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 Cross-Protective Immunity Landscape Among Different SARS-CoV-2 Variant RBDs.

Despite the fact that SARS-CoV-2 vaccines have been available in most parts of the world, the epidemic status remains grim with new variants emerging and escaping the immune protection of existing vaccines. Therefore, the development of more effective antigens and evaluation of their cross-protective immunity against different SARS-CoV-2 variants are particularly urgent. In this study, we expressed the wild type (WT), Alpha, Beta, Delta, and Lambda RBD proteins to immunize mice and evaluated their cross-neutralizing activity against different pseudoviruses (WT, Alpha, Beta, Delta, Lambda, and Omicron). All monovalent and pentavalent RBD antigens induced high titers of IgG antibodies against different variant RBD antigens. In contrast, WT RBD antigen-induced antibodies showed a lower neutralizing activity against Beta, Delta, Lambda, and Omicron pseudoviruses compared to neutralization against itself. Interestingly, Beta RBD antigen and multivalent antigen induced broader cross-neutralization antibodies than other variant RBD antigens. These data provide a reference for vaccine strain selection and universal COVID-19 vaccine design to fight the constant emergence of new SARS-CoV-2 variants.

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)

Imaging characteristics of initial chest computed tomography and clinical manifestations of patients with COVID-19 pneumonia.

PURPOSE: To investigate the chest CT imaging characteristics and clinical manifestations of patients with COVID-19 pneumonia. METHODS: This study included 150 patients with COVID-19 pneumonia diagnosed from January 10 to February 12, 2020 to analyze their clinical and CT imaging characteristics. RESULTS: The period between symptom onset and initial CT examination ranged from 1 to 8 days. There were 83 cases (55.33%) involving both lungs, 67 cases (44.67%) involving a single lung (left 25 cases and right 42 cases). There were 49 cases (32.67%) of single intrapulmonary lesion, 33 cases (22.00%) of multiple intrapulmonary lesions, 68 cases (44.00%) of diffused intrapulmonary lesions, 67 cases (44.67%) of subpleural lesions, 24 cases (16.00%) of lesions localizing along the bronchovascular bundles, and 59 cases (39.33%) with lesions in both locations. There were 18 cases (12.00%) exhibiting ground-glass nodules of < 10 mm, 124 cases (82.67%) of patchy ground-glass opacities with or without consolidation, 8 cases (5.33%) of cord-like lesions, 6 cases (4.00%) of pleural effusion, and 2 cases (1.33%) of enlarged lymph nodes. CONCLUSIONS: The main manifestations of initial chest CT in COVID-19 pneumonia patients was ground-glass opacities, commonly involving single site in patients < 35 years old and multiple sites and extensive area in patients > 60 years old. The common lesion sites were the subpleural region and the posterior basal segments of the lower lobes, mostly showing thickening of the interlobular septum and mixed with consolidation.

Impact of COVID-19 on Acute Stroke Presentation in a Designated COVID-19 Hospital.

Objectives: Thousands of designated COVID-19 hospitals have been set up in China to fight the ongoing COVID-19 pandemic. Anecdotal reports indicate a falling rate of acute stroke diagnoses in these hospitals during the COVID-19 period. We conducted an exploratory single-center analysis to estimate the change in acute stroke presentation at the designated COVID-19 hospitals. Methods: This retrospective observational study included all patients admitted to Yongchuan Hospital Affiliated to Chongqing Medical University with acute stroke between January 24 and March 10, 2020. Patient demographics, characteristics of the stroke, treatment details, and clinical outcomes were compared with those of patients admitted in the corresponding period in the year before (2019, "the pre-COVID-19 period"). Subgroup analysis was performed in the ischemic and hemorrhagic stroke groups. Results: A total of 110 patients presented with acute stroke symptoms during the COVID-19 pandemic, compared with 173 patients in the pre-COVID-19 period. A higher proportion of stroke patients presented to the hospital via emergency medical services during the pandemic (48.2 vs. 31.8%, p = 0.006). There was a lower proportion of ischemic stroke patients (50.9 vs. 65.3%, p = 0.016) than in the preceding year. There were significantly fewer patients with 90-day modified Rankin Scale score ≥3 in the COVID-19 period compared with the pre-COVID-19 period (17.3 vs. 30.6%, p = 0.012). Among patients with ischemic stroke, the mean time from patient arrival to vessel puncture for emergency endovascular therapy in the COVID-19 period was shorter than that in the pre-COVID-19 period (109.18 ± 71.39 vs. 270.50 ± 161.51 min, p = 0.002). Among patients with hemorrhagic stroke, the rate of emergency surgical operation in the COVID-19 period was higher than that in the pre-COVID-19 period (48.1 vs. 30.0%, p = 0.047). The mean time from patient arrival to emergency surgical operation (15.31 ± 22.89 vs. 51.72 ± 40.47 min, p = 0.002) was shorter in the COVID-19 period than in the pre-COVID-19 period. Conclusions: Although fewer acute stroke patients sought medical care in this designated COVID-19 hospital during the COVID-19 pandemic, this type of hospital was more efficient for timely treatment of acute stroke. Recognizing how acute strokes presented in designated COVID-19 hospitals will contribute to appropriate adjustments in strategy for dealing with acute stroke during COVID-19 and future pandemics.

