Early detection of disease outbreaks and non-outbreaks using incidence data (preprint)

Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management. Here, we develop a general model, with no real-world training data, that accurately forecasts outbreaks and non-outbreaks. We propose a novel framework, using a feature-based time series classification method to forecast outbreaks and non-outbreaks. We tested our methods on synthetic data from a Susceptible-Infected-Recovered model for slowly changing, noisy disease dynamics. Outbreak sequences give a transcritical bifurcation within a specified future time window, whereas non-outbreak (null bifurcation) sequences do not. We identified incipient differences in time series of infectives leading to future outbreaks and non-outbreaks. These differences are reflected in 22 statistical features and 5 early warning signal indicators. Classifier performance, given by the area under the receiver-operating curve, ranged from 0.99 for large expanding windows of training data to 0.7 for small rolling windows. Real-world performances of classifiers were tested on two empirical datasets, COVID-19 data from Singapore and SARS data from Hong Kong, with two classifiers exhibiting high accuracy. In summary, we showed that there are statistical features that distinguish outbreak and non-outbreak sequences long before outbreaks occur. We could detect these differences in synthetic and real-world data sets, well before potential outbreaks occur.

An early warning indicator trained on stochastic disease-spreading models with different noises (preprint)

The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modeling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise, and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreak by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.

How Contingency Adjusts CSR in Tourism Industry: A Quasi Experiment in China (preprint)

Although numerous organizational researchers have acknowledged that COVID-19 shocks reduced the tourism industry’s financial performance, relevant literature remains scarce. Do tourism firms reduce corporate social responsibility (CSR) investments to decrease costs? The answer is unclear. This study fills the gap between stakeholder and cost stickiness theories. Based on a quasi-natural experiment of listed Chinese tourism companies from 2017 to 2021, the study finds that the COVID-19 shock caused tourism firms to increase strategic and decrease responsive CSR. In addition, tourism firms that adopted cost leadership strategies trimmed responsive CSR more than strategic CSR. Tourism firms with differentiated leadership strategies increased strategic and decreased responsive CSR. Tourism firms with higher levels of political connections increased responsive CSR, while tourism firms with higher organizational resilience increased strategic CSR. At the theoretical level, this study reveals the theoretical mechanism of the COVID-19 epidemic’s shock on tourism firms' adjustment of CSR from the perspective of cost stickiness. On a practical level, it helps inform tourism firms’ decision-making regarding CSR adjustments for sustainable development when they face widespread crisis scenarios.

A moderately higher time-in-range threshold improves the prognosis of type-2 diabetes patients complicated with COVID-19 (preprint)

After fully lifting coronavirus disease 2019 (COVID-19) pandemic control measures in mainland China in 12/2022, the incidence of COVID-19 has increased markedly, making it difficult to meet the general time-in-range (TIR) requirement. We investigated a more clinically practical TIR threshold and examined its association with the prognosis of COVID-19 patients with type-2 diabetes. Sixty-three type-2 diabetes patients complicated with COVID-19 were evaluated. Patient information included epidemiological and laboratory characteristics, treatment options and outcomes. The percentages of time-above-range (TAR), time-below-range (TBR) and TIR were calculated from intermittently scanned continuous glucose monitoring. The composite end point included a >20-day length of stay, intensive care unit admission, mechanical ventilation use, or death. TIR with thresholds of 80 to 190 mg/dL was significantly associated with favorable outcomes. An increase of 1% in TIR is connected with a reduction of 3.70% in the risk of adverse outcomes. The Youden index was highest when the TIR was 54.73%, and the sensitivity and specificity were 58.30% and 77.80%, respectively. After accounting for confounding variables, our analysis revealed that threshold target ranges (TARs) ranging from 200 mg/dL to 230 mg/dL significantly augmented the likelihood of adverse outcomes.The TIR threshold of 80 to 190 mg/dL has a comparatively high predictive value of the prognosis of COVID-19. TIR >54.73% was associated with a decreased risk of adverse outcomes. These findings provide clinically critical insights into possible avenues to improve outcomes for COVID-19 patients with type-2 diabetes.

