Integrated interactome and transcriptome analysis reveals key host factors critical for SARS-CoV-2 infection.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has seriously threatened global public health and caused huge economic losses. Omics studies of SARS-CoV-2 can help understand the interaction between the virus and host, thereby providing a new perspective in guiding the intervention and treatment of the SARS-CoV-2 infection. Since large amount of SARS-CoV-2 omics data have been accumulated in public databases, this study aimed to identify key host factors involved in SARS-CoV-2 infection through systematic integration of transcriptome and interactome data. By manually curating published studies, we obtained a comprehensive SARS-CoV-2-human protein-protein interactions (PPIs) network, comprising 3591 human proteins interacting with 31 SARS-CoV-2 viral proteins. Using the RobustRankAggregation method, we identified 123 multiple cell line common genes (CLCGs), of which 115 up-regulated CLCGs showed host enhanced innate immunity and chemotactic response signatures. Combined with network analysis, co-expression and functional enrichment analysis, we discovered four key host factors involved in SARS-CoV-2 infection: IFITM1, SERPINE1, DDX60, and TNFAIP2. Furthermore, SERPINE1 was found to facilitate SARS-CoV-2 replication, and can alleviate the endoplasmic reticulum (ER) stress induced by ORF8 protein through interaction with ORF8. Our findings highlight the importance of systematic integration analysis in understanding SARS-CoV-2-human interactions and provide valuable insights for future research on potential therapeutic targets against SARS-CoV-2 infection.

Inactivated vaccine-elicited potent antibodies can broadly neutralize SARS-CoV-2 circulating variants.

A full understanding of the inactivated COVID-19 vaccine-mediated antibody responses to SARS-CoV-2 circulating variants will inform vaccine effectiveness and vaccination development strategies. Here, we offer insights into the inactivated vaccine-induced antibody responses after prime-boost vaccination at both the polyclonal and monoclonal levels. We characterized the VDJ sequence of 118 monoclonal antibodies (mAbs) and found that 20 neutralizing mAbs showed varied potency and breadth against a range of variants including XBB.1.5, BQ.1.1, and BN.1. Bispecific antibodies (bsAbs) based on nonoverlapping mAbs exhibited enhanced neutralizing potency and breadth against the most antibody-evasive strains, such as XBB.1.5, BQ.1.1, and BN.1. The passive transfer of mAbs or their bsAb effectively protected female hACE2 transgenic mice from challenge with an infectious Delta or Omicron BA.2 variant. The neutralization mechanisms of these antibodies were determined by structural characterization. Overall, a broad spectrum of potent and distinct neutralizing antibodies can be induced in individuals immunized with the SARS-CoV-2 inactivated vaccine BBIBP-CorV, suggesting the application potential of inactivated vaccines and these antibodies for preventing infection by SARS-CoV-2 circulating variants.

COVID‐19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions

Although scholars in management recognize the value of harnessing big data to understand, predict and respond to future events, there remains little or very limited overview of how various analytics techniques can be harnessed to provide the basis for guiding scholars in studying contemporary management topics and global grand challenges raised by the COVID‐19 pandemic. In this Methodology Corner, we present a review of the methodological innovations in studying big data analytics and how they can be better utilized to examine contemporary organizational issues. We provide insights on methods in descriptive/diagnostic, predictive and prescriptive analytics, and how they can be leveraged to study ‘black swan' events such as the COVID‐19‐related global crisis and its aftermath's implications for managers and policymakers.

Identifying spatiotemporal trends of SARS-CoV-2 RNA in wastewater: From the perspective of upstream and downstream wastewater-based epidemiology (WBE) (preprint)

Recently, many efforts have been made to address the rapid spread of newly identified COVID-19 virus variants . Wastewater-based epidemiology (WBE) is considered as a potential early warning tool for identifying the rapid spread of this virus. This study investigated the occurrence of SARS-CoV-2 in eight wastewater treatment plants (WWTPs) and their sewerage systems which serve most of the population in Taoyuan City, Taiwan. Across the entire study period, the wastewater viral concentrations were correlated with the number of COVID-19 cases in each WWTP (Spearman' r = 0.23 - 0.76). In addition, it is confirmed that several treatment technologies could effectively eliminate the virus RNA from WWTPs influent (> 90 %). On the other hand, further results revealed that an inverse distance weighted (IDW) interpolation and hot spot model combined with geographic information system (GIS) method could be applied to analyze the spatiotemporal variations of SARS-CoV-2 in wastewater from sewer system. In addition, socio-economic factors namely population density, land-use, and tax-income were successfully identified as the potentials drivers which substantially affect the onset of COVID-19 outbreak in Taiwan. Finally, the data obtained from this study can provide a powerful tool in public health decision-making not only in response to the current epidemic situation but also other epidemic issues in the future.

