Challenges of COVID-19 Case Forecasting in the US, 2020-2021 (preprint)

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naive baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making. Author SummaryAs SARS-CoV-2 began to spread throughout the world in early 2020, modelers played a critical role in predicting how the epidemic could take shape. Short-term forecasts of epidemic outcomes (for example, infections, cases, hospitalizations, or deaths) provided useful information to support pandemic planning, resource allocation, and intervention. Yet, infectious disease forecasting is still a nascent science, and the reliability of different types of forecasts is unclear. We retrospectively evaluated COVID-19 case forecasts, which were often unreliable. For example, forecasts did not anticipate the speed of increase in cases in early winter 2020. This analysis provides insights on specific problems that could be addressed in future research to improve forecasts and their use. Identifying the strengths and weaknesses of forecasts is critical to improving forecasting for current and future public health responses.

Xuanfei Baidu Decoction suppresses complement overactivation and ameliorates IgG immune complex-induced acute lung injury by inhibiting JAK2/STAT3/SOCS3 and NF-κB Signaling Pathway

Background The significant clinical efficacy of Xuanfei Baidu Decoction (XFBD) is proven in the treatment of patients with coronavirus disease 2019 (COVID-19) in China. However, the mechanisms of XFBD against acute lung injury (ALI) are still poorly understood. Methods In vivo, the mouse model of ALI was induced by IgG immune complexes (IgG-IC), and then XFBD (4g/kg, 8g/kg) were administered by gavage respectively. 24h after inducing ALI, the lungs were collected for histological and molecular analysis. In vitro, alveolar macrophages inflammation models induced by IgG-IC were performed and treated with different dosage of XFBD-Containing Serum to investigate the protective role and molecular mechanisms of XFBD. Results The results revealed that XFBD mitigated lung injury and significantly downregulated the production of pro-inflammatory mediators in lung tissues and macrophages upon IgG-IC stimulation. Notably, XFBD attenuated C3a and C5a generation, inhibited the expression of C3aR and C5aR and suppressed the activation of JAK2/STAT3/SOCS3 and NF-κB signaling pathway in lung tissues and macrophages induced by IgG-IC. Moreover, in vitro experiments, we verified that Colivelin TFA (CAF, STAT3 activator) and C5a treatment markedly elevated the IgG-IC-triggered inflammatory responses in macrophages and XFBD weakened the effects of CAF or C5a. Conclusion XFBD suppressed complement overactivation and ameliorated IgG immune complex-induced acute lung injury by inhibiting JAK2/STAT3/SOCS3 and NF-κB Signaling Pathway. These data contribute to understanding the mechanisms of XFBD in COVID-19 treatment. Graphical Image, graphical Schematic representation of proposed mechanism underlying the protective effects of XFBD on the IgG-IC-induced ALI. XFBD suppressed complement overactivation and protected against IgG immune complex-induced acute lung injury by inhibiting JAK2/STAT3/SOCS3 and NF-κB Signaling Pathway.

Diversity and independent evolutionary profiling of rodent-borne pathogens in tropical island, China (preprint)

The risk of emerging infectious diseases (EID) is increasing globally. More than 60% of EIDs worldwide are caused by animal-borne pathogens, and most viral pathogens are rodent-borne. This study aimed to characterise the virome and analyse the phylogenetic evolution and diversity of rodent-borne viruses in Hainan Province, China. We collected 588 anal and throat samples from rodents, combined them into 28 pools according to their species and location, and processed them for next-generation sequencing and bioinformatics analysis. The diverse viral reads closely related to mammals were assigned to 15 viral families. Molecular clues of the important rodent-borne viruses were further identified by polymerase chain reaction for phylogenetic analysis and annotation of genetic characteristics such as coronavirus, arenavirus, picornavirus. We identified a pestivirus in Leopoldoms edwardsi and two bocaviruses in Rattus andamanensis and Leopoldoms edwardsi from the national nature reserves of Jianfengling and Bangxi with low amino acid identity to known pathogens are proposed as the novel species, and their rodent hosts have not been previously reported to carry these viruses. These results expand our knowledge of viral classification and host range and suggest that there are highly diverse, undiscovered viruses that have evolved independently in their unique wildlife hosts in inaccessible areas, which may cause zoonosis if they cross their host barrier. Our virome and phylogenetic analyses of rodent-borne viruses provide basic data for the prevention and control of human infectious diseases caused by rodent-borne viruses in the subtropical area of China.