Identification of Age-Related Characteristic Genes Involved in Severe COVID-19 Infection Among Elderly Patients using Machine Learning and Immune Cell Infiltration Analysis (preprint)

Background Elderly patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are at higher risk of severe clinical manifestation, extended hospitalization, and increased mortality. Those patients are more likely to experience persistent symptoms and exacerbate the condition of basic diseases with long COVID-19 syndrome. However, the molecular mechanisms underlying severe COVID-19 in the elderly patients remain unclear. Our study aims to investigate the function of the interaction between disease-characteristic genes and immune cell infiltration in patients with severe COVID-19 infection.Methods COVID-19 datasets (GSE164805 and GSE180594) and aging dataset (GSE69832) were obtained from the Gene Expression Omnibus (GEO) database. The combined different expression genes (DEGs) were subjected to Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Diseases Ontology (DO) functional enrichment analysis, Gene Set Enrichment Analysis (GSEA), machine learning, and immune cell infiltration analysis.Results GO and KEGG enrichment analysis revealed that the eight DEGs (IL23A, PTGER4, PLCB1, IL1B, CXCR1, C1QB, MX2, ALOX12) were mainly involved in inflammatory mediator regulation of TRP channels, coronavirus disease-COVID-19, and cytokine activity signaling pathways. Two-degree algorithm (LASSO and SVM-RFE) and correlation analysis showed that the seven DEGs upregulated the immune cells of macrophages M0/M1, memory B cells, gramma delta T cell, dendritic cell resting and master cell resisting.Conclusion Our study identified seven hallmark genes that can serve as disease-characteristic genes and target immune cells infiltrated in severe COVID-19 patients among the elderly population, which may contribute to the study of pathogenesis and the evaluation of diagnosis and prognosis in aging patients infected with severe COVID-19.

What Makes Locked-Down Students Happy: The Sense of Mattering and Subjective Well-Being

Subjective well-being (SWB) of university students who had to study off-campus due to the pandemic was investigated in this current study. Studies had reported that one of the most robust factors of SWB is the sense of mattering. While the sense of mattering is built upon social feedback, being locked down limited their source of social feedback to they can only develop their interpersonal mattering through their significant others whom they shared the dwelling place and their societal mattering through the 'society' they found in the social media. We purposively selected 82 participants to respond to our scales of mattering types and SWB. Among our inclusion criteria was to have a limited number of living partners (0-3) to make sure that their mattering was predominantly built from the social media feedback. The result of the multiple regression analysis suggested that despite their interpersonal mattering having a weaker contribution to their SWB, it was still a significant predictor of SWB when controlling for societal mattering. Thus, both types of mattering are still considered salient and robust predictors of SWB. Further limitations and suggestions are discussed.

Waterproofing performance of polypropylene – concrete wall of underground silo under combined compressive stress and water pressure

Safe, economical and high-quality storage of huge amount of grain for a longer duration under COVID-19 is a challenge and underground storage is a good alternative due to stable temperature, less cooling consumption and better pest control effect. However, the underground silo has very high requirement of waterproof and the performance of underground silo under combined compression and water pressure was rarely studied. In this study, a new composite structure, polypropylene – concrete wall (PPCW) for underground silo was proposed. Total three PPCWs with different size were manufactured to test the waterproofing features under joint effect of compression and hydropower of water. The strains, lateral displacement and cracking conditions of PPCWs were investigated. According to the experimental results, the PP board and concrete presented very good performance of interaction working under compression. The maximum water pressure of the specimens with stud spacing of 250mm increased by about 15.7% compared with that of the specimens with stud spacing of 350mm. The welding and strength of PP board has the greatest influence on the ultimate performance of PPCW. Based on the empirical coefficient method of concrete flat-slab and tested results, a new modified method was proposed to predict the bending moment at mid-span of PPCW by using an adjustment coefficient, Rm. Considering this experimental case only, the adopting a Rm = 0.64 could control the relative errors between test and analysis under 15.6%. Silo;Polypropylene;Composite;Bending moment;Adjustment coefficient.

