Dynamic feature learning for COVID-19 segmentation and classification

Since December 2019, coronavirus SARS-CoV-2 (COVID-19) has rapidly developed into a global epidemic, with millions of patients affected worldwide. As part of the diagnostic pathway, computed tomography (CT) scans are used to help guide patient management. However, parenchymal imaging findings in COVID-19 are non-specific and can be seen in other diseases. In this work, we propose to first segment lesions from CT images, and further, classify COVID-19 patients from healthy persons and common pneumonia patients. In detail, a novel Dynamic Fusion Segmentation Network (DFSN) that automatically segments infection-related pixels is first proposed. Within this network, low-level features are aggregated to high-level ones to effectively capture context characteristics of infection regions, and high-level features are dynamically fused to model multi-scale semantic information of lesions. Based on DFSN, Dynamic Transfer-learning Classification Network (DTCN) is proposed to distinguish COVID-19 patients. Within DTCN, a pre-trained DFSN is transferred and used as the backbone to extract pixel-level information. Then the pixel-level information is dynamically selected and used to make a diagnosis. In this way, the pre-trained DFSN is utilized through transfer learning, and clinical significance of segmentation results is comprehensively considered. Thus DTCN becomes more sensitive to typical signs of COVID-19. Extensive experiments are conducted to demonstrate the effectiveness of the proposed DFSN and DTCN frameworks. The corresponding results indicate that these two models achieve state-of-the-art performance in terms of segmentation and classification.

A differential diagnosis model for patients with COVID-19 and non-COVID-19 pneumonia based on laboratory indicators

OBJECTIVE: To retrospectively analyze the clinical characteristics, imaging features and laboratory indexes of the patients with COVID-19 and non-COVID-19 so as to seek for differential diagnosis indexes. METHODS: A total of 66 patients with COVID-19 and 40 non-COVID-19 patients were recruited as study subjects who were treated in the hospital from Jan 2020 to Apr 2020. The demographic data, clinical symptoms, underlying diseases, imaging features, length of hospital stay and laboratory test indexes at the admission were statistically analyzed. RESULTS: The white blood cell(WBC),albumin(ALB) and prealbumin(PALB) of the COVID-19 patients were remarkably lower than those of the non-COVID-19 patients;while the length of hospital stay, aspartate aminotransferase(AST), international normalized ratio(INR), fibrinogen(Fbg), lactate dehydrogenase(LDH), tumor specific growth factor(TSGF) and ferritin(Ferritin) of the COVID-19 group were remarkably higher than those of the non-COVID-19 group. The COVID-19 patients had a higher frequency of air bronchogram, reticular pattern, number of affected lobes and number of affected segments, but a lower frequency of centrilobular nodules than did the non-COVID-19 patients. The length of hospital stay of the COVID-19 patients was positively correlated with the age but was negatively correlated with LYM and ALB, and the length of hospital stay of the patients complicated with diabetes mellitus and hypertension was longer than the patients with other complications. Receiver operating characteristic(ROC) curve analysis showed that the areas under curves of WBC, TSGF, LDH and Ferritin were more than 75% in distinguishing between COVID-19 and non-COVID-19. Multivariate logistic regression analysis showed that TSGF, LDH and Ferritin were the independent factors for distinguishing between COVID-19 and non-COVID-19, and the area under curve of the joint detection of the three indexes was 0.9181. CONCLUSION: The ordinary COVID-19 patients and non-COVID-19 patients vary in some clinical characteristic, imaging features and clinical laboratory indexes. The joint diagnosis model of TSGF, LDH and Ferritin may be used as an effective indicator for distinguishing between ordinary COVID-19 and non-COVID-19.

