Microfluidic chip and isothermal amplification technologies for the detection of pathogenic nucleic acid.

The frequency of outbreaks of newly emerging infectious diseases has increased in recent years. The coronavirus disease 2019 (COVID-19) outbreak in late 2019 has caused a global pandemic, seriously endangering human health and social stability. Rapid detection of infectious disease pathogens is a key prerequisite for the early screening of cases and the reduction in transmission risk. Fluorescence quantitative polymerase chain reaction (qPCR) is currently the most commonly used pathogen detection method, but this method has high requirements in terms of operating staff, instrumentation, venues, and so forth. As a result, its application in the settings such as poorly conditioned communities and grassroots has been limited, and the detection needs of the first-line field cannot be met. The development of point-of-care testing (POCT) technology is of great practical significance for preventing and controlling infectious diseases. Isothermal amplification technology has advantages such as mild reaction conditions and low instrument dependence. It has a promising prospect in the development of POCT, combined with the advantages of high integration and portability of microfluidic chip technology. This study summarized the principles of several representative isothermal amplification techniques, as well as their advantages and disadvantages. Particularly, it reviewed the research progress on microfluidic chip-based recombinase polymerase isothermal amplification technology and highlighted future prospects.

Reflections on Quarantine and Social Relations in Zijian Chi's Snow Crow in Light of Shanghai's 2022 Quarantines

Zijian Chi's 2010 Snow Crow explores how the plague outbreak and subsequent quarantines of 1910-1911 in Harbin China affect ordinary people. Snow Crow portrays social changes that result from the pandemic and reveals much about quarantine. The most significant initial impacts are social. By comparing the Shanghai China quarantines of the first half of 2022 with the pandemic prevention measures undertaken in Harbin in Zijian, Chi's Snow Crow, we can see an interplay of government and civilian responses to the prevention and control of the plague's spread. Quarantine makes business-as-usual impossible. While we certainly know more than we did in 1910, we also stand to learn a lot from the lessons of the Harbin Plague, lessons that Snow Crow offers - if only we will listen.

Rapid and universal detection of SARS-CoV-2 and influenza A virus using a reusable dual-channel optic fiber immunosensor.

Establishment of rapid on-site detection technology capable of concurrently detecting SARS-Cov-2 and influenza A virus is urgent to effectively control the epidemic from these two types of important viruses. Accordingly, we developed a reusable dual-channel optical fiber immunosensor (DOFIS), which utilized the evanescent wave-sensing properties and tandem detection mode of the mobile phase, effectively accelerating the detection process such that it can be completed within 10 min. It could detect the nucleoprotein of multiple influenza A viruses (H1N1, H3N2, and H7N9), as well as the spike proteins of the SARS-CoV-2 Omicron and Delta variants, and could respond to 20 TCID50 /ml SARS-CoV-2 pseudovirus and 100 TCID50 /ml influenza A (A/PR/8/H1N1), presenting lower limit of detection and wider linear range than enzyme-linked immunosorbent assay. The detection results on 26 clinical samples for SARS-CoV-2 demonstrated its specificity (100%) and sensitivity (94%), much higher than the sensitivity of commercial colloidal gold test strip (35%). Particularly, DOFIS might be reused more than 80 times, showing not only cost-saving but also potential in real-time monitoring of the pathogenic viruses. Therefore, this newly-developed DOFIS platform is low cost, simple to operate, and has broad spectrum detection capabilities for SARS-CoV-2 mutations and multiple influenza A strains. It may prove suitable for deployment as a rapid on-site screening and surveillance technique for infectious disease.

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.

Control strategies and their effects on the COVID-19 pandemic in 2020 in representative countries.

COVID-19 is the most severe pandemic globally since the 1918 influenza pandemic. Effectively responding to this once-in-a-century global pandemic is a worldwide challenge that the international community needs to jointly face and solve. This study reviews and discusses the key measures taken by major countries in 2020 to fight against COVID-19, such as lockdowns, social distancing, wearing masks, hand hygiene, using Fangcang shelter hospitals, large-scale nucleic acid testing, close-contacts tracking, and pandemic information monitoring, as well as their prevention and control effects. We hope it can help improve the efficiency and effectiveness of pandemic prevention and control in future.

Mental Status and Psychological Needs of Chinese Police Officers in a Highly Impacted City during the COVID-19 Pandemic

The purpose of the study was to investigate the mental status and psychological needs of police officers during the COVID-19 outbreak in China. The Anti-Pandemic Public Mental Status Scale and self-administered Psychological Needs Scale were administered online to police officers in Y city, a significant sub-central city of Hubei Province, where was affected by the pandemic the most seriously. A total of 5,467 valid questionnaires were collected, of which female police accounted for 17.7%. Compared with the national public and Y city public data previously measured using the Anti-Pandemic Public Mental Status Scale, this study found that 24.6% of the Y city police suffered maladaptive problems. The mental status of the national public was the best, followed by the Y city police. The mental status of the Y city public was the worst. Moreover, there was a significant interaction between gender and unit type of Y city police (p = 0.02). The mental status of female police working in prisons was worse than their male counterparts (p = 0.01). Furthermore, psychological needs survey results showed that the police most wanted to learn the topics of self-adjustment and family relations. The most desired psychological assistances were relaxation and stress reduction, while the percentage of willingness to choose psychological counseling was low. During the pandemic, some police officers showed obvious psychological symptoms and the mental health services could be provided according to their psychological needs.