Enhancing Food Supply Chain in Green Logistics with Multi-Level Processing Strategy under Disruptions

Food supply chains (FSCs) have long been exposed to environmental variability and shock events caused by various economic, political, and infrastructural factors. The outbreak of the COVID-19 pandemic has further exposed and identified the vulnerability of FSCs, and promoted integrated optimization approaches for building resilience. However, existing works focusing on general supply chains (SCs) and FSCs have not been fully aware of the distinct characteristics of FSCs in green logistics, i.e., the expiration of fresh products. In reality, perishable food materials can be processed into products of different processing levels (i.e., multi-level processing) for longer shelf lives, which can serve as a timely and economic strategy to increase safety stocks for mitigating disruption risks. Motivated by this fact, we study the problem of enhancing FSC with a multi-level processing strategy. An integrated location, inventory, and distribution planning model for a multi-echelon FSC under COVID-19-related disruptions is formulated to maximize the total profit over a finite planning horizon. Specifically, a two-stage stochastic programming model is presented to hedge against disruption risks, where scenarios are generated to characterize geographical impact induced by source-region disruptions. For small-scale problems, the model can be solved with commercial solvers. To exactly and efficiently solve the large-scale instances, we design an integer L-shaped method. Numerical experiments are conducted on a case study and randomly generated instances to show the efficiency of our model and solution method. Based on the case study, managerial insights are drawn.

Uncovery of the action mechanisms and major bioactive compounds responsible for the protective effects of Toujie Quwen Granules on COVID-19 based on network pharmacology and molecular docking

Objective: To explore the mechanism of action and active constituents of Toujie Quwen Granules (TJQWG) for treatment of COVID-19, and provide a scientific reference for its clinical application.

Ultraminiature optical fiber-tip directly-printed plasmonic biosensors for label-free biodetection

Miniaturization of biosensors has become an imperative demand because of its great potential in in vivo biomarker detection and disease diagnostics as well as the point-of-care testing for coping with public health crisis, such as the coronavirus disease 2019 pandemic. Here, we present an ultraminiature optical fiber-tip biosensor based on the plasmonic gold nanoparticles (AuNPs) directly printed upon the end face of a standard multimode optical fiber at visible light range. An in-situ precision photoreduction technology is developed to additively print the micropatterns of size-controlled AuNPs. The AuNPs reveal distinct localized surface plasmon resonance, whose peak wavelength provides an ideal spectral signal for label-free biodetection. The fabricated optical fiber-tip plasmonic biosensor can not only detect antibody, but also test SARS-CoV-2 mimetic DNA sequence at the concentration level of 0.8 pM. Such an ultraminiature fiber-tip plasmonic biosensor offers a cost-effective biodetection technology for a myriad of applications ranging from point-of-care testing to in vivo diagnosis of stubborn diseases.

Visual detection and differentiation of porcine epidemic diarrhea virus wild−type strains and attenuated vaccine strains using CRISPR/Cas13a-based lateral flow strip

Porcine epidemic diarrhea virus (PEDV) is an enteric coronavirus that causes acute watery diarrhea and vomiting in unweaned piglets. Infections result in high mortality and serious economic losses to the swine industry. PEDV attenuated vaccine does not completely protect against all mutant wild-type strains, and PEDV infection can periodically occur. A sensitive, accurate, and simple detection method for PEDV is needed to reduce the occurrence of the disease. In this study, the CRISPR/Cas13a system was combined with recombinase aided amplification to develop a rapid diagnostic method to distinguish PEDV wild-type strains from attenuated vaccine strains. The method is based on isothermal detection at 37°C. The results are used for visual readout. The assay had high sensitivity and specificity, with a detection limit of 101 copies/μL for the gene of interest, and no cross-reactivity with other pathogens. The Cas13a detection worked well with clinical samples. This visual, sensitive, and specific nucleic acid detection method based on CRISPR/Cas13a should be a powerful tool for detecting PEDV.