Immunotherapy for Sepsis Induced by Infections: Clinical Evidence and Potential Targets.

Sepsis is a life-threatening organ dysfunction caused by the maladjustment of the body's response to infection. Abnormal immune response plays an important role in the progression of sepsis, and immunomodulatory therapy is a promising therapeutic strategy for sepsis. Great efforts have been made recently to elucidate the mechanism by which immune dysfunction contributes to sepsis, and identify potential biomarkers and targets for the diagnosis and therapy of sepsis induced by emerging pathogens, especially for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)that causes COVID-19. In this review, we summarize recent progress on the understanding of immune dysregulation involved in sepsis, and highlight potential biomarkers and targets to evaluate immune status of the patients with sepsis for individualized and precise immunotherapy.

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations.

The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature-at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset-consisting of over 30 000 articles with manually reviewed topics-was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/.

A BERT-based ensemble learning approach for the BioCreative VII challenges: full-text chemical identification and multi-label classification in PubMed articles.

In this research, we explored various state-of-the-art biomedical-specific pre-trained Bidirectional Encoder Representations from Transformers (BERT) models for the National Library of Medicine - Chemistry (NLM CHEM) and LitCovid tracks in the BioCreative VII Challenge, and propose a BERT-based ensemble learning approach to integrate the advantages of various models to improve the system's performance. The experimental results of the NLM-CHEM track demonstrate that our method can achieve remarkable performance, with F1-scores of 85% and 91.8% in strict and approximate evaluations, respectively. Moreover, the proposed Medical Subject Headings identifier (MeSH ID) normalization algorithm is effective in entity normalization, which achieved a F1-score of about 80% in both strict and approximate evaluations. For the LitCovid track, the proposed method is also effective in detecting topics in the Coronavirus disease 2019 (COVID-19) literature, which outperformed the compared methods and achieve state-of-the-art performance in the LitCovid corpus. Database URL: https://www.ncbi.nlm.nih.gov/research/coronavirus/.

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations (preprint)

The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design vaccines and drugs. The related findings have been reported in biomedical literature at a rate of about 10,000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200,000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g., Diagnosis and Treatment) to the articles in LitCovid. Despite the continuing advances in biomedical text mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset, consisting of over 30,000 articles with manually reviewed topics, was created for training and testing. It is one of the largest multilabel classification datasets in biomedical scientific literature. 19 teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181, and 0.9394 for macro F1-score, micro F1-score, and instance-based F1-score, respectively. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development.

Patients' Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment.

BACKGROUND: Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19. OBJECTIVE: This study aims to visualize and measure patients' heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future. METHODS: A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables' coefficients, standard error, P value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes. RESULTS: A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, P<.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis "accuracy" attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, P<.001; class 3: OR 1.958, 95% CI 1.769-2.167, P<.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, P=.37). The OR of levels of attributes increases with the increase of accuracy across all classes. CONCLUSIONS: Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People's preferences for the "accuracy" and "diagnostic expenses" attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration.

Implementation of Thermal Camera for Non-Contact Physiological Measurement: A Systematic Review.

Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to measure the physiological signals such as respiratory rate, heart rate, and body temperature. In this paper, we provided a general overview of the existing studies by examining the physiological signals of measurement, the used platforms, the thermal camera models and specifications, the use of camera fusion, the image and signal processing step (including the algorithms and tools used), and the performance evaluation. The advantages and challenges of thermal camera-based physiological measurement were also discussed. Several suggestions and prospects such as healthcare applications, machine learning, multi-parameter, and image fusion, have been proposed to improve the physiological measurement of thermal camera in the future.

[Head and Face Protection of the Health Care Workers in the Operating Room of Peking Union Medical College Hospital during the Corona Virus Disease-19 Pandemic].

