Quantitatively assessing early detection strategies for mitigating COVID-19 and future pandemics (preprint)

Researchers and policymakers have proposed systems to detect novel pathogens early by monitoring samples from hospital patients, wastewater, and air travel, in order to mitigate future pandemics. How much benefit would such systems offer? We developed, empirically validated, and mathematically characterized a quantitative model that simulates disease spread and detection time for any given disease and detection system. We find that hospital monitoring could have detected COVID-19 in Wuhan 0.4 weeks earlier than it was actually discovered, at 2,300 cases compared to 3,400. Wastewater monitoring would not have accelerated COVID-19 detection in Wuhan, but provides benefit in smaller catchments and for asymptomatic or long-incubation diseases like polio or HIV/AIDS. Monitoring of air travel provides little benefit in most scenarios we evaluated. In sum, early detection systems can substantially mitigate some future pandemics, but would not have changed the course of COVID-19.

TREM-1, TREM-2 and their association with disease severity in patients with COVID-19 (preprint)

Background Delayed diagnosis and inadequate treatment caused by limited biomarkers were associated with outcomes of COVID-19 patients. It is necessary to find other promising biomarkers and candidate targets for defining dysregulated inflammatory state besides the typical biomarkers and drug targets have been used clinically.Methods In a cohort of hospitalized COVID-19 patients with varying degrees of illness severity, we characterized TREM-1 and TREM-2 expression in plasma and on the surface of cell subpopulations using ELISA and flow cytometry, respectively. And their correlations with disease severity and contrast with main clinical indicators were evaluated.Results We found the increased expression of soluble TREM-1 and TREM-2 in plasma from COVID-19 patients compared to the control group. Moreover, membrane-bound TREM-1 and TREM-2 expression was also upregulated on the cell surface of circulating blood T cells from COVID-19 patients. Correlation analysis results showed the sTREM-2 level was negatively correlated with PaO2/FiO2, but positively correlated with CRP, PCT and IL-6 level. Receiver operating characteristic (ROC) curves presented that TREM-1 and TREM-2 exhibited strong predictive abilities, and their expression was equal to CRP and IL-6, and better than leukocytes or neutrophil absolute count and PCT in distinguishing disease severity.Conclusion These results highlighted the important role of TREM-1 and TREM-2 in viral infection. TREM-2 and TREM-1 were critical host immune factors in response to SARS-COV-2 infection and could serve as potential diagnostic and therapeutic biomarkers of COVID-19.

Effects of Dust Event Days on Influenza: Evidence from Arid Environments in Lanzhou

Airborne aerosol is believed to be an important pathway for infectious disease transmissions like COVID-19 and influenza. However, the effects of dust event days on influenza have been rarely explored, particularly in arid environments. This study explores the effects of ambient particulate matter (PM) and dust events on laboratory-confirmed influenza in a semi-arid city. A descriptive analysis of daily laboratory-confirmed influenza (influenza) cases, PM (PM10 and PM2.5), meteorological parameters, and dust events were conducted from 2014 to 2019 in Lanzhou, China. The case-crossover design combined with conditional Poisson regression models was used to estimate the lagging effects of PM and dust events on influenza. In addition, a hierarchical model was used to quantitatively evaluate the interactive effect of PM with ambient temperature and absolute humidity on influenza. We found that PM and dust events had a significant effect on influenza. The effects of PM10 and PM2.5 on influenza became stronger as the cumulative lag days increased. The greatest estimated relative risks (RRs) were 1.018 (1.011,1.024) and 1.061 (1.034,1.087), respectively. Compared with the non-dust days, the effects of dust events with duration ≥ 1 day and with duration ≥ 2 days on influenza were the strongest at lag0 day, with the estimated RRs of 1.245 (95% CI: 1.061–1.463) and 1.483 (95% CI: 1.232–1.784), respectively. Subgroup analysis showed that pre-school children and school-aged children were more sensitive to PM and dust events exposure. Besides, we also found that low humidity and temperature had an interaction with PM to aggravate the risk of influenza. In summary, ambient PM and dust events exposure may increase the risk of influenza, and the risk of influenza increases with the dust events duration. Therefore, more efforts from the government as well as individuals should be strengthened to reduce the effect of PM on influenza, particularly in cold and dry weather.