SARS-CoV-2 nucleic acid positive rate among close contacts of the patients infected with novel Coronavirus in China: a meta-analysis

The aim this meta-analaysis was to understand the current status of nucleic acid positivity rate of severe acute respiratory syndrome coronavirus (SARS-CoV-2) in close contacts of novel coronavirus-infected patients in China. The literature related to SARS-CoV-2 nucleic acid testing in close contacts of novel coronavirus-infected patients in China was searched in PubMed, EMbase, China Journal Full-text Data Base (CNKI), Wanfang Science and Technology Journal Full-text Database, and Veep Chinese Science and Technology Journal Full-text Database (VIP) from December 2019 to December 2020. 24 December 2019-2020. The quality of the literature was evaluated with reference to the revised American Agency for Healthcare Research and Quality (AHRQ) statement. StataSE15.0 software was used for meta-analysis, combined positive rates were calculated using the Freeman-Tukey double inverse sine conversion method, subgroup analysis was performed according to sex, age, infected person relationship, mode of infection and frequency of exposure, and sensitivity analysis and Egger's method was used to test for publication bias. Results A total of 11 publications were included, with a total sample size of 24 906 cases. The SARS-CoV-2 nucleic acid positivity rate in the close contact population of novel coronavirus-infected patients was 5.42% (95% CI: 3.57%-7.64%), and subgroup analysis showed that the positivity rate was 4.35% in males and 6.36% in females;the positivity rate was 5.88% in the 0-9 years group and 4.76% in the 10-59 years group. The positive rates were 5.88% for the 0-9 years group, 4.76% for the 10-59 years group and 8.73% for the =60 years group;13.42% for family members and 2.09% for others;11.44% for people living together, 9.90% for meals and 1.95% for other modes of infection;and 1.32%, 6.12% and 9.60% for occasional, normal and frequent contacts, respectively. The differences between the subgroups were statistically significant (?2 = 37.89 to 809.90, all P < 0.05). The sensitivity analysis suggested stable results and the Egger's test for publication bias was not statistically significant (t=0.93, P=0.376). Conclusion Close contacts of novel coronavirus-infected individuals in the Chinese region have a positive rate for SARS-CoV-2 nucleic acid.

Accounting Transparency, Fear Sentiment and the COVID-19 Epidemic: For Public Health Security and the Construction of an Early Warning System

A central issue of public health security and the construction of an early warning system is to establish a set of responsibility-oriented incentives and restraint mechanisms. This is closely related to the accounting transparency of the institutional environment and the fear sentiment of the individual's predicament. This study analyses the relationship between accounting transparency, fear sentiment, and COVID-19 through a VAR model analysis. The results show a significant and negative relationship between accounting transparency and daily new COVID-19 patients. In particular, accounting transparency has a negative impact on the increase in the number of people infected with a two-period lag, while the three-period lag in the number of new epidemics has a negative impact on accounting information. Second, accounting transparency has a positive impact on the increase in the search volume on COVID-19 within a three-period lag. After the three-period lag, the number of new epidemics has a positive impact on accounting information. Third, an increase in fear sentiment can be driven by the fear of COVID-19. Fourth, in the public health early warning system, according to the abovementioned time characteristics, the system arranges the emotional counseling, early warning incentives, and institutional constraints to be dealt with in the first 4 days. In addition, in the early warning target-oriented system setting, the parallel system helps to improve the early warning efficiency.

Topic2Labels: A framework to annotate and classify the social media data through LDA topics and deep learning models for crisis response

The abundant use of social media impacts every aspect of life, including crisis management. Disaster management needs real-time data to be used in machine learning and deep learning models to aid their decision making. Mostly the data that is newly generated from social media is unstructured and unlabeled. Current text classification models based on supervised deep learning models heavily rely on human-labeled data that very small size and imbalanced in the context of disasters, ultimately affecting the generalization of models. In this study, we propose Topic2labels (T2L) framework which provides an automated way of labeling the data through LDA (latent dirichlet allocation) topic modelling approach and utilize Bert (the bidirectional encoder representation from transformer) embeddings for construction of feature vector to be employed to classify the data contextually. Our framework consists of three layers. In the first layer, we adopt LDA to generate the topics from the data, and develop a new algorithm to rank the topics, and map the highest ranked dominant topic into label to annotate the data. In the second layer, we transform the labeled text into feature representation through Bert embeddings and in the third layer we leveraged deep learning models as classifiers to classify the textual data into multiple categories. Experimental results on crisis-related datasets show that our framework performs better in terms of classification performance and yields improvement as compared to other baseline approaches.