Conjugation with 8-arm PEG and CRM197 enhances the immunogenicity of SARS-CoV-2 ORF8 protein (preprint)

The SARS-CoV-2 variants raise concerns about the effectiveness of vaccines. Safe and effective vaccines are urgently needed to combat the COVID-19 pandemic. As a SARS-CoV-2 antigen target, ORF8 strongly inhibits the IFN-β and NF-κB-responsive promoter, which is a potential antigen target for the development of SARS-CoV-2 vaccine. Adjuvants or delivery system were necessitated to improve the immunogenicity of ORF8. CRM197 was a carrier protein with the ability to activate T helper cells for antigens. Eight-arm PEG could conjugate multiple antigen molecules in one entity with inherent adjuvant effect. In the present study, ORF8 was conjugated with CRM197 and 8-arm PEG, respectively. The cellular and humoral immune responses to the conjugates (ORF8-CRM and ORF8-PEG) were evaluated in the BALB/c mice. As compared with ORF8-CRM and ORF8 administrated with aluminum adjuvant (ORF8/AL), ORF8-PEG induced a higher ORF8-specific IgG titer (2.6x10), higher levels of cytokines (IFN-γ, TNF-α, IFN-β, and IL-5), stronger splenocyte proliferation. Thus, conjugation with 8-arm PEG was an effective method to improve the immune response to ORF8. Moreover, ORF8-PEG did not lead to apparent toxicity to the cardiac, liver and renal functions. ORF8-PEG was expected to act as an effective vaccine to provide the immune protection against SARS-CoV-2.

The Impact of Consumer Purchase Behavior Changes on the Business Model Design of Consumer Services Companies Over the Course of COVID-19.

The COVID-19 pandemic has had a profound psychological and behavioral impact on people around the world. Consumer purchase behaviors have thus changed greatly, and consumer services companies need to adjust their business models to adapt to this change. From the perspective of consumer psychology, this paper explores the impact of consumer purchase behavior changes over the course of the pandemic on the business model design of consumer services companies using a representative survey of 1,742 individuals. Our results show that changes in consumer purchase behavior have a significant impact on the design of consumer services firms' business models. Specifically, changes in consumers' purchase object, motive, and timeframe are more likely to spark a novelty-centered business model design, whereas changes in purchase method tend to inspire an efficiency-centered one. Our findings provide a theoretical reference for consumer services companies in designing business models when faced with unexpected crises.

Disparities in Geographical Accessibility of Permanent COVID-19 Vaccination clinics in the State of Ohio (preprint)

Rapid and widespread distribution of the COVID-19 vaccine is crucial for containing the spread of this infectious disease. Health departments and planners must ensure that access to the COVID-19 vaccination is adequate and equitable. This study measured the spatial accessibility to permanent vaccination clinics at the census tract level using the 2-step floating catchment area method. Accessibility scores were heterogeneous across geographic regions and among different groups of people. In particular, many urban areas enjoy better access to vaccination clinics compared to rural areas. Minorities and people under poverty are concentrated in neighborhoods with above-average accessibility, while white and elderly concentrate in census tracts with below-average accessibility. The relationship between accessibility and the Social Vulnerability Index (SVI) was explored using the Spatial lag model and Bivariate Local Moran’s I analysis. Patterns of high accessibility scores and high social vulnerability index could be observed in urban areas, while suburban areas enjoy high accessibility and low social vulnerability. Patterns of high accessibility census tracts adjacent to tracts with high minority rates and low adjacent to low are salient, indicating the strong relationship between race and accessibility. The Spatial Lag model also confirms this finding.

Context-Aware Knowledge Tracing Integrated with the Exercise Representation and Association in Mathematics

Influenced by COVID-19, online learning has become one of the most important forms of education in the world. In the era of intelligent education, knowledge tracing (KT) can provide excellent technical support for individualized teaching. For online learning, we come up with a new knowledge tracing method that integrates mathematical exercise representation and association of exercise (ERAKT). In the aspect of exercise representation, we represent the multi-dimensional features of the exercises, such as formula, text and associated concept, by using ontology replacement method, language model and embedding technology, so we can obtain the unified internal representation of exercise. Besides, we utilize the bidirectional long short memory neural network to acquire the association between exercises, so as to predict his performance in future exercise. Extensive experiments on a real dataset clearly proved the effectiveness of ERAKT method, they also verified that adding multi-dimensional features and exercise association can indeed improve the accuracy of prediction. [For the full proceedings, see ED615472.]