Evaluating the Effect of SARS-CoV-2 Spike Mutations by Causal Inference (preprint)

Driven by various mutations on the viral Spike protein, diverse variants of SARS-CoV-2 have emerged and prevailed repeatedly, which necessitates the identification of key Spike mutations for fitness enhancement. To address the need, this manuscript formulates a principled framework of causal inference for evaluating Spike mutations. In the context of large-scale genomes of SARS-CoV-2, it estimates the contribution of mutations to viral fitness across lineages and validates mutational effects on the Spike stability, receptor-binding affinity, and potential for immune escape. Key fitness-enhancing mutations and protein regions are recognized and studied. The transmission capacity of any new variant possessing these mutations can be predicted based on our model, solely based on the viral sequence. This research produces an innovative and systematic insight into SARS-CoV-2 and promotes functional studies of its key mutations.

Spike-specific CXCR3+ TFH cells play a dominant functional role in supporting antibody responses in SARS-CoV-2 infection and vaccination (preprint)

CD4+ T follicular helper (TFH) cells are required for high-quality antibody generation and maintenance. However, the longevity and functional role of these cells are poorly defined in COVID-19 convalescents and vaccine recipients. Here, we longitudinally investigated the dynamics and functional roles of spike-specific circulating TFH cells and their subsets in convalescents at the 2nd, 5th, 8th, 12th and 24th months after COVID-19 symptom onset and in vaccinees after two and three doses of inactivated vaccine. SARS-CoV-2 infection elicited robust spike-specific TFH cell and antibody responses, of which spike-specific CXCR3+ TFH cells but not spike-specific CXCR3- TFH cells and neutralizing antibodies were persistent for at least two years in more than 80% of convalescents who experienced symptomatic COVID-19, which was well coordinated between spike-specific TFH cell and antibody responses at the 5th month after infection. Inactivated vaccine immunization also induced spike-specific TFH cell and antibody responses; however, these responses rapidly declined after six months with a two-dose standard administration, and a third dose significantly promoted antibody maturation and potency. Functionally, spike-specific CXCR3+ TFH cells exhibited better responsiveness than spike-specific CXCR3- TFH cells upon spike protein stimulation in vitro and showed superior capacity in supporting spike-specific antibody secreting cell (ASC) differentiation and antibody production than spike-specific CXCR3- TFH cells cocultured with autologous memory B cells. In conclusion, spike-specific CXCR3+ TFH cells played a dominant functional role in antibody elicitation and maintenance in SARS-CoV-2 infection and vaccination, suggesting that induction of CXCR3-biased spike-specific TFH cell differentiation will benefit SARS-CoV-2 vaccine development aiming to induce long-term protective immune memory.

A novel intranasal administration adenoviral vector-based platform for rapid COVID-19 vaccine development (preprint)

The coronavirus SARS-CoV-2 has a severe impact on global public health, and the emerging variants threaten the efficacy of the circulating vaccines. Here, we report that a single vaccination with a non-replicated Chimpanzee adenovirus-based vaccine against the SARS-CoV-2 B.1.617.2 variant (JS1-delta) elicits potent humoral, cellular and mucosal immunity in mice. Additionally, a single intranasal administration of JS1- delta provides sufficient protection against B.1.617.2 challenge in mice. This study indicates that Chimpanzee adenovirus type 3 (ChAd3) derived vector represents a promising platform for antiviral vaccine development against respiratory infections, and that JS1-delta is worth further investigation in human clinical trials.

BEV-Net: Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning (preprint)

Social distancing, an essential public health measure to limit the spread of contagious diseases, has gained significant attention since the outbreak of the COVID-19 pandemic. In this work, the problem of visual social distancing compliance assessment in busy public areas, with wide field-of-view cameras, is considered. A dataset of crowd scenes with people annotations under a bird's eye view (BEV) and ground truth for metric distances is introduced, and several measures for the evaluation of social distance detection systems are proposed. A multi-branch network, BEV-Net, is proposed to localize individuals in world coordinates and identify high-risk regions where social distancing is violated. BEV-Net combines detection of head and feet locations, camera pose estimation, a differentiable homography module to map image into BEV coordinates, and geometric reasoning to produce a BEV map of the people locations in the scene. Experiments on complex crowded scenes demonstrate the power of the approach and show superior performance over baselines derived from methods in the literature. Applications of interest for public health decision makers are finally discussed. Datasets, code and pretrained models are publicly available at GitHub.