Identification of RPSA as a Potential Biomarker in Bronchoalveolar Lavage Fluid for Acute Respiratory Distress Syndrome (preprint)

Background: Acute respiratory distress syndrome (ARDS) is a life-threatening condition leading to severe pulmonary injuries, and proteomic analysis of bronchoalveolar lavage fluid (BALF) might elucidate potential biomarkers for diagnosis and targets for treatment of ARDS. Methods: Through iTRAQ analysis, we investigated paired BALF samples from three ARDS patients in the acute and recovery phases. The proteins sharing the same expression patterns between the two ARDS phases among different patients were determined as co-upregulated and co-downregulated proteins (CUDPs), and differentially expressed proteins (DEPs), whose fold change > 1.2 and P value < 0.05, were selected from CUDPs. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied to determine the enriched functions and pathways of the CUDPs. Protein-protein interaction (PPI) network was generated at STRING database, and hub genes were identified by the Cytoscape software. A549 cells were treated by lipopolysaccharide (LPS) to simulate alveolar epithelial cells in ARDS. Results: We identified 374 CUDPs and 53 DEPs. The GO analysis indicated that the most significantly enriched function was neutrophil mediated immunity response, and the KEGG analysis revealed that the 374 CUDPs were most significantly enriched in Coronavirus disease COVID-19 interaction. RPSA was discovered as the most top hub gene among DEPs, and was downregulated at protein levels during ARDS recovery. Moreover, we further confirmed that both RNA and protein level of RPSA increased upon inflammatory stimulation in vitro. Conclusion: Our results proposed RPSA as a candidate for biomarker and therapeutic target of ARDS.

Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach

During the recent pandemic of COVID-19, an increasing amount of information has been propagated on social media. This situational information is valuable for public authorities. Therefore, this study characterized the propagation scale of situational information types by harnessing the power of natural language processing techniques and machine learning algorithms. We observed that the length of the post has a positive correlation with type 1 information (announcements), and negative words were mostly used in type 5 information (criticizing the government), whereas anxiety-related words have a negative effect on the amount of retweeted type 0 (precautions) and type 2 (donations) information. This type of research study not only contributes to the situational information literature by comprehensively defining categories but also provides data-oriented practical insights into information so that management authorities can formulate response strategies after the pandemic. Our approach is one of its kind and combines Twitter content features, user features and LIWC linguistic features with machine learning algorithms to analyze the propagation scale of situational information, and it achieved 77% accuracy with SVM while classifying the information categories.

Can Social Media Data Be Utilized to Enhance Early Warning: Retrospective Analysis of the U.S. Covid-19 Pandemic (preprint)

ABSTRACT The U.S. needs early warning systems to help it contain the spread of infectious diseases. Conventional early warning systems use lab-test results or dynamic records to signal early warning signs. New early warning systems can supplement these data with indicators of public awareness like news articles and search queries. This study aims to explore the potential of utilizing social media data to enhance early warning of the COVID-19 outbreak. To demonstrate the feasibility, this study conducts a retrospective analysis and investigates more than 14 million related Twitter postings in the date range from January 20 to March 10, 2020. With the aid of natural language processing tools and machine learning classifiers, this study classifies each of these tweets into either a signal or a non-signal. In this study, a “signal” tweet implies that the user recognized the COVID-19 outbreak risk in the U.S. This study then proposes a parameter “signal ratio” to signal warning signs of the COVID-19 pandemic over periods. Results reveal that social media data and the signal ratio can detect the hazards ahead of the COVID-19 outbreak. This claim has been validated with a leading time of 16 days through the comparison to other referenced methods based on Google trends or media news.

The green B&B promotion strategies for tourist loyalty: surveying the restart of Chinese national holiday travel after COVID-19

This paper attempts to empirically analyze green/healthy B&B promotion strategies for tourism recovery after the first wave of COVID-19. The survey will be meaningful in the real world of B&B tourism recovery, and it was conducted during the first Chinese national holiday without travel restrictions. China was the first country to resume travel after COVID-19. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used for testing. The relationships observed among the green/healthy physical environment, well-being perception (WBP), tourist satisfaction (TS), and tourist loyalty (TL) provide a better understanding of how to support sustainable tourism recovery. Green/healthy B&B promotion strategies that focus on a green/healthy physical environment after the health crisis can also be employed in other countries and regions experiencing the same situation.

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

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

An Evaluation of Green Ryokans through a Tourism Accommodation Survey and Customer-Satisfaction-Related CASBEE–IPA after COVID-19 Pandemic

Following the outbreak of the COVID-19 pandemic, it became significant to study how to improve the customer satisfaction for Japanese tourist accommodations for restart and recovery in the future, and in preparation for the 2021 Japan Olympics. Therefore, the current paper attempts to evaluate ryokans through descriptive statistics from a tourism accommodation survey and customer-satisfaction-related comprehensive assessment system for built environment efficiency (CASBEE) importance–performance analysis (IPA). Through three progressive studies, three findings were obtained: (1) ryokans are more flexible than hotels, have strong anti-risk capabilities, and have received more and more attention from tourists and support from the Japanese government;(2) improvement strategies for customer satisfaction after COVID-19 were provided from IPA;and (3) a dynamic evaluation model of green ryokans was discussed and may be employed in other countries and regions experiencing the same situation.