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.)

Human Mobility Data in the COVID-19 Pandemic: Characteristics, Applications, and Challenges (preprint)

The COVID-19 pandemic poses unprecedented challenges around the world. Many studies indicate that human mobility data provide significant support for public health actions during the pandemic. Researchers have applied mobility data to explore spatiotemporal trends over time, investigate associations with other variables, and predict or simulate the spread of COVID-19. Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks. We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective. We identified three major sources of mobility data: public transit systems, mobile operators, and mobile phone applications. Four approaches have been commonly used to estimate human mobility: public transit-based flow, social activity patterns, index-based mobility data, and social media-derived mobility data. We compared mobility datasets’ characteristics by assessing data privacy, quality, space-time coverage, high-performance data storage and processing, and accessibility. We also present challenges and future directions of using mobility data. This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.

Revealing public opinion towards COVID-19 vaccines using Twitter data in the United States: a spatiotemporal perspective (preprint)

Background: The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States (US) and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. Objective: The aim of this study is to investigate public opinion and perception on COVID-19 vaccines by investigating the spatiotemporal trends of their sentiment and emotion towards vaccines, as well as how such trends relate to popular topics on Twitter in the US. Methods: We collected over 300,000 geotagged tweets in the US from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified three phases along the pandemic timeline with the significant changes of public sentiment and emotion. We further linked the changes to eleven key events and major topics as the potential drivers to induce such changes via cloud mapping of keywords and topic modeling. Results: An increasing trend of positive sentiment in parallel with the decrease of negative sentiment are generally observed in most states, reflecting the rising confidence and anticipation of the public towards COVID-19 vaccines. The overall tendency of the eight types of emotion implies the trustiness and anticipation of the public to vaccination, accompanied by the mixture of fear, sadness and anger. Critical social/international events and/or the announcements of political leaders and authorities may have potential impacts on the public opinion on vaccines. These factors, along with important topics and manual reading of popular posts on eleven key events, help identify underlying themes and validate insights from the analysis. Conclusions: The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics and promote the confidence of individuals within a certain region or community, towards vaccines.

ODT FLOW: A Scalable Platform for Extracting, Analyzing, and Sharing Multi-source Multi-scale Human Mobility (preprint)

In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows. Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes with extensive spatial coverage, which allows for efficient extraction, query, and aggregation of billion-level origin-destination (OD) flows in parallel at the server-side. An interactive spatial web portal, ODT Flow Explorer, is developed to allow users to explore multi-source mobility datasets with user-defined spatiotemporal scales. To promote reproducibility and replicability, we further develop ODT Flow REST APIs that provide researchers with the flexibility to access the data programmatically via workflows, codes, and programs. Demonstrations are provided to illustrate the potential of the APIs integrating with scientific workflows and with the Jupyter Notebook environment. We believe the platform coupled with the derived multi-scale mobility data can assist human mobility monitoring and analysis during disaster events such as the ongoing COVID-19 pandemic and benefit both scientific communities and the general public in understanding human mobility dynamics.

Identification of novel bat coronaviruses sheds light on the evolutionary origins of SARS-CoV-2 and related viruses (preprint)