Objective To investigate the understanding of the head and face protection of the health care workers in operating room of Peking Union Medical College Hospital during the corona virus disease-19(COVID-19) pandemic.Methods The knowledge of head and face protection of health care workers in the operating room was evaluated based on the non-registered questionnaires for protection measures collected on-line.Results The survey was conducted in two phases.In the first phase(COVID-19 outbreak),153 questionnaires were collected.In the second phase(when Beijing lowered the emergency response to level 3 and normalized the epidemic prevention and control),101 questionnaires were collected.The results showed that 98% of health care workers had used any form of protective devices during the pandemic and anesthesiologists had the highest usage rate(93.0%)of ear-loop face mask with eye shield.During the pandemic,health care workers mainly used goggles(71.2%)for protection to diagnose and treat the patients with fever and ear-loop face mask with eye shield(56.2%)for protection to diagnose and treat the non-fever patients.In the first-and second-phase survey,43% and 68% of health care workers still used protection,and they mainly used face shield(50.0% and 56.5%)and ear-loop face mask with eye shield(56.1% and 68.1%).Conclusions During the pandemic,more than 90% of the health care workers in the operating room of Peking Union Medical College Hospital were aware of head and face protection.Different healthcare workers in the operating room had different choices of head and face protection,and more than 40% of them would still keep such protection during the normalized stage of pandemic prevention and control.

Etiological and epidemiological features of acute respiratory infections in China.

Nationwide prospective surveillance of all-age patients with acute respiratory infections was conducted in China between 2009‒2019. Here we report the etiological and epidemiological features of the 231,107 eligible patients enrolled in this analysis. Children <5 years old and school-age children have the highest viral positivity rate (46.9%) and bacterial positivity rate (30.9%). Influenza virus, respiratory syncytial virus and human rhinovirus are the three leading viral pathogens with proportions of 28.5%, 16.8% and 16.7%, and Streptococcus pneumoniae, Mycoplasma pneumoniae and Klebsiella pneumoniae are the three leading bacterial pathogens (29.9%, 18.6% and 15.8%). Negative interactions between viruses and positive interactions between viral and bacterial pathogens are common. A Join-Point analysis reveals the age-specific positivity rate and how this varied for individual pathogens. These data indicate that differential priorities for diagnosis, prevention and control should be highlighted in terms of acute respiratory tract infection patients' demography, geographic locations and season of illness in China.

Degradation of contaminants in plasma technology: An overview.

The information of plasma technologies applications for environmental clean-up on treating and degrading metals, metalloids, dyes, biomass, antibiotics, pesticides, volatile organic compounds (VOCs), bacteria, virus and fungi is compiled and organized in the review article. Different reactor configurations of plasma technology have been applied for reactive species generation, responsible for the pollutants removal, hydrogen and methane production and microorganism inactivation. Therefore, in this review article, the reactive species from discharge plasma are presented here to provide the insight into the environmental applications. The combinations of plasma technology with flux agent and photocatalytic are also given in this review paper associated with the setup of the plasma system on the removal process of metals, VOCs, and microorganisms. Furthermore, the potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) inactivation via plasma technology is also described in this review paper. Detailed information of plasma parameter configuration is given to support the influence of the critical process in the plasma system to deal with contaminants.

Attitudes towards COVID-19 precautionary measures and willingness to work during an outbreak among medical students in Singapore: a mixed-methods study.

BACKGROUND: The COVID-19 pandemic has revealed challenges that medical students face when healthcare systems are under intense pressure. There is a need to assess medical students' education needs in pandemic preparedness. The objective of this mixed-methods study was threefold: (1) to assess COVID-19 perceived efficacy, susceptibility, and anxiety in relation to health literacy; (2) to describe attitudes towards a policy of precautionary measures against COVID-19 and willingness to work during an outbreak; and (3) to examine multilevel factors associated with willingness to work. METHODS: An online survey was conducted among 263 medical students in Singapore during the lockdown period in July 2020. Participants were surveyed on COVID-19 related literacy, perceptions, anxiety, attitudes towards a policy of precautionary measures, and willingness to work during an outbreak. Bivariate and multivariate analyses were used to determine the factors associated with the key outcome variable of willingness to work. In addition, open-ended questions were used to assess medical education needs, which were reported using thematic analysis. RESULTS: Perceived adequacy of COVID-19 information was associated with higher perceived efficacy, lower perceived susceptibility, and lower anxiety levels among the students. Medical students were mostly supportive of COVID-19 precautionary measures except for relatively intrusive measures like in-home surveillance. The degree of willingness to work during an outbreak varied based on certain conditions, in particular family's health and safety, and was associated with self-efficacy, perceived susceptibility, and hospital capacity of outbreak management. CONCLUSIONS: Medical students' attitudes towards a policy of precautionary measures varied depending on legality, financial and psychological support, and privacy concerns. Health literacy played an important role in increasing the efficacy of protection against COVID-19 and reducing pandemic-related anxiety among medical students. Their willingness to work during an outbreak was increased by an effective policy of precautionary measures, hospital capacity to manage a pandemic, and assurance of family safety. Medical education should include pandemic preparedness to better prepare students to aid in pandemics, with emphasis on public health policy and ethics coupled with clinical training targeted to managing outbreaks.