Global pathways to sustainable development to 2030 and beyond (preprint)

Progress to-date towards the ambitious global 2030 agenda for sustainable development has been limited, and upheaval from the COVID-19 pandemic will further exacerbate the already significant challenges to Sustainable Development Goal (SDG) achievement. Here, we undertake a model-based global integrated assessment to characterise alternative pathways towards 36 time-bound, science-driven targets by 2030, 2050, and 2100. We show that it will be unlikely to jointly achieve socioeconomic and environmental targets by 2030, even under the most optimistic pathways and the least ambitious targets. Nonetheless, humanity can still avoid destabilisation of the Earth system and increase socioeconomic prosperity post-2030 via a Green Recovery pathway. A Green Recovery by mid- and end of the century requires reducing global population by 5% and 26%, empowering sustainable economic development by 32% and 52%, increasing education availability by 10% and 40%, reducing the total global fossil energy production by 36% and 80%, reducing agricultural land area by 7% and 10%, and promoting healthy and sustainable lifestyles by lowering consumption of animal-based foods (i.e., meat and dairy) by 39% and 50%, compared to the business-as-usual trajectories for 2050 and 2100, respectively. Our results show that the combination of these changes together towards extended, more ambitious goals by 2050 and 2100 is central to the transformative change needed to ensure that both people and planet prosper in medium- and long-term futures.

Comparing and Integrating US COVID-19 Data from Multiple Sources with Anomaly Detection and Repairing (preprint)

Over the past few months, the outbreak of Coronavirus disease (COVID-19) has been expanding over the world. A reliable and accurate dataset of the cases is vital for scientists to conduct related research and for policy-makers to make better decisions. We collect the United States COVID-19 daily reported data from four open sources: the New York Times, the COVID-19 Data Repository by Johns Hopkins University, the COVID Tracking Project at the Atlantic, and the USAFacts, then compare the similarities and differences among them. To obtain reliable data for further analysis, we first examine the cyclical pattern and the following anomalies, which frequently occur in the reported cases: (1) the order dependencies violation, (2) the point or period anomalies, and (3) the issue of reporting delay. To address these detected issues, we propose the corresponding repairing methods and procedures if corrections are necessary. In addition, we integrate the COVID-19 reported cases with the county-level auxiliary information of the local features from official sources, such as health infrastructure, demographic, socioeconomic, and environmental information, which are also essential for understanding the spread of the virus.

Spatiotemporal Dynamics, Nowcasting and Forecasting of COVID-19 in the United States (preprint)

Epidemic modeling is an essential tool to understand the spread of the novel coronavirus and ultimately assist in disease prevention, policymaking, and resource allocation. In this article, we establish a state of the art interface between classic mathematical and statistical models and propose a novel space-time epidemic modeling framework to study the spatial-temporal pattern in the spread of infectious disease. We propose a quasi-likelihood approach via the penalized spline approximation and alternatively reweighted least-squares technique to estimate the model. Furthermore, we provide a short-term and long-term county-level prediction of the infected/death count for the U.S. by accounting for the control measures, health service resources, and other local features. Utilizing spatiotemporal analysis, our proposed model enhances the dynamics of the epidemiological mechanism and dissects the spatiotemporal structure of the spreading disease. To assess the uncertainty associated with the prediction, we develop a projection band based on the envelope of the bootstrap forecast paths. The performance of the proposed method is evaluated by a simulation study. We apply the proposed method to model and forecast the spread of COVID-19 at both county and state levels in the United States.

Prognostic value of NT-proBNP in patients with severe COVID-19 (preprint)

The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China has been declared a public health emergency of international concern. The cardiac injury was dominate in the process. However, whether N terminal pro B type natriuretic peptide (NT-proBNP) predicted outcome of COVID-19 patients was unknown. The study initially enrolled 102 patients with severe COVID-19 pneumonia from a continuous sample. After screening out the ineligible cases, 54 patients were analyzed in this study. Results found that patients with higher NT-proBNP (above 88.64 pg/mL) level had more risks of in-hospital death. After adjusting for potential cofounders in separate modes, NT-proBNP presented as an independent risk factor of in-hospital death in patients with severe COVID-19.