2020 update on human coronaviruses: One health, one world.

Since human coronavirus (HCoVs) was first described in the 1960s, seven strains of respiratory human coronaviruses have emerged and caused human infections. After the emergence of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), a pneumonia outbreak of coronavirus disease 2019 (COVID-19) caused by a novel coronavirus (SARS-CoV-2) has represented a pandemic threat to global public health in the 21st century. Without effectively prophylactic and therapeutic strategies including vaccines and antiviral drugs, these three coronaviruses have caused severe respiratory syndrome and high case-fatality rates around the world. In this review, we detail the emergence event, origin and reservoirs of all HCoVs, compare the differences with regard to structure and receptor usage, and summarize therapeutic strategies for COVID-19 that cause severe pneumonia and global pandemic.

Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study.

BACKGROUND: The impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China. METHODS: We descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011-2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020-2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020-2021. RESULTS: The weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011-2019, with an obvious winter-spring seasonality in Beijing. During the 2020-2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, "Cancelling public events" and "Restrictions on internal travel" measures played an important role in the reduction of ILI in Beijing, while the "restrictions on international travel" was statistically most associated with ILI reductions in the Hong Kong SAR. CONCLUSIONS: Our study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future.

Prevention and control strategies in medical institutions under the pandemic of omicron variant strain

OBJECTIVE: To discuss the infection prevention and control strategies of medical institutions under the epidemic situation of Omicron mutant so as to provide guidance for the emergency treatment work under special circumstances, such as the discovery of positive cases and positive mixed sampling tests, the discovery of close contacts in the hospital, the emergence of positive cases in emergency patients, and the emergence of positive cases in hospital staff. METHODS: Through reviewing the response behaviors and policies of local health administration departments and medical institutions since the current COVID-19 in Shandong Province, the paper summarized the emergency response process and exposed weaknesses, and explores corresponding strategies. RESULTS: The regional and systematic emergency response plan system for hospital infection control should be established and improved, the awareness of medical institutions' infection control should be enhanced, the organizational management ability, risk control ability and personal protection ability should be promoted, and emergency disposal guidelines for infection prevention and control in medical institutions should be formulated. CONCLUSION: The infection prevention and control strategies under the epidemic situation of Omicron mutant has exposed the weaknesses of medical institutions. The ability to integrate resources and medical service should be enhanced to promote the accurate prevention and control of hospital infection, and work should be done to allocate manpower shortage so as to ensure the service continuity of key departments and achieve collaborative linkage and governance.

Investigation of the mechanism of fangfeng tongsheng pills in the treatment COVID-19 by network pharmacology and molecular docking

Objective: To explore the active components and potential mechanism of Fangfeng Tongsheng Pills by using network pharmacology and molecular docking in the treatment of coronavirus disease 19(COVID-19). Methods The main chemical constituents and action targets of various medicines in Fangfeng Tongsheng Pills were collected via traditional Chinese medicine system pharmacology database and online analysis platform(TCMSP). The related targets of COVID-19 were collected by using GeneCards database, and the repeating parts with Fangfeng Tongsheng Pills were taken as the research targets. Cytoscape software was used to create a drug-target-disease network. The common target was imported into STRING database, and the protein-protein interaction network diagram was constructed by Cytoscape software. The GO(gene ontology) function enrichment analysis and Kyoto encyclopedia of genes and genomes(KEGG) pathway enrichment analysis were performed by DAVID to predict their mechanism. The core components of Fangfeng Tongsheng Pills were docked with the therapeutic target of COVID-19 by AutoDock software. Results A total of 224 active compounds and 696 active targets were screened from Fangfeng Tongsheng Pills, including 79 targets coincided with COVID-19, and 10 active compounds, i.e. quercetin, luteolin, kaempferol,beta-sitosterol, naringenin, etc., 23 effective targets, i.e. PTGS2, PTGS1, NOS2, F10, DPP4, etc. A total of 65 GO function enrichment analysis results and 101 KEGG pathway enrichment results were obtained, including inflammatory response, tumor necrosis factor(TNF) signaling pathway, hypoxia inducible factor-1(HIF-1) signaling pathway, vascular endothelial growth factors(VEGF) signaling pathway, toll-like receptors(TLRs) signaling pathway, phosphatidylinositol 3-kinase-protein kinase B(PI3K-Akt) signaling pathway, and mitogen-activated protein kinase(MAPK) signaling pathway. Conclusion The active components in Fangfeng Tongsheng Pills, such as beta-sitosterol, quercetin, luteolin, kaempferol and naringenin, can combine with SARS-Co V2-3CL hydrolase and ACE2, act on the key target [TNF, Caspase-3, mitogen-activated protein kinase(MAPK1), interleukin-6(IL-6), prostaglandin-endoperoxide synthase 2(PGTS2)] of TNF, HIF-1, VEGF, MAPK and toll-like receptor signaling pathway, and play the roles of anti-inflammation, immune regulation, anti-hypoxic stress and anti-virus infection, thus play a role in the treatment of COVID-19.

Identifying the shifting sources to predict the dynamics of COVID-19 in the U.S.