Coronavirus disease 2019 outbreak in Beijing's Xinfadi Market, China: a modeling study to inform future resurgence response.

BACKGROUND: A local coronavirus disease 2019 (COVID-19) case confirmed on June 11, 2020 triggered an outbreak in Beijing, China after 56 consecutive days without a newly confirmed case. Non-pharmaceutical interventions (NPIs) were used to contain the source in Xinfadi (XFD) market. To rapidly control the outbreak, both traditional and newly introduced NPIs including large-scale management of high-risk populations and expanded severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR-based screening in the general population were conducted in Beijing. We aimed to assess the effectiveness of the response to the COVID-19 outbreak in Beijing's XFD market and inform future response efforts of resurgence across regions. METHODS: A modified susceptible-exposed-infectious-recovered (SEIR) model was developed and applied to evaluate a range of different scenarios from the public health perspective. Two outcomes were measured: magnitude of transmission (i.e., number of cases in the outbreak) and endpoint of transmission (i.e., date of containment). The outcomes of scenario evaluations were presented relative to the reality case (i.e., 368 cases in 34 days) with 95% Confidence Interval (CI). RESULTS: Our results indicated that a 3 to 14 day delay in the identification of XFD as the infection source and initiation of NPIs would have caused a 3 to 28-fold increase in total case number (31-77 day delay in containment). A failure to implement the quarantine scheme employed in the XFD outbreak for defined key population would have caused a fivefold greater number of cases (73 day delay in containment). Similarly, failure to implement the quarantine plan executed in the XFD outbreak for close contacts would have caused twofold greater transmission (44 day delay in containment). Finally, failure to implement expanded nucleic acid screening in the general population would have yielded 1.6-fold greater transmission and a 32 day delay to containment. CONCLUSIONS: This study informs new evidence that in form the selection of NPI to use as countermeasures in response to a COVID-19 outbreak and optimal timing of their implementation. The evidence provided by this study should inform responses to future outbreaks of COVID-19 and future infectious disease outbreak preparedness efforts in China and elsewhere.

Genetic and antigenic heterogeneity of GI-1/Massachusetts lineage infectious bronchitis virus variants recently isolated in China.

Four GI-1/Massachusetts-type (GI-1/Mass-type) infectious bronchitis virus (IBV) strains were isolated and the complete genomes of these isolates, coupled with the Mass-type live-attenuated vaccine H120 and the Mass-type pathogenic M41 strains, were sequenced in the present study. Our results show that isolates LJL/140820 and I0306/17 may be derived from the Ma5 (another Mass-type live-attenuated vaccine strain) and H120 vaccine strains, respectively. The I1124/16 strain was found to be a M41 variant that likely resulted from nucleotide accumulated mutations in the genome. Consistently, the results of the virus neutralization test showed that isolate I1124/16 was antigenically related but slight different from the M41. Our results from the protection experiments pointed out that chickens immunized with H120 failed to eliminate viral shedding after infection with the isolate I1124/16, which was different from that of M41; this result was consistent to the field observation and further implicated that the variant IBV isolate I1124/16 was antigenic different from the M41 strain. Furthermore, the I1124/16 was found to have comparable but slightly lower pathogenicity with the M41 strain. More studies based on the reverse genetic techniques are needed to elucidate the amino acids in the S1 subunit of spike protein contributing to the altered antigenicity of the isolate I1124/16. In addition, an IBV isolate, LJL/130609, was found to be originated from recombination events between the I1124/16- and Connecticut-like strains. Our results from the virus neutralization test also showed that isolates LJL/130609 and I1124/16 were antigenic closely related. Hence, there are at least 3 different genetic evolution patterns for the circulation of the GI-1/Mass-type IBV field strains in China. The differences of vaccines used, the field conditions and genetic pressures between different flocks, likely account for the emergence, evolution patterns, and characteristics of the Mass-type IBV strains.