Effect of The “Normalized Epidemic Prevention And Control Requirements” On Hospital-Acquired And Community-Acquired Infections In China (preprint)

Background: No studies have yet reported the effect of prevention and control measures, which were implemented to combat COVID-19, on the prevention and control of common HAIs. We aimed to examine the effect of the “Normalized Epidemic Prevention and Control Requirements” (implemented in May 2020) by comparison of hospital-acquired infections (HAIs) and community-acquired infections (CAIs) in China during 2018, 2019, and 2020. Methods: : Data of inpatients before and after implementation of new requirements were retrospectively analyzed, including infection rate, use of alcohol-based hand cleaner, anatomical sites of infections, pathogen species, infection by multi-drug-resistant species, use of different antibiotics, and antibiotic use density. Results: : The HAI rate was significantly higher in 2020 than in 2018 and 2019 ( P< 0.05), and the CAI rate was significantly higher in 2019 and 2020 than in 2018 ( P <0.001). Lower respiratory tract infections were the most common HAI during all years, with no significant changes over time. Lower respiratory tract infections were also the most common CAI, but were significantly more common in 2018 and 2019 than 2020 ( P <0.001). There were no changes in upper respiratory tract infections among HAIs or CAIs. Most HAIs and CAIs were from Gram-negative bacteria, and the percentages of fungal infections were greater in 2019 and 2020 than 2018. MRSA infections were more common in 2020 than in 2018 and 2019 ( P< 0.05). The utilization rate and usage days of antibiotics decreased over time ( P <0.001), the culture rate of microbial specimens before antibiotics usage increased over time ( P <0.001), but antibiotic use density remained steady over time. Conclusions: : The new prevention and control requirements provided important benefits during the COVID-19 pandemic. However, their effects on HAIs were not obvious.

A comprehensive survey of bat sarbecoviruses across China for the origin tracing of SARS-CoV and SARS-CoV-2 (preprint)

Severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2 have been thought to originate from bat, but whether the cross-species transmission occurred directly from bat to human or through an intermediate host remains elusive. In this study, we performed CoV screening of 102 samples collected from animal-selling stalls of Wuhan Huanan Market (WHM) and pharyngeal and anal swabs from13,064 bats collected at 703 locations across China, covering almost all known southern hotspots for sarbecovirus, between 2016 and 2021. This is the first systematic survey of bat CoV in China during the outbreak of Corona Virus Disease 2019. We found four non-sarbeco CoVs in samples of WHM, and 142 SARS-CoV related CoVs (SARSr-CoV) and 4 recombinant CoVs in bats, of which YN2020B-G share the highest sequence identity with SARS-CoV among all known bat CoVs, suggesting endemic SARSr-CoVs in bats in China. However, we did not find any SARS-CoV-2 related CoVs (SC2r-CoV) in any samples, including specimens collected from the only two domestic places where RaTG13 and RmYN02 were previously reported (the Tongguan caves and the karst caves around the Xishuangbanna Tropical Botanical Garden), indicating that SC2r-CoVs might not actively circulate among bats in China. Phylogenetic analysis showed that there are three different lineages of sarbecoviruses, L1 (SARSr-CoV), L2 (SC2r-CoV), and L-R (a novel CoV lineage from L1 and L2 recombination), in China. Of note, L-R CoVs are only found in R. pusillus. Further macroscopical analysis of the genetic diversity, host specificity for colonization and accidental infection, and geographical characteristics of available CoVs in database revealed the presence of a general geographical distribution pattern for bat sarbecoviruses, with the highest genetic diversity and sequence homology to SARS-CoV or SARS-CoV-2 along the southwest border of China, the least in the northwest of China. Considering the receptor binding motifs for spike gene of sarbecoviruses in Indochina Peninsula show the greatest diversity, our data provide the rationale that extensive surveys in further south and southwest to or of China might be needed for finding closer ancestors of SARS-CoV and SARS-CoV-2.

