The impact of traffic control measures on the spread of COVID-19 within urban agglomerations based on a modified epidemic model.

With the spatial structure of urban agglomerations, well-developed transportation networks and close economic ties can increase the risk of intercity transmission of infectious diseases. To reveal the epidemic transmission mechanism in urban agglomerations and to explore the effectiveness of traffic control measures, this study proposes an Urban-Agglomeration-based Epidemic and Mobility Model (UAEMM) based on the reality of urban transportation networks and population mobility factors. Since the model considers the urban population inflow, along with the active intracity population, it can be used to estimate the composition of urban cases. The model was applied to the Chang-Zhu-Tan urban agglomeration, and the results show that the model can better simulate the transmission process of the urban agglomeration for a certain scale of epidemic. The number of cases within the urban agglomeration is higher than the number of cases imported into the urban agglomeration from external cities. The composition of cases in the core cities of the urban agglomeration changes with the adjustment of prevention and control measures. In contrast, the number of cases imported into the secondary cities is consistently greater than the number of cases transmitted within the cities. A traffic control measures discount factor is introduced to simulate the development of the epidemic in the urban agglomeration under the traffic control measures of the first-level response to major public health emergency, traffic blockades in infected areas, and public transportation shutdowns. If none of those traffic control measures had been taken after the outbreak of COVID-19, the number of cases in the urban agglomeration would theoretically have increased to 3879, which is 11.61 times the actual number of cases that occurred. If only one traffic control measure had been used alone, each of the three measures would have reduced the number of cases in the urban agglomeration to 30.19 %-57.44 % of the theoretical values of infection cases, with the best blocking effect coming from the first-level response to major public health emergency. Traffic control measures have a significant effect in interrupting the spread of COVID-19 in urban agglomerations. The methodology and main findings presented in this paper are of general interest and can also be used in studies in other countries for similar purposes to help understand the spread of COVID-19 in urban agglomerations.

Effects of physical exercise intervention on depressive and anxious moods of college students: A meta-analysis of randomized controlled trials

Background Due to the limitation of drug treatment and other adverse reactions, many psychological treatments always adopt rehabilitation training or non-drug intervention methods, while physical exercise is considered as an auxiliary way. A mass of literature has verified the therapeutic benefits of physical exercise to reduce depression and anxiety in clinical populations. However, little attention is paid to the mental health benefits of exercise for non-clinical populations. The purpose of this meta-analysis is to systematically aggregate and quantify findings of the effect of physical exercise on depression and anxiety in non-clinical populations, through which to evaluate whether physical exercise intervention as a non-drug means can effectively improve the depressive and anxious moods of college students. Significance This paper combines sport and psychotherapy and links kinesiology and psychology, which can deepen readers' understanding and stimulate their interest in the practice of sport and exercise psychology. The 2019 novel coronavirus disease (COVID-19) has swept the world, causing a global epidemic with serious physical and psychological consequences, and this study may help policymakers and health care professionals to make effective recommendations for psychological interventions for college students. Methods The study was based on five electronic databases: CNKI, Wan Fang Data, SinoMed, PubMed, and Web of Science. The quality of the selected articles was evaluated by the PEDro scale. The Meta-Analysis was performed using R-4.0.4, which computed pooled estimates of effect size and respective 95% confidence intervals (95% CI) for intervention. Bias and sensitivity analyses were calculated to explore the source of heterogeneity, and subgroup analyses were performed according to time, frequency, and event. Results Synthesizing all the trials, the results show that the study heterogeneity of physical exercise on the improvement of depressive mood in college students is relatively high (I2=63%, P<0.01), which has a medium effect (SMD=-0.63, 95% confidence interval=-0.80 to -0.46). The results reveal low heterogeneity in anxious mood (I2=36%, P = 0.04), with a medium effect (SMD=-0.58, 95% confidence interval=-0.71 to -0.44). Conclusion The Meta-Analysis confirms the effective and positive role of physical exercise in reducing depressive and anxious moods of college students. Physical exercise can be used as a non-medical method to improve the mental health state of college students and promote full development. Further research should evaluate the impact of various sports and specific exercise prescriptions on college students' negative emotions, so as to apply them to complementary and alternative therapies.

