level of government restrictions, is rather simple. This article mainly focuses on several specific control methods and single-wave pandemic control. CONCLUSIONS: The proposed framework PaCAR can offer adaptive pandemic control recommendations on different variants and population sizes. Intelligent pandemic control empowered by artificial intelligence may help us make it through the current COVID-19 and other possible pandemics in the future with less cost both of lives and economy. HIGHLIGHTS: We introduce a new efficient, large-scale agent-based epidemic simulator in our framework PaCAR, which can be applied to train reinforcement learning networks in a real-world scenario with a population of more than 10,000,000.We develop a novel learning mechanism in PaCAR, which augments reinforcement learning with sequence learning, to learn the tradeoff policy decision of saving lives and economic development in the post-vaccination era.We demonstrate that the policy learned by PaCAR outperforms different benchmark policies under various reality conditions during COVID-19.We analyze the resulting policy given by PaCAR, and the lessons may shed light on better pandemic preparedness plans in the future.

The suppression effect of emotional contagion in the COVID-19 pandemic: A multi-layer hybrid modelling and simulation approach

The entire world has suffered a lot since the outbreak of the novel coronavirus (COVID-19) in 2019, so simulation models of COVID-19 dynamics are urgently needed to understand and control the pandemic better. Meanwhile, emotional contagion, the spread of vigilance or panic, serves as a negative feedback to the epidemic, but few existing models take it into consideration. In this study, we proposed an innovative multi-layer hybrid modelling and simulation approach to simulate disease transmission and emotional contagion together. In each layer, we used a hybrid simulation method combining agent-based modelling (ABM) with system dynamics modelling (SDM), keeping spatial heterogeneity while reducing computation costs. We designed a new emotion dynamics model IWAN (indifferent, worried, afraid and numb) to simulate emotional contagion inside a community during an epidemic. Our model was well fit to the data of China, the UK and the US during the COVID-19 pandemic. If there weren't emotional contagion, our experiments showed that the confirmed cases would increase rapidly, for instance, the total confirmed cases during simulation in Guangzhou, China would grow from 334 to 2096, which increased by 528%. We compared the calibrated emotional contagion parameters of different countries and found that the suppression effect of emotional contagion in China is relatively more visible than that in the US and the UK. Due to the experiment results, the proposed multi-layer network model with hybrid simulation is valid and can be applied to the quantitative analysis of the epidemic trends and the suppression effect of emotional contagion in different countries. Our model can be modified for further research to study other social factors and intervention policies in the COVID-19 pandemic or future epidemics. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

The suppression effect of emotional contagion in the COVID-19 pandemic: A multi-layer hybrid modelling and simulation approach.

The entire world has suffered a lot since the outbreak of the novel coronavirus (COVID-19) in 2019, so simulation models of COVID-19 dynamics are urgently needed to understand and control the pandemic better. Meanwhile, emotional contagion, the spread of vigilance or panic, serves as a negative feedback to the epidemic, but few existing models take it into consideration. In this study, we proposed an innovative multi-layer hybrid modelling and simulation approach to simulate disease transmission and emotional contagion together. In each layer, we used a hybrid simulation method combining agent-based modelling (ABM) with system dynamics modelling (SDM), keeping spatial heterogeneity while reducing computation costs. We designed a new emotion dynamics model IWAN (indifferent, worried, afraid and numb) to simulate emotional contagion inside a community during an epidemic. Our model was well fit to the data of China, the UK and the US during the COVID-19 pandemic. If there weren't emotional contagion, our experiments showed that the confirmed cases would increase rapidly, for instance, the total confirmed cases during simulation in Guangzhou, China would grow from 334 to 2096, which increased by 528%. We compared the calibrated emotional contagion parameters of different countries and found that the suppression effect of emotional contagion in China is relatively more visible than that in the US and the UK. Due to the experiment results, the proposed multi-layer network model with hybrid simulation is valid and can be applied to the quantitative analysis of the epidemic trends and the suppression effect of emotional contagion in different countries. Our model can be modified for further research to study other social factors and intervention policies in the COVID-19 pandemic or future epidemics.

Covid-19: Global Progress in Developing Vaccines and Mechanisms for Equitable Distribution

The serious unique infectious pneumonia (COVID-19), caused by the new coronavirus (SARS-coV-2) in Wuhan, China in late 2019, has rapidly spread and become a global pandemic. It resulted in crises menacing people's health, lives, international engagement and economic systems. Thus, a vaccine holds most potential for a rapid means of resolving the pandemic before the end of 2021. There are 23 different candidate vaccines worldwide that have entered into clinical trials. Among them, the two that have progressed the fastest are Sinovac Biotech's inactivated vaccine and the recombinant vaccine (ChAdOx1-S) developed by Oxford University, which are already in the third phase of clinical trials. In late April 2020, WHO, EU and the Bill and Melinda Gates Foundation launched the ACT Accelerator Plan to acquire more COVID-19 tools. Also, GAVI, CEPI and WHO are jointly promoting the COVAX Facility, responsible for coordinating and integrating resources among worldwide vaccine developers and manufacturers. In addition to assuming risks of vaccine development, they also provide early investment in candidate vaccine products. These efforts increase chances of successful vaccine development as they expedite safe, efficient development and mass manufacturing of COVID-19 vaccines. This will result in the common goal of equitable distribution of vaccines for every nation.