Although a variety of SARS-CoV-2 related coronaviruses have been identified, the evolutionary origins of this virus remain elusive. We describe a meta-transcriptomic study of 411 samples collected from 23 bat species in a small (~1100 hectare) region in Yunnan province, China, from May 2019 to November 2020. We identified coronavirus contigs in 40 of 100 sequencing libraries, including seven representing SARS-CoV-2-like contigs. From these data we obtained 24 full-length coronavirus genomes, including four novel SARS-CoV-2 related and three SARS-CoV related genomes. Of these viruses, RpYN06 exhibited 94.5% sequence identity to SARS-CoV-2 across the whole genome and was the closest relative of SARS-CoV-2 in the ORF1ab, ORF7a, ORF8, N, and ORF10 genes. The other three SARS-CoV-2 related coronaviruses were nearly identical in sequence and clustered closely with a virus previously identified in pangolins from Guangxi, China, although with a genetically distinct spike gene sequence. We also identified 17 alphacoronavirus genomes, including those closely related to swine acute diarrhea syndrome virus and porcine epidemic diarrhea virus. Ecological modeling predicted the co-existence of up to 23 Rhinolophus bat species in Southeast Asia and southern China, with the largest contiguous hotspots extending from South Lao and Vietnam to southern China. Our study highlights both the remarkable diversity of bat viruses at the local scale and that relatives of SARS-CoV-2 and SARS-CoV circulate in wildlife species in a broad geographic region of Southeast Asia and southern China. These data will help guide surveillance efforts to determine the origins of SARS-CoV-2 and other pathogenic coronaviruses.

Human mobility and COVID-19 transmission: a systematic review and future directions (preprint)

Without a widely distributed vaccine, controlling human mobility has been identified and promoted as the primary strategy to mitigate the transmission of COVID-19. Many studies have reported the relationship between human mobility and COVID-19 transmission by utilizing the spatial-temporal information of mobility data from various sources. To better understand the role of human mobility in the pandemic, we conducted a systematic review of articles that measure the relationship between human mobility and COVID-19 in terms of their data sources, statistical models, and key findings. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we selected 47 articles from Web of Science Core Collection up to September 2020. Restricting human mobility reduced the transmission of COVID-19 spatially, although the effectiveness and stringency of policy implementation vary temporally and spatially across different stages of the pandemic. We call for prompt and sustainable measures to control the pandemic. We also recommend researchers 1) to enhance multi-disciplinary collaboration; 2) to adjust the implementation and stringency of mobility-control policies in corresponding to the rapid change of the pandemic; 3) to improve statistical models used in analyzing, simulating, and predicting the transmission of the disease; and 4) to enrich the source of mobility data to ensure data accuracy and suability.

The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States (preprint)

Background: Human mobility among geographic units is a possible cause of the widespread transmission of COVID-19 across regions. Due to the pressure of epidemic control and economic recovery, the states of the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating the epidemic policies.Methods: The study utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (except the District of Colombia) with the daily new cases at the county level from Jan 22, 2020, to August 20, 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation and stepwise OLS regression with socioeconomic factors.Results: The K-means clustering divided the time-varying spatial autocorrelation curves of 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with median age, population density, and the proportion of international immigrants and the highly educated population, but negatively correlated with the birth rate. The voting rate for Donald Trump in the 2016 U.S. presidential election showed a weak negative correlation. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and the highly educated population proportion.Interpretation: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population, high-density populated states need to strengthen regional mobility restrictions, and the highly educated population should reduce unnecessary regional movement and strengthen self-protection.

The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States (preprint)

Background: Human mobility among geographic units is a possible cause of the widespread transmission of COVID-19 across regions. Due to the pressure of epidemic control and economic recovery, the states of the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating the epidemic policies.Methods: The study utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (except the District of Colombia) with the daily new cases at the county level from Jan 22, 2020, to August 20, 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation and stepwise OLS regression with socioeconomic factors.Results: The K-means clustering divided the time-varying spatial autocorrelation curves of 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with median age, population density, and the proportion of international immigrants and the highly educated population, but negatively correlated with the birth rate. The voting rate for Donald Trump in the 2016 U.S. presidential election showed a weak negative correlation. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and the highly educated population proportion.Interpretation: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population, high-density populated states need to strengthen regional mobility restrictions, and the highly educated population should reduce unnecessary regional movement and strengthen self-protection. 