Role of tannic acid against SARS-cov-2 cell entry by targeting the interface region between S-protein-RBD and human ACE2

Coronavirus disease 2019 (COVID-19) was caused by a new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 utilizes human angiotensin converting enzyme 2 (hACE2) as the cellular receptor of its spike glycoprotein (SP) to gain entry into cells. Consequently, we focused on the potential of repurposing clinically available drugs to block the binding of SARS-CoV-2 to hACE2 by utilizing a novel artificial-intelligence drug screening approach. Based on the structure of S-RBD and hACE2, the pharmacophore of SARS-CoV-2-receptor-binding-domain (S-RBD) -hACE2 interface was generated and used to screen a library of FDA-approved drugs. A total of 20 drugs were retrieved as S-RBD-hACE2 inhibitors, of which 16 drugs were identified to bind to S-RBD or hACE2. Notably, tannic acid was validated to interfere with the binding of S-RBD to hACE2, thereby inhibited pseudotyped SARS-CoV-2 entry. Experiments involving competitive inhibition revealed that tannic acid competes with S-RBD and hACE2, whereas molecular docking proved that tannic acid interacts with the essential residues of S-RBD and hACE2. Based on the known antiviral activity and our findings, tannic acid might serve as a promising candidate for preventing and treating SARS-CoV-2 infection.

How Effective Is a Traffic Control Policy in Blocking the Spread of COVID-19? A Case Study of Changsha, China.

(1) Background: COVID-19 is still affecting people's daily lives. In the past two years of epidemic control, a traffic control policy has been an important way to block the spread of the epidemic. (2) Objectives: To delve into the blocking effects of different traffic control policies on COVID-19 transmission. (3) Methods: Based on the classical SIR model, this paper designs and improves the coefficient of the infectious rate, and it builds a quantitative SEIR model that considers the infectivity of the exposed for traffic control policies. Taking Changsha, a typical city of epidemic prevention and control, as a study case, this paper simulates the epidemic trends under three traffic control policies adopted in Changsha: home quarantine, road traffic control, and public transport suspension. Meanwhile, to explore the time sensitivity of all traffic control policies, this paper sets four distinct scenarios where the traffic control policies were implemented at the first medical case, delayed by 3, 5, and 7 days, respectively. (4) Results: The implementation of the traffic control policies has decreased the peak value of the population of the infective in Changsha by 66.03%, and it has delayed the peak period by 58 days; with the home-quarantine policy, the road traffic control policy, and the public transport suspension policy decreasing the peak value of the population of the infective by 56.81%, 39.72%, and 45.31% and delaying the peak period by 31, 18, and 21 days, respectively; in the four scenarios where the traffic control policies had been implemented at the first medical case, delayed by 3, 5, and 7 days, respectively, the variations of both the peak value and the peak period timespan of confirmed cases under the home-quarantine policy would have been greater than under the road traffic control and the public transport suspension policies. (5) Conclusions: The implementation of traffic control policies is significantly effective in blocking the epidemic across the city of Changsha. The home-quarantine policy has the highest time sensitivity: the earlier this policy is implemented, the more significant its blocking effect on the spread of the epidemic.

Mapping the Trophic State Index of Eastern Lakes in China using an Empirical Model and Sentinel-2 Imagery Data