How can age-based vaccine allocation strategies be optimized? A multi-objective optimization framework

Human life is deeply influenced by infectious diseases. A vaccine, when available, is one of the most effective ways of controlling the spread of an epidemic. However, vaccine shortage and uncertain vaccine effectiveness in the early stage of vaccine production make vaccine allocation a critical issue. To tackle this issue, we propose a multi-objective framework to optimize the vaccine allocation strategy among different age groups during an epidemic under vaccine shortage in this study. Minimizing total disease onsets and total severe cases are the two objectives of this vaccine allocation optimization problem, and the multistage feature of vaccine allocation are considered in the framework. An improved Strength Pareto Evolutionary Algorithm (SPEA2) is used to solve the optimization problem. To evaluate the two objectives under different strategies, a deterministic age-stratified extended SEIR model is developed. In the proposed framework, different combinations of vaccine effectiveness and vaccine production capacity are investigated, and it is identified that for COVID-19 the optimal strategy is highly related to vaccine-related parameters. When the vaccine effectiveness is low, allocating most of vaccines to 0–19 age group or 65+ age group is a better choice under a low production capacity, while allocating most of vaccines to 20–49 age group or 50–64 age group is a better choice under a relatively high production capacity. When the vaccine effectiveness is high, a better strategy is to allocate vaccines to 65+ age group under a low production capacity, while to allocate vaccines to 20–49 age group under a relatively high production capacity.

Perceived Stigma among Discharged Patients of COVID-19 in Wuhan, China: a Latent Profile Analysis (preprint)

Background Perceived stigma has greatly influenced the life quality of the COVID-19 patients who recovered and were discharged (RD hereafter). It is essential to understand COVID-19 stigma of RD and its related risk factors. The current study aims to identify the characteristics of perceived COVID-19 stigma in RD using latent profile analysis (LPA), to explore its psycho-social influencing factors, and to determine the cut-off point of the stigma scale using receiver operating characteristic (ROC) analysis. Methods A cross-sectional study was conducted among COVID-19 RD in 13 communities in Jianghan District, Wuhan City, Hubei Province, China from June 10 to July 25, 2021, enrolling total 1297 participants. Data were collected on demographic characteristics, COVID-19 perceived stigma, post-traumatic stress disorder (PTSD), anxiety, depression, sleep disorder, fatigue, resilience, social support, and peace of mind. LPA was performed to identify different profiles of perceived COVID-19 stigma level. Univariate analysis and multinominal logistic regression analysis were conducted to explore the influencing factors in different profiles. ROC analyses was carried out to identify the cut-off value of perceived stigma. Results Among the participants, three profiles of perceived stigma were identified: “low perceived COVID-19 stigma” (12.8%), “moderate perceived COVID-19 stigma” (51.1%), and “severe perceived COVID-19 stigma” (36.1%). Multinominal logistic regression analysis revealed that older age, living with other people, anxiety, and sleep disorder were positively associated with moderate perceived COVID-19 stigma, while higher educational level was negatively associated with moderate perceived COVID-19 stigma. Female, older age, living with other people, anxiety, and sleep disorder were positively associated with severe perceived COVID-19 stigma, while higher educational level, social support, and peace of mind were negatively associated with severe perceived COVID-19 stigma. ROC curve of the Short Version of COVID-19 Stigma Scale(CSS-S) for screening perceived COVID-19 stigma showed that the optimal cut-off value was ≥20. Conclusion The study focuses on the issue of perceived COVID-19 stigma and its psycho-socio influencing factors. It provides evidence for implementing relevant psychological interventions to COVID-19 RD.