Spatiotemporal Differences of COVID-19 Infection among Healthcare Workers and Patients in China from January to March 2020 (preprint)

Studying the spatiotemporal differences in coronavirus disease (COVID-19) between social groups such as healthcare workers (HCWs) and patients can aid in formulating epidemic containment policies. Most previous studies of the spatiotemporal characteristics of COVID-19 were conducted in a single group and did not explore the differences between groups. To fill this research gap, this study assessed the spatiotemporal characteristics and differences among patients and HCWs infection in Wuhan, Hubei (excluding Wuhan), and China (excluding Hubei). The temporal difference was greater in Wuhan than in the rest of Hubei, and was greater in Hubei (excluding Wuhan) than in the rest of China. The incidence was high in healthcare workers in the early stages of the epidemic. Therefore, it is important to strengthen the protective measures for healthcare workers in the early stage of the epidemic. The spatial difference was less in Wuhan than in the rest of Hubei, and less in Hubei (excluding Wuhan) than in the rest of China. The spatial distribution of healthcare worker infections can be used to infer the spatial distribution of the epidemic in the early stage and to formulate control measures accordingly.

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distribution using healthcare worker infection data (preprint)

A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of cases is widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.

Estimating Unreported COVID-19 Cases in the United States Based on the tvSIRu Model (preprint)

BackgroundThe potential unreported infection may impair and mislead policymaking for COVID-19，and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that may be underestimated based on county-level data to take better countermeasures against COVID-19. We suggested taking time-varying SIR models with unreported infection rates (UIR)to estimate the factual COVID-19 cases in the United States.MethodsSIR integrated with unreported infection rates (SIRu) of fixed time effect and SIR with time-varying parameters (tvSIRu)were applied to estimate and compare the value of transmission rate(TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. ResultsBased on US county-level COVID-19 data from January 22 (T1) to August 20 (T212) in 2020, SIRu was first tested and verified by a general OLS regression. The further regression of SIRu at the country-level showed that the average values of TR, UIR, and IFR were 0.034,19.5, 0.51% respectively. The range of TR, UIR, IFR of all states ranged were 0.007-0.157 (mean=0.048) ,7.31-185.6 (mean=38.89), and 0.04%-2.22% (mean=0.22%). Among time-varying transmission rate equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the UIR has an estimate of 9.1(95%CI = 5.7-14.0), and IFR was 0.70% (0.52%-0.95%) at T212.InterpretationDespite the decline in TR and IFR, the UIR of the United States is still on the rise, which had been supposed to decrease with sufficient tests or improved countersues. The US medical system may be largely affected by severe cases in the rapid spread of COVDI-19.

Estimating Unreported COVID-19 Cases in the United States Based on Time-Varying SIR Model (preprint)

Background The potential unreported infection may impair and mislead policymaking，and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that may be underestimated based on county-level data to take better countermeasures against COVID-19. We suggested taking time-varying SIR models with unreported infection rates (UIR)to estimate the factual COVID-19 cases in the United States.Methods SIR integrated with unreported infection rates (SIRu) of fixed time effect and SIR with time-varying parameters (tvSIRu)were applied to estimate and compare the value of transmission rate(TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. Results Based on US county-level COVID-19 data from January 22 (T1) to August 20 (T212) in 2020, SIRu was first tested and verified by a general OLS regression. The further regression of SIRu at the country-level showed that the average values of TR, UIR, and IFR were 0.034,19.5, 0.51% respectively. The range of IR, UIR, IFR of all states ranged were 0.007-0.157 (mean=0.048) ,7.31-185.6 (mean=38.89), and 0.04%-2.22% (mean=0.22%). Among time-varying transmission rate equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the UIR has an estimate of 9.1(95%CI = 5.7-14.0), and IFR was 0.70% (0.52%-0.95%) at T212.Interpretation Despite the decline in TR and IFR, the UIR of the United States is still on the rise, which had been supposed to decrease with sufficient tests or improved countersues. The US medical system may be largely affected by severe cases in the rapid spread of COVDI-19.