Lake eutrophication has become a critical environmental issue due to the global effects of anthropogenic activities and climate change, and has been comprehensively studied for many years. A series of models and indicators have been proposed to assess the trophic state of lakes. The trophic state index (TSI) is a synthetic index that integrates chlorophyll-a, water clarity, and total phosphorus and is widely used to evaluate the trophic state of aquatic environments. In this study, we collected in situ lake samples (N=431) from typical lakes to match Sentinel-2 MultiSpectral Instrument (MSI) imagery data using the Case 2 Regional Coast Color processor. Then we developed a new empirical model, TSI = –34.04 × (band 4/band 5) – 1.114 × (band 1/band 4) + 97.376). This model is valid for all of China, with good performance and few errors (RMSE=7.36;MAE=6.25) for the validation dataset. Recognizing that over 94% of the Chinese population located along eastern watersheds and large lakes have competing water uses, and given the TSI model on the seasonal scales, we further estimated the mean TSI and trophic state in eastern Chinese lakes (> 100 km2) from 2019 to 2020. The results revealed that more lakes were eutrophic in autumn (94.28%) than in spring (> 77.14%), indicating a serious eutrophication of eastern lakes. Although the eastern lakes have been studied in more detail, this study found that eutrophication still has markedly negative impacts on lake ecosystems. In addition, no significant improvement was observed in spring, most likely due to the months of curfew/lockdown from January 2020 onwards due to COVID-19. This may be due to the enrichment of nutrients deposited in sediment or watershed soil, which can be characterized as “autochthonous sources” of lake eutrophication, over decades with high rates of economic development. This study demonstrates the applicability of Sentinel-2 MSI data to monitor lake eutrophication as well as the feasibility of blue/red and red/red edge combinations. The framework and TSI model used bands available on MSI sensors to develop a novel approach for generating historical eutrophication data for large-scale evaluation of and decision-making related aquatic environmental changes, even in poorly studied areas.

Mental Health Among Informal Caregivers of Older Adults in Asia

The purpose of this symposium is to highlight the mental health needs and factors associated with mental health among informal caregivers of older adults in Asia. The symposium consists of five papers. The first paper explores the perceived role, needs, and rewards of informal caregiving among caregivers of residents in independent long-term care facilities in South India. The second paper presents a systematic review and meta-analysis on the association between long-term care service use and informal caregiver burden, depression, and health status. The third paper examines the association between caregivers’ characteristics and quality of life among informal caregivers of older adults with cognitive impairment in China. The fourth paper examines the association between coping strategies and caregiver burden and depression among Chinese caregivers of older adults with cognitive impairment. The last paper examines the association between cohort, meaning making, and depression among adult caregivers during the COVID-19 pandemic in Hong Kong. Taken together, these five papers underscore of the mental health needs and protective and risk factors of mental well-being among caregivers in Asia. Findings of those papers inform the development and adaptation of culturally sensitive interventions to improve mental health outcomes among informal caregivers in Asia. The disccuant will comment on the strengths and limitations of these papers in terms of their contributions to the theory, research, and practice on mental health among informal caregivers in Asia.

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence (preprint)

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Pediatric asthma control during the COVID-19 pandemic: a systematic review and meta-analysis (preprint)

Background: During the current ongoing COVID-19 pandemic, studies had reported that patients with asthma would experience increased asthma-associated morbidity because of the respiratory virus SARS-CoV-2 infection, based on experience with other respiratory viral infections. However, some studies suggested that there was no apparent increase in asthma related morbidity in children with asthma, it is even possible that due to reduced exposures due to confinement, such children may have improved outcomes. In order to understand the impact of Covid-19 on asthma control in children, we performed this systematic review and meta-analysis. Methods: We searched PubMed, Embase, and Cochrane Library to find literature from December 2019 to June 2021 related to Covid-19 and children’s asthma control, among which results such as abstracts, comments, letters, reviews and case reports were excluded. The level of asthma control during the COVID-19 pandemic was synthesized and discussed. Results: A total of 20456 subjects were included in 7 studies. Random effect model is used to account for the data. Compared to the same period before the COVID-19 pandemic, asthma exacerbation, asthma admission, emergency room visit reduced a lot. The outcome of use of inhaled corticosteroids and Beta-2 agonists shows no significant difference. Conclusion: Compared to the same period before the COVID-19 pandemic and the measures in response to it, the level of asthma control has been significantly improved. We need to understand the exact factors leading to these improvements and find methods to sustain it.