Patients with Advanced Pancreatic & Biliary Cancer Appear More Vulnerable to SARS-CoV-2 Omicron Variant: An Observational Study during the COVID-19 Outbreak in Shanghai (preprint)

Background:There is a great controversy about lethal effect of Omicron Variant on vulnerable populations and the measure of full-open or zero community transmission policy. Thus, we designed an observational study to evaluate the outcomes of Omicron-infected patients with pancreatic & biliary cancer (the so-called “King of Cancer”) in order to provide potential evidence for the most appropriate strategy to counter Omicron transmission in Shanghai.Methods: Omicron infected patients with advanced pancreatic & biliary cancer were enrolled from April 15 to May 31, 2022. Four groups were set in this study: Group 1, Omicron-infected cancer patients (N=4); Group 2, non-infected cancer patients (N=4); Group 3, infected non-cancer-afflicted subjects (N=4); Group 4, non-infected non-cancer-afflicted subjects (N=4). On Day 0, 7 and 14 after infection, the blood samples were dynamically collected from all subjects. The primary endpoints were disease severity and survival. Results:By the endpoints in this observational study, Patients No. 2, 3 and 4 died separately at Day 11, 25 and 13 after viral infection, all of whom were patients with advanced cancer, with the death rate up to 75%. Group 1 presented an overall T cell exhaustion status compared with other groups with obviously lower T cell populations and higher B cell% and CD4+T/CD8+T ratio (P<0.05). Time-course cytokine monitoring results showed that IL-1β was significantly decreased in Group 1 (P<0.05) and generally kept at a low level without obvious fluctuation. IL-6 was markedly increased in infected cancer patients (P<0.01), but remained a low level and had no apparent change during the whole infection process in non-cancer-afflicted subjects. Furthermore, several inflammatory parameter indexes indicated a tight association of Omicron infection with disease course and prognosis of Omicron-infected cancer patients.Conclusions:For patients with advanced pancreatic & biliary cancer, the strict & comprehensive control strategy for COVID-19 epidemic in Shanghai provided a guarantee of low infection and death rate. Conclusively, this policy shall be persisted upon the consideration of the welfare of vulnerable populations.

Changing characteristics of the industrial correlation and economic contribution of air transport in China: An input–output analysis

To investigate changing characteristics of the industrial correlation and economic contribution of air transport in China in different periods, the industry linkage effect and the industry ripple effect of air transport were assessed based on an input–output analysis over the period 2007–2020. Taking the air transport sector as an exogenous variable, the production-inducing effect and the supply shortage effect were calculated, and their changing characteristics were analyzed. The result shows that China's air transport industry is gradually changing its close linkages with secondary industries, and is instead strengthening its interaction with some tertiary industries. Before the COVID-19 outbreak in 2020, the production-inducing multiplier of air transport was trending downwards and the supply shortage multiplier was trending upwards. After the COVID-19 outbreak, China's air transport industry was hit hard and both the production-inducing effect and the supply shortage effect decreased significantly. The results of this paper are of great significance in the evaluation of the industrial correlation and economic contribution of air transport and can serve as a reference for formulating policies and improving the management of the air transport industry.

Efficacy and safety of the hyaluronic acid inhibitor Hymecromone for the treatment of COVID-19: study protocol for a single-centre, randomized, controlled, Double-blind Clinical trial (preprint)