Nonlinear dynamic mechanism of regional air pollution during the COVID-19 lockdown

Based on hourly concentration of PM2.5 and O3 during the epidemic period（January 24, 2020 to May 31, 2020） in Changsha, Zhuzhou and Xiangtan, the diurnal patterns, long-term persistence, multifractality and self-organization evolution dynamics of these two pollutants were studied to reveal the internal dynamic mechanism of the occurrence and evolution of heavy pollution events during the epidemic period. Firstly, the diurnal patterns of PM2.5 and O3 concentrations were investigated. It showed that O3 showed a single peak of high concentration in the daytime and low in the night, while PM2.5 showed a single lowest peak concentration in the day and high in the night, which was different from the pattern in non-epidemic periods. Furthermore, detrended fluctuation analysis（DFA）, the multifractal detrended fluctuation analysis（MFDFA） and probability statistical analysis were applied to study the long-term persistence, multi-fractal structure of PM2.5 and O3 series. The results showed that PM2.5 and O3 series had significant long-term persistence characteristics and strong multi-fractal structures for the three cities. Meanwhile, detrended cross-correlation analysis（DCCA） and multifractal detrended cross-correlation analysis（MFDCCA） were conducted to estimate the cross-correlations between PM2.5 and O3 series. Long-term persistence as well as multifractal features at different time scales was also observed in PM2.5-O3 cross-correlations. Next, nonlinear analysis results obtained during epidemic period were compared with those obtained in the same periods of non-epidemic years of 2019 and 2018. Finally, based on the self-organized criticality（SOC） theory, the internal dynamic law of spatial and temporal evolution of PM2.5 and O3 series was discussed. Combined with the typical regional meteorological characteristics, it was found that the intrinsic dynamic mechanism of SOC may be one of the leading mechanisms of heavy air pollution episodes during the COVID-19 lockdown period. During the epidemic period, PM2.5 and O3 concentrations did not evolve independently but remained complex interactions. Under the stable meteorological conditions, the nonlinear coupling effect inside the air combined pollution might reach the dynamic critical state, thus, lead to the risk of heavy air pollution in Greater Changsha Metropolitan Region during the epidemic period.

Seven-day COVID-19 quarantine may be too short: assessing post-quarantine transmission risk in four university cohorts (preprint)

Background: Despite rising rates of vaccination, quarantine remains critical to control SARS-CoV-2 transmission. COVID-19 quarantine length around the world varies in part due to the limited amount of empirical data. Objective: To assess post-quarantine transmission risk for various quarantine lengths. Design: Cohort study. Setting: Four US universities, September 2020 to February 2021. Participants: 3,641 students and staff were identified as close contacts to SARS-CoV-2-positive individuals. They entered strict or non-strict quarantine and were tested on average twice per week for SARS-CoV-2. Strict quarantine included designated housing with a private room, private bathroom and meal delivery. Non-strict quarantine potentially included interactions with household members. Measurements: Dates of exposure and last negative and first positive tests during quarantine. Results: Of the 418 quarantined individuals who eventually converted to positive, 11%, 4.2%, and 1.2% were negative and asymptomatic on days 7, 10 and 14, respectively. The US CDC recently shortened its quarantine guidance from 14 to 7 days based on estimates of 2.3-8.6% post-quarantine transmission risk at day 7, significantly below the 11% risk we report here. Notably, 6% of individuals tested positive after day 7 in strict quarantine, versus 14% in non-strict quarantine. Ongoing exposure during quarantine likely explains the higher rate of COVID-19 in non-strict quarantine. Limitations: Quarantine should be longer for individuals using antigen testing, given antigen testing's lower sensitivity than qPCR. Results apply in settings in which SAR-CoV-2 variants do not affect latent period. Conclusions: To maintain the 5% transmission risk that the CDC used in its guidance, our data suggest that quarantine with qPCR testing 1 day before intended release should extend to 10 days for non-strict quarantine.