Biomimetic Design and Mass Production of Sustainable Multiscale Cellulose Fibers‐Based Hierarchical Filter Materials for Protective Clothing

As a core part of personal protective equipment (PPE), filter materials play a key role in individual protection, especially in the fight against the COVID‐19. Here, a high‐performance multiscale cellulose fibers‐based filter material is introduced for protective clothing, which overcomes the limitation of mutual exclusion of filtration and permeability in cellulose‐based filter materials. With the hierarchical biomimetic structure design and the active surface of multiscale cellulose fibers, high PM2.5 removal efficiency of ≈92% is achieved with the high moisture transmission rate of 8 kg m−2 d−1. Through a simple and effective dip‐coating and roll‐to‐roll process, the hierarchical filter materials can be made on a large scale and further fabricated into high‐quality protective clothing by industrial production equipment. [ABSTRACT FROM AUTHOR] Copyright of Advanced Materials Technologies is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Neutralizing and binding antibody kinetics of COVID-19 patients during hospital and convalescent phases (preprint)

Background: Knowledge of host immune response after natural SARS-CoV-2 infection is essential for the direction of vaccination and epidemiological control strategies against COVID-19. Methods: : Thirty-four COVID-19 patients were enrolled with 244 serial blood specimens (38.1% after hospital discharge) collected to explore the chronological evolution of neutralizing (NAb), total (TAb), IgM, IgG and IgA antibody in parallel. Results: : IgG titers reached a peak later (35 days postonset) than those of Nab, Ab, IgM and IgA (25 days postonset). IgM levels declined with an estimated half-life of 35 days postonset, which was more rapid than those of IgA and IgG (73-76 days postonset). All patients remained positive for NAb, IgG and IgA up to 3 months after illness onset. The relative contribution of IgM to NAb was higher than that of IgG (standardized β regression coefficient: 0.53 vs 0.48). However, the relative contribution of IgG to NAb increased and that of IgM further decreased after 6 weeks postonset. Conclusions: : This study suggests that SARS-CoV-2 infection induces robust neutralizing and binding antibody responses in patients. Humoral immunity against SARS-CoV-2 acquired by infection may persist for a relatively long time.

An Extended COVID-19 Epidemiological Model with Vaccination and Multiple Interventions for Controlling COVID-19 Outbreaks in the UK (preprint)

ABSTRACT For controlling the first wave of the UK COVID-19 pandemic in 2020, a plethora of hypothetical COVID-19 models has been developed for simulating how diseases spread under different non-pharmaceutical interventions like suppression and mitigation and providing useful guidance to UK policymakers. While many models demonstrate their effectiveness on predicting and controlling the spread of COVID-19, they rarely consider consequence of incorporating the effects of potential SARS-CoV-2 variants and implementing vaccine interventions in large-scale. By December 2020, the second wave in the UK appeared to be much more aggressive with many more cases as one potentially more contagious SARS-CoV-2 variant was detected in the UK since September 2020. Meanwhile, UK has begun their first mass vaccination campaign on 8 December 2020, where three vaccines were in use including Pfizer, BioNTech and Moderna. Thus, these new issues pose an emergent need to build up advanced models for accessing effectiveness of taking both vaccination and multiple interventions for controlling COVID-19 outbreaks and balancing healthcare demands. Targeting at this problem, we conducted a feasibility study by defining a new mathematical model SEMCVRD (Susceptible [S], Exposed [E] (infected but asymptomatic), Mild [M] and Critical [C] (mild cases, severe and critical cases), [V] (vaccinated), Recovered [R] and Deceased [D]), containing two importantly new features: the combined infection of the mutant strain and the original strain and the addition of a new group who have been vaccinated. The model was fitted and evaluated with a public COVID-19 dataset including daily new infections, new deaths and daily vaccination in the UK from February 2020 to February 2021. Based on the simulation results, 1) we find under the assumption that the vaccine is equivalently effective against both the original strain and new variants of COVID-19, if the UK government implements insensitive suppression intervention for 13 weeks, COVID-19 epidemic will be controlled by the first week of April 2021 and nearly ended by the first week of May 2021. It shows that taking both vaccine and suppression interventions can effectively inhibit the spread and infection of the new mutant virus. 2) we suggest implementing a 3-weeks phased and progressive lifting intervention strategy up to a low intensity mitigation level for effectively controlling COVID-19 outbreaks in the UK. By implementing this strategy, the total number of infections in the UK will be limited to 4.2 million and the total number of deaths in the UK is 135 thousand, by the end of June 2021. The epidemic will nearly end in the early of June 2021, and the UK will not experience a shortage of medical resources. 3) On the assumption that UK has a capability of providing 600 thousand vaccinations every day, a 3-weeks phased and progressive lifting intervention strategy up to a moderate intensity mitigation level can end the epidemic by the end of May 2021. This strategy would reduce the overall infections and deaths of COVID-19 outbreaks, and balance healthcare demand in the UK.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US (preprint)