Mobility restriction is a crucial measure to control the transmission of the COVID-19. Research has shown that effective distance measured by the number of travelers instead of physical distance can capture and predict the transmission of the deadly virus. However, these efforts have been limited mainly to a single source of disease. Also, they have not been tested on finer spatial scales. Based on prior work of effective distances on the country level, we propose the multiple-source effective distance, a metric that captures the distance for the virus to propagate through the mobility network on the county level in the U.S. Then, we estimate how the change in the number of sources impacts the global mobility rate. Based on the findings, a new method is proposed to locate sources and estimate the arrival time of the virus. The new metric outperforms the original single-source effective distance in predicting the arrival time. Last, we select two potential sources and quantify the arrival time delay caused by the national emergency declaration. In doing so, we provide quantitative answers on the effectiveness of the national emergency declaration.

Can I Get Back Later or Turn It Off? Day-Level Effect of Remote Communication Autonomy on Sustainable Proactivity

Overwhelming remote communication episodes have become critical daily work demands for employees. On the basis of affective event theory, this study explores the effect of daily remote communication autonomy on positive affect and proactive work behaviors. We conducted a multilevel path analysis using a general survey, followed by experience sampling methodology, with a sample of 80 employees in China who completed surveys thrice daily over a two-week period. The results showed that daily remote communication autonomy increased positive affective reactions, which, in turn, enhanced proactive work behaviors on the same workday. Furthermore, positive day-level relationships leading to employee proactivity were only significant when the employees’ person-level general techno-workload was not high. The findings provide a new perspective for managing employees working under continuous techno-workload and demands for remote interactions.

Network percolation reveals adaptive bridges of the mobility network response to COVID-19.

Human mobility is crucial to understand the transmission pattern of COVID-19 on spatially embedded geographic networks. This pattern seems unpredictable, and the propagation appears unstoppable, resulting in over 350,000 death tolls in the U.S. by the end of 2020. Here, we create the spatiotemporal inter-county mobility network using 10 TB (Terabytes) trajectory data of 30 million smart devices in the U.S. in the first six months of 2020. We investigate the bond percolation process by removing the weakly connected edges. As we increase the threshold, the mobility network nodes become less interconnected and thus experience surprisingly abrupt phase transitions. Despite the complex behaviors of the mobility network, we devised a novel approach to identify a small, manageable set of recurrent critical bridges, connecting the giant component and the second-largest component. These adaptive links, located across the United States, played a key role as valves connecting components in divisions and regions during the pandemic. Beyond, our numerical results unveil that network characteristics determine the critical thresholds and the bridge locations. The findings provide new insights into managing and controlling the connectivity of mobility networks during unprecedented disruptions. The work can also potentially offer practical future infectious diseases both globally and locally.

PTSD Among Healthcare Workers During the COVID-19 Outbreak: A Study Raises Concern for Non-medical Staff in Low-Risk Areas.

Objective: To investigate the prevalence of sleep quality and post-traumatic stress disorder (PTSD) symptoms of healthcare workers (HCWs) and identify the determinants for PTSD symptoms among HCWs in high-risk and low-risk areas during the COVID-19 outbreak in China. Methods: The Pittsburgh Sleep Quality Index and the Impact of Event Scale were used to assess sleep quality and symptoms of PTSD of 421 Chinese HCWs, respectively, from January 30 to March 2, 2020. The influencing factors of PTSD symptoms were identified by univariate analysis and multiple regression. Results: The incidence of HCWs getting PTSD symptoms were 13.2%. HCWs from high-risk areas had significantly poorer sleep quality (p < 0.001). Poor sleep quality was the risk factor of PTSD symptoms for HCWs from high-risk (p = 0.018) and low-risk areas (p < 0.001). Furthermore, non-medical staff were found to be the risk factor for PTSD symptoms only in low-risk areas. Discussion: HCWs in Hubei had poorer sleep quality. Non-medical HCWs from low-risk areas were associated with more severe PTSD symptoms. Mental health programs should be considered for HCWs, especially those who are often overlooked.

Predictive Value of First-Trimester Glycosylated Hemoglobin Levels in Gestational Diabetes Mellitus: A Chinese Population Cohort Study.

This study was aimed at exploring the predictive value of first-trimester glycosylated hemoglobin (HbA1c) levels in the diagnosis of gestational diabetes mellitus (GDM). A total of 744 pregnant women registered at the Peking University International Hospital between March 2017 and March 2019 were included in this study. Data on personal characteristics and biochemical indicators of the pregnant women were collected during the first trimester. The International Association of Diabetes and Pregnancy Study Groups has adopted specific diagnostic criteria as the gold standard for the diagnosis of GDM. Receiver operating characteristic (ROC) curve statistics were used to assess the predictive value of first-trimester HbA1c levels in the diagnosis of GDM. HbA1c levels in the first trimester were significantly higher in the GDM group than in the non-GDM group (5.23% ± 0.29% vs. 5.06 ± 0.28%, P < 0.05). The first-trimester HbA1c level was an independent risk factor for gestational diabetes. The area under the ROC curve (AUC) of HbA1c for GDM was 0.655 (95% confidence interval 0.620-0.689, P < 0.001). The positive likelihood ratio was the highest at HbA1c = 5.9%, sensitivity was 2.78, and specificity was 99.83%. There was no statistical difference in AUC between fasting blood glucose and HbA1c (P = 0.407). First-trimester HbA1c levels can be used to predict GDM. The risk of GDM was significantly increased in pregnant women with first-trimester HbA1c levels > 5.9%. There was no statistical difference between first-trimester HbA1c and fasting blood glucose levels in predicting GDM.

Building an Open Resources Repository for COVID-19 Research.

The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse (https://dataverse.harvard.edu/dataverse/2019ncov), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.