The self-assembled nanoparticle-based trimeric RBD mRNA vaccine elicits robust and durable protective immunity against SARS-CoV-2 in mice.

As COVID-19 continues to spread rapidly worldwide and variants continue to emerge, the development and deployment of safe and effective vaccines are urgently needed. Here, we developed an mRNA vaccine based on the trimeric receptor-binding domain (RBD) of the SARS-CoV-2 spike (S) protein fused to ferritin-formed nanoparticles (TF-RBD). Compared to the trimeric form of the RBD mRNA vaccine (T-RBD), TF-RBD delivered intramuscularly elicited robust and durable humoral immunity as well as a Th1-biased cellular response. After further challenge with live SARS-CoV-2, immunization with a two-shot low-dose regimen of TF-RBD provided adequate protection in hACE2-transduced mice. In addition, the mRNA template of TF-RBD was easily and quickly engineered into a variant vaccine to address SARS-CoV-2 mutations. The TF-RBD multivalent vaccine produced broad-spectrum neutralizing antibodies against Alpha (B.1.1.7) and Beta (B.1.351) variants. This mRNA vaccine based on the encoded self-assembled nanoparticle-based trimer RBD provides a reference for the design of mRNA vaccines targeting SARS-CoV-2.

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.

A recombinant receptor-binding domain in trimeric form generates protective immunity against SARS-CoV-2 infection in nonhuman primates.

A safe and effective vaccine is critical to combat the COVID-19 pandemic. Here, we developed a trimeric SARS-CoV-2 receptor-binding domain (RBD) subunit vaccine candidate that simulates the natural structure of the spike (S) trimer glycoprotein. Immunization with the RBD trimer-induced robust humoral and cellular immune responses, and a high level of neutralizing antibodies was maintained for at least 4.5 months. Moreover, the antibodies that were produced in response to the vaccine effectively cross-neutralized the SARS-CoV-2 501Y.V2 variant (B.1.351). Of note, when the vaccine-induced antibodies dropped to a sufficiently low level, only one boost quickly activated the anamnestic immune response, conferring full protection against a SARS-CoV-2 challenge in rhesus macaques without typical histopathological changes in the lung tissues. These results demonstrated that the SARS-CoV-2 RBD trimer vaccine candidate is highly immunogenic and safe, providing long-lasting, broad, and significant immunity protection in nonhuman primates, thereby offering an optimal vaccination strategy against COVID-19.

CT changes of severe coronavirus disease 2019 based on prognosis.

This study aimed to determine the characteristics of CT changes in patients with severe coronavirus disease 2019 (COVID-19) based on prognosis. Serial CT scans in 47 patients with severe COVID-19 were reviewed. The patterns, distribution and CT score of lung abnormalities were assessed. Scans were classified according to duration in weeks after onset of symptoms. These CT abnormalities were compared between discharged and dead patients. Twenty-six patients were discharged, whereas 21 passed away. Discharged patients were characterized by a rapid rise in CT score in the first 2 weeks followed by a slow decline, presence of reticular and mixed patterns from the second week, and prevalence of subpleural distribution of opacities in all weeks. In contrast, dead patients were characterized by a progressive rise in CT score, persistence of ground-glass opacity and consolidation patterns in all weeks, and prevalence of diffuse distribution from the second week. CT scores of death group were significantly higher than those of discharge group (P < 0.05). The CT changes differed between the discharged and dead patients. An understanding of these differences can be of clinical significance in the assessment of the prognosis of severe COVID-19 patients.

A Case Report of a Patient with Severe Type of Coronavirus Disease 2019 (COVID-19) Treated by Hyperbaric Oxygen: CT Dynamic Changes

Coronavirus disease 2019 (COVID-19) is currentlya pandemic affecting the world, and there is no specific therapeutic drug available. Hypoxemia is a common clinical manifestation of severe patients, and oxygen therapy is the main treatment. This article reports for the first time the computed tomography (CT) imaging dynamic changes in a case of 69-year-old male with severe type of COVID-19 treated by hyperbaric oxygen, which provides a more effective regimen for severe COVID-19 patients with hypoxemia. We emphasize that hyperbaric oxygen treatment for COVID-19 is a preliminary attempt, and more clinical studies are required to confirm its efficacy.