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naive baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. f

Modeling and Prediction of the Transmission Dynamics of COVID-19 Based on the SINDy-LM Method (preprint)

The transmission dynamics of COVID-19 is investigated in this study. A SINDy-LM modeling method that can effectively balance model complexity and prediction accuracy is proposed based on data-driven technique. First, the Sparse Identification of Nonlinear Dynamical systems (SINDy) method is used to discover and describe the nonlinear functional relationship between the dynamic terms in the model in accordance with the observation data of the COVID-19 epidemic. Moreover, the Levenberg–Marquardt (LM) algorithm is utilized to optimize the obtained model for improving the accuracy of the SINDy algorithm. Second, the obtained model, which is consistent with the logistic model in mathematical form with small errors and high robustness, is leveraged to review the epidemic situation in China. Otherwise, the evolution of the epidemic in Australia and Egypt is predicted, which demonstrates that this method has universality for constructing the global COVID-19 model. The proposed model is also compared with the extreme learning machine (ELM), which shows that the prediction accuracy of the SINDy-LM method outperforms that of the ELM method and the generated model has higher sparsity.

Maximum chest CT score is associated with progression to severe illness in patients with COVID-19: A retrospective study from Wuhan, China (preprint)

Background: The Coronavirus Disease 2019 (COVID-19) pandemic is a world-wide health crisis. Limited information is available regarding which patients will experience more severe disease symptoms. We evaluated hospitalized patients who were initially diagnosed with moderate COVID-19 for clinical parameters and radiological feature that showed an association with progression to severe/critical symptoms. Methods: : This study, a retrospective single-center study at the Central Hospital of Wuhan, enrolled 243 patients with confirmed COVID­19 pneumonia. Forty of these patients progressed from moderate to severe/critical symptoms during follow up. Demographic, clinical, laboratory, and radiological data were extracted from electronic medical records and compared between moderate- and severe/critical-type symptoms. Univariable and multivariable logistic regressions were used to identify the risk factors associated with symptom progression. Results: : Patients with severe/critical symptoms were older (p<0.001) and more often male (p=0.046). A combination of chronic obstructive pulmonary disease (COPD) and high maximum chest computed tomography (CT) score was associated with disease progression. Maximum CT score (>11) had the greatest predictive value for disease progression. The area under the receiver operating characteristic curve was 0.861 ( 95% confidence interval: 0.811-0.902). Conclusions: : Maximum CT score and COPD were associated with patient deterioration. Maximum CT score (>11) was associated with severe illness.

Inter-Series Attention Model for COVID-19 Forecasting (preprint)

COVID-19 pandemic has an unprecedented impact all over the world since early 2020. During this public health crisis, reliable forecasting of the disease becomes critical for resource allocation and administrative planning. The results from compartmental models such as SIR and SEIR are popularly referred by CDC and news media. With more and more COVID-19 data becoming available, we examine the following question: Can a direct data-driven approach without modeling the disease spreading dynamics outperform the well referred compartmental models and their variants? In this paper, we show the possibility. It is observed that as COVID-19 spreads at different speed and scale in different geographic regions, it is highly likely that similar progression patterns are shared among these regions within different time periods. This intuition lead us to develop a new neural forecasting model, called Attention Crossing Time Series (\textbf{ACTS}), that makes forecasts via comparing patterns across time series obtained from multiple regions. The attention mechanism originally developed for natural language processing can be leveraged and generalized to materialize this idea. Among 13 out of 18 testings including forecasting newly confirmed cases, hospitalizations and deaths, \textbf{ACTS} outperforms all the leading COVID-19 forecasters highlighted by CDC.