The immunodominant and neutralization linear epitopes for SARS-CoV-2.

Although vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are under development, the antigen epitopes on the virus and their immunogenicity are poorly understood. Here, we simulate the 3D structures and predict the B cell epitopes on the spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins of SARS-CoV-2 using structure-based approaches and validate epitope immunogenicity by immunizing mice. Almost all 33 predicted epitopes effectively induce antibody production, six of these are immunodominant epitopes in individuals, and 23 are conserved within SARS-CoV-2, SARS-CoV, and bat coronavirus RaTG13. We find that the immunodominant epitopes of individuals with domestic (China) SARS-CoV-2 are different from those of individuals with imported (Europe) SARS-CoV-2, which may be caused by mutations on the S (G614D) and N proteins. Importantly, we find several epitopes on the S protein that elicit neutralizing antibodies against D614 and G614 SARS-CoV-2, which can contribute to vaccine design against coronaviruses.

The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis.

BACKGROUND: The influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated. OBJECTIVE: This study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities. METHODS: Pearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available. RESULTS: The Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=-0.565, P<.001), Shanghai (r=-0.47, P<.001), and Guangzhou (r=-0.53, P<.001). In Japan, however, a positive correlation was observed (r=0.416, P<.001). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), GAM analysis showed the number of daily new confirmed cases to be positively associated with both average temperature and relative humidity, especially using lagged 3D modeling where the positive influence of temperature on daily new confirmed cases was discerned in 5 cities (exceptions: Beijing, Wuhan, Korea, and Malaysia). Moreover, the sensitivity analysis showed, by incorporating the city grade and public health measures into the model, that higher temperatures can increase daily new case numbers (beta=0.073, Z=11.594, P<.001) in the lagged 3-day model. CONCLUSIONS: The findings suggest that increased temperature yield increases in daily new cases of COVID-19. Hence, large-scale public health measures and expanded regional research are still required until a vaccine becomes widely available and herd immunity is established.

Comparative Transcriptome Analysis Reveals the Intensive Early Stage Responses of Host Cells to SARS-CoV-2 Infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a widespread outbreak of highly pathogenic coronavirus disease 2019 (COVID-19). It is therefore important and timely to characterize interactions between the virus and host cell at the molecular level to understand its disease pathogenesis. To gain insights, we performed high-throughput sequencing that generated time-series data simultaneously for bioinformatics analysis of virus genomes and host transcriptomes implicated in SARS-CoV-2 infection. Our analysis results showed that the rapid growth of the virus was accompanied by an early intensive response of host genes. We also systematically compared the molecular footprints of the host cells in response to SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV). Upon infection, SARS-CoV-2 induced hundreds of up-regulated host genes hallmarked by a significant cytokine production, followed by virus-specific host antiviral responses. While the cytokine and antiviral responses triggered by SARS-CoV and MERS-CoV were only observed during the late stage of infection, the host antiviral responses during the SARS-CoV-2 infection were gradually enhanced lagging behind the production of cytokine. The early rapid host responses were potentially attributed to the high efficiency of SARS-CoV-2 entry into host cells, underscored by evidence of a remarkably up-regulated gene expression of TPRMSS2 soon after infection. Taken together, our findings provide novel molecular insights into the mechanisms underlying the infectivity and pathogenicity of SARS-CoV-2.

COVID-19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions

Although scholars in management recognize the value of harnessing big data to understand, predict and respond to future events, there remains little or very limited overview of how various analytics techniques can be harnessed to provide the basis for guiding scholars in studying contemporary management topics and global grand challenges raised by the COVID-19 pandemic. In this Methodology Corner, we present a review of the methodological innovations in studying big data analytics and how they can be better utilized to examine contemporary organizational issues. We provide insights on methods in descriptive/diagnostic, predictive and prescriptive analytics, and how they can be leveraged to study 'black swan' events such as the COVID-19-related global crisis and its aftermath's implications for managers and policymakers.