The COVID-19 Pandemic Accelerated Publication of Medical Articles (preprint)

Background: Experimental biological research and clinical trials that relied on the healthcare system, access to public laboratory equipment, and adequate space were particularly affected by the COVID-19 pandemic and mobility restrictions. The study is to analyze the influence of COVID-19 on medical research by exploring the clinical trials and articles published by medical researchers worldwide during the COVID-19 pandemic. Methods: Routinely conducted, deferred, and terminated clinical trial statistics from the Cancer Center of Tongji Hospital were collected during the COVID-19 pandemic. The number of global medical articles published in 2020 and those from the previous five years were analyzed according to the PubMed database. To conduct subgroup analyses, the articles were classified according to type and research field. Results: The total number of publications per month in 2020 increased compared to the number of articles published in 2019. However, a decrease in the number of clinical trials was noted. Reviews and research papers increased by 7.28% and 20.60%, respectively. The number of clinical trial published decreased by 62.29%. The proportion of cancer-related publications (38.44% vs. 44.79%) decreased, whereas the proportion of immunology, pulmonology, and emergency publications (19.23% vs. 17.48%) increased. In the Cancer Center of the Tongji Hospital in Wuhan during the COVID-19 pandemic, of the 46 clinical trials analyzed, 37 (80.40%) were delayed, 8 (17.40%) were remote, and 1 (2.20%) was routine. Conclusions: The COVID-19 pandemic had promoted the publication of medical research articles especially those related to the immunology, pulmonology, and emergency medicine. It had a pronounced negative impact on the execution and publication of clinical trials.

Observed decreases in on-road CO2 concentrations in Beijing during COVID-19 restrictions

To prevent the spread of the COVID-19 epidemic, restrictions such as “lockdowns” were conducted globally, which led to a significant reduction in fossil fuel emissions, especially in urban areas. However, CO2 concentrations in urban areas are affected by many factors, such as weather, biological sinks and background CO2 fluctuations. Thus, it is difficult to directly observe the CO2 reductions from sparse ground observations. Here, we focus on urban ground transportation emissions, which were dramatically affected by the restrictions, to determine the reduction signals. We conducted six series of on-road CO2 observations in Beijing using mobile platforms before (BC), during (DC) and after (AC) the implementation of COVID-19 restrictions. To reduce the impacts of weather conditions and background fluctuations, we analyze vehicle trips with the most similar weather conditions possible and calculated the enhancement metric, which is the difference between the on-road CO2 concentration and the “urban background” CO2 concentration measured at the tower of the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences. The results showed that the DC CO2 enhancement was decreased by 41 (±1.3) parts per million (ppm) and 26 (±6.2) ppm compared to those for the BC and AC trips, respectively. Detailed analysis showed that, during COVID-19 restrictions, there was no difference between weekdays and weekends during working hours (09:00–17:00 local standard time;LST). The enhancements during rush hours (07:00–09:00 and 17:00–20:00 LST) were almost twice those during working hours, indicating that emissions during rush hours were much higher. For DC and BC, the enhancement reductions during rush hours were much larger than those during working hours. Our findings showed a clear CO2 concentration decrease during COVID-19 restrictions, which is consistent with the CO2 emissions reductions due to the pandemic. The enhancement method used in this study is an effective method to reduce the impacts of weather and background fluctuations. Low-cost sensors, which are inexpensive and convenient, could play an important role in further on-road and other urban observations.

Decoding the RNA viromes in rodent lungs to understand rodent-borne pathogens (preprint)

Most human infectious viral diseases – including COVID-19 and Ebola – originated in animals. As the largest group of mammalian species, rodents are natural reservoirs for many diverse zoonotic viruses. Better understanding the core rodent virome will reduce the risk of future emergence or re-emergence of rodent-borne pathogens. A recent study focused on viruses found in the lungs of rodents in Mainland Southeast Asia, a hotspot for zoonotic emerging infectious diseases. Lung samples were collected from 3,284 rodents and insectivores throughout Thailand, Lao PDR, and Cambodia. Using metatranscriptomics, researchers outlined unique characteristics of the rodent viruses identified. Many mammalian- or arthropod-related viruses from distinct evolutionary lineages were reported for the first time, and viruses related to known pathogens were found. These results expand our understanding of the core virome in rodent species in Mainland Southeast Asia and suggest that a highly diverse array of viruses remains to be found in these species. Viral surveillance in wildlife hosts will minimize the impact of potential wildlife-originating infectious diseases.