Radiologic Risk Factors for Mortality of Patients with COVID-19 Pneumonia in Wuhan, China: A Retrospective Study (preprint)

Background: Computed tomography (CT) characteristics associated with critical outcomes of patients with coronavirus disease 2019 (COVID-19) have been reported. However, CT risk factors for mortality are poorly understood. We aimed to investigate the automatically quantified CT imaging predictors for COVID-19 mortality.Methods: In this retrospective study, laboratory-confirmed COVID-19 patients at Wuhan Central Hospital between December 9, 2019, and March 19, 2020, were included. A novel prognostic biomarker, V-HU score, depicting the volume of total pneumonia infection and the average Hounsfield unit (HU) value of consolidation areas was quantified from CT by an artificial intelligence (AI) system. Cox proportional hazards models were used to investigate risk factors for mortality.Findings: This study included 238 patients (126 survivors and 112 non-survivors). The V-HU marker was an independent predictor (hazard ratio [HR] 2·78, 95% CI 1·50-5·17; p=0·0012) after adjusting for several COVID-19 prognostic indicators significant in univariable analysis. The prognostic performance of the model containing clinical and outpatient laboratory factors was improved by integrating the V-HU marker (c-index: 0·695 versus 0·728; p<0·0001). Older patients (age>=65 years; HR 3·56, 95% CI 1·64-7·71; p=0·0006) and younger patients (age<65 years; HR 4·60, 95% CI 1·92-10·99; p<0·0001) could be risk-stratified by the V-HU marker.Interpretation: A combination of an increased volume of total pneumonia infection and high HU value of consolidation areas showed a strong correlation to COVID-19 mortality, as determined by AI quantified CT. The novel radiologic marker may be used for early risk assessment to prioritize critical care resources for patients at a high risk of mortality.Funding: None.Declaration of Interests: The authors declare no competing interests.Ethics Approval Statement: The study was approved by the Research Ethics Commission of Wuhan Central Hospital, and the requirement for writing informed consent was waived by the Ethics Commission for the emergence of infectious diseases.

Neutralizing and binding antibody kinetics of COVID-19 patients during hospital and convalescent phases (preprint)

Knowledge of the host immune response after natural SARS-CoV-2 infection is essential for informing directions of vaccination and epidemiological control strategies against COVID-19. In this study, thirty-four COVID-19 patients were enrolled with 244 serial blood specimens (38.1% after hospital discharge) collected to explore the chronological evolution of neutralizing (NAb), total (TAb), IgM, IgG and IgA antibody in parallel. IgG titers reached a peak later (approximately 35 days postonset) than those of Nab, Ab, IgM and IgA (20~25 days postonset). After peaking, IgM levels declined with an estimated average half-life of 10.36 days, which was more rapid than those of IgA (51.25 days) and IgG (177.39 days). Based on these half-life data, we estimate that the median times for IgM, IgA and IgG to become seronegative are 4.59 (IQR 4.12-5.03), 7.78 (IQR 6.71-9.16) and 42.72 (IQR 33.75-47.96) months post disease onset. The relative contribution of IgM to NAb was higher than that of IgG (standardized {beta} regression coefficient: 0.53 vs 0.48), so the rapid decline in NAb may be attributed to the rapid decay of IgM in acute phase. However, the relative contribution of IgG to NAb increased and that of IgM further decreased after 6 weeks postonset. It's assumed that the decline rate of NAb might slow down to the same level as that of IgG over time. This study suggests that SARS-CoV-2 infection induces robust neutralizing and binding antibody responses in patients and that humoral immunity against SARS-CoV-2 acquired by infection may persist for a relatively long time.