IntroductionHyaluronic acid (HA) is one of the main components of glycosaminoglycan (GAG) in proteoglycans. Among patients with novel coronavirus pneumonia, the serum HA content of severe patients was significantly higher than that of mild patients. Therefore, hyaluronic acid inhibitors have the potential to be the treatment of novel coronavirus pneumonia. This study plans to carry out a study on the optimization of the hyaluronic acid inhibitor Hymecromone in the treatment of COVID-19 to improve the therapeutic effect.Methods and analysisThis is a single-center, randomized, parallel controlled, double-blind clinical trial designed to evaluate the efficacy and safety of hymecromone tablets in subjects who confirmed to be infected by the SARS-CoV-2 virus and diagnosed as mild or moderate novel coronavirus pneumonia in China. The subjects in the experimental arm shall receive necessary routine treatment and hymecromone tablets while the control arm shall receive placebo. The study aims to compare the proportion of subjects in the experimental group and the control group who developed disease progression within 28 days after initial treatment. Meanwhile, all subjects will be monitored for safety constantly during the whole study phases.Ethics and disseminationThe study protocol was approved by the Ethics Committee of Zhongshan Hospital Fudan University (identifiers: Clinical Ethical Approval No. B2022-251R).Trial registrationClinicalTrails.org, NCT05386420. Registered 24 May 2022, https://clinicaltrials.gov/ct2/show/NCT05386420Strengths and limitations of this studyThis is one of the first prospective randomized controlled double-blind studies of the efficacy and safety of the hyaluronic acid inhibitor Hymecromone for the treatment of COVID-19. This study will be an innovative clinical intervention strategy, and is expected to provide an effective new treatment plan for the clinical treatment of severe infection with COVID-19. The limitation is it is a single center study, it might need more centers cases to be further external validated.INTRODUCTION

Birds carry a diversity of significant viruses in their virome (preprint)

Birds can carry and transmit viruses to humans and other animals. Thus, understanding the viral community hosted by birds could help us predict future outbreaks of human disease. A recent metagenomics study took a broad look at the viruses found in the gut of wild and captive birds. The dataset included samples from over 3,000 birds that represented over 87 species and 10 different phylogenetic orders and the researchers characterized genomes from numerous viral families including astroviruses, coronaviruses, parvoviruses, and adenoviruses. Examining trends, they found that wild birds had higher viral diversity than captive birds. There was also evidence of potential cross-species transmission between wild birds and domestic poultry. Further analysis of the viral genomic sequences revealed differences in virus distribution patterns between wild and captive birds. Different phylogenetic orders of birds and geographic sites also had distinct distribution patterns. Interestingly, there were no significant differences in virus distribution patterns between migratory and resident birds. While further studies are needed to explore the diversity and potential pathogenicity of these viruses in more detail, this study expanded our understanding of viral diversity in birds.

A new polysaccharide platform constructs self-adjuvant nanovaccines to enhance immune responses (preprint)

BackgroundNanovaccines have shown the promising potential in controlling and eradicating the threat of infectious diseases worldwide. There has been a great need in developing a versatile strategy to conveniently construct diverse types of nanovaccines and induce potent immune responses. To that end, it is critical for obtaining a potent self-adjuvant platform to assemble with different types of antigens into nanovaccines. ResultsIn this study, we identified a new natural polysaccharide from the rhizomes of Bletilla striata (PRBS), and used this polysaccharide as a platform to construct diverse types of nanovaccines with potent self-adjuvant property. In the construction process of SARS-CoV-2 nanovaccine, PRBS molecules and RBD protein antigens were assembled into ~300 nm nanoparticles by hydrogen bond. For HIV nanovaccine, hydrophobic effect dominantly drove the co-assembly between PRBS molecules and Env expression plasmid into ~350 nm nanospheres. Importantly, PRBS can potently activate the behaviors and functions of multiple immune cells such as macrophages, B cells and dendritic cells. Depending on PRBS-mediated immune activation, these self-adjuvant nanovaccines can elicit significantly stronger antigen-specific antibody and cellular responses in vivo, in comparison with their corresponding traditional vaccine forms. Moreover, we also revealed the construction models of PRBS-based nanovaccines by analyzing multiple assembly parameters such as bond energy, bond length and interaction sites.  ConclusionsPRBS, a newly-identified natural polysaccharide which can co-assemble with different types of antigens and activate multiple critical immune cells, has presented a great potential as a versatile platform to develop potent self-adjuvant nanovaccines.