Nosocomial infection control strategy in cabin hospital during the outbreak of COVID-19 / 中华医院管理杂志

The construction of mobile Cabin hospitals is a pioneering effort for the prevention and controlling of the patients with mild symptoms during the outbreak epidemic of Novel Coronavirus Pneumonia. It is a prerequisite and important guarantee for the hospital to manage the hospital infection and prevent the spreading of the epidemic. Our team is located in Dongxihu mobile Cabin hospital, which is one of the first three Cabin hospitals in Wuhan. This article takes the operation process of this hospital as a clue, and discusses aspects of personal protection, environmental sanitation management, item management, occupational exposure disposal, and discharged patient management. It also analyzed common and critical problems in operation. With a view to provide reference for other Cabin hospitals or temporary treatment agencies.

Clinical and Deep-Learning Based Quantitative Serial Chest CT Features of The COVID-19 Disease: Association with Clinical Subtypes and A Follow-Up Study (preprint)

Background: To explore the clinical features and deep-learning (DL) based quantitative CT finding’s applications and evolution as well as the correlations in COVID-19.Methods: 273 chest CT scans (median interval, 6 days) from 75 COVID-19 RT-PCT positive patients (53 moderate and 22 severe) were included. Quantification parameters, such as CT value distribution, lesion (abnormal), GGO, consolidation rates, Hellinger distance and IOU, were automatically extracted from CT images by a combination of traditional image process algorithm and DL network. Clinical characteristics were also collected and analysed.Results: The hypertension and diabetes were more common in severity. The CRP, ESP, LDH and D-dimer were higher while LYM and LYM% lower in severity (P < 0.05). The DL network was detected the lesions to obtain quantitively CT indicators, with fast to process a chest CT images (average time, 2.2s) and high overlap with radiologist. The hellinger, abnormal, GGO, consolidation rates and HU values were higher and the IOU lower in severity than moderate patients (P < 0.05). The largest AUC was 0.943, using the cutoff value of 10.5% for abnormal rate. The CT score have postive correlations with CRP, D-dimer and ESR (P < 0.05). The increased levels of ESR and D-dimer were positively correlated with abnormal, consolidation and GGO rates (P < 0.05). Investigation for quantitative CT changes were performed in three periods: 1) 1-2 weeks, CT score and abnormal rate were increased. The GGO converted to consolidation in severity; 2) 2-5 weeks, CT scores stable trend, while abnormal and GGO rates had upward trend in severity; 3) > 5weeks, CT score and abnormal rate have decreased.Conclusions: There were three phases of two patterns’ evolutionary trends in quantitative CT findings with differences in two groups, and have correlations with laboratory markers, which helpful for evaluating severity and prognosis in COVID-19 patients.

Clinical Characteristics and Risk Factors of Fatal Patients with COVID-19: a Retrospective Cohort Study in Wuhan, China (preprint)

Background The coronavirus disease 2019 (COVID-19) has caused global pandemic, resulting in considerable mortality. The risk factors, clinical treatments and especially comprehensive risk models for COVID-19 death are urgently warranted.Methods In this retrospective study, 281 non-survivors and 712 survivors with propensity score matching by age, sex and comorbidities were enrolled from January 13, 2020 to March 31, 2020.Results Higher SOFA, qSOFA, APACHE II and SIRS scores, hypoxia, elevated inflammatory cytokines, multi-organ dysfunction, decreased immune cells subsets and complications were significantly associated with the higher COVID-19 death risk. In addition to traditional predictors for death risk, including APACHE II (AUC = 0.83), SIRS (AUC = 0.75), SOFA (AUC = 0.70) and qSOFA scores (AUC = 0.61), another four prediction models that included immune cells subsets (AUC = 0.90), multiple organ damage biomarkers (AUC = 0.89), complications (AUC = 0.88) and inflammatory-related indexes (AUC = 0.75) were established. Additionally, the predictive accuracy of combining these risk factors (AUC = 0.950) was also significantly higher than that of each risk group alone, outperforming previous risk models, which was significant for early clinical management for COVID-19.Conclusions The potential risk factors could help to predict the clinical prognosis of COVID-19 patients at an early stage. The combined model might be more suitable for the death risk evaluation of COVID-19.

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19) (preprint)

Objectives: This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. Materials and Methods: 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: 1. Correlation between these two estimations; 2. Exploring the dynamic patterns using these two estimations between moderate and severe groups.Results: The Spearman’s correlation coefficient between these two estimation methods was 0.920 (p<0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. Conclusions: The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.