SORMAS-Stats: A Novel Statistical Application for Real-time Estimation of Epidemiological Parameters of Communicable Diseases using Contact-tracing Data. (Preprint)

BACKGROUND The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in epidemic response. It consists of documentation, linkage and follow-up of cases, contacts, and events. To allow SORMAS users to visualise, compute key surveillance indicators and estimate epidemiological parameters from such a network data in real time, we developed the SORMAS Statistics (SORMAS-Stats) application. OBJECTIVE The aim of this study is to describe the key visualisations, surveillance indicators and epidemiological parameters implemented in the SORMAS-Stats application, and illustrate the application of SORMAS-Stats to COVID-19 outbreak response. METHODS Based on literature review and user requests, we included the following visualisation and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number (Rt), dispersion parameter (k) and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting a lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptoms onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. We applied the Markov Chain Monte Carlo approach and estimated Rt using the incidence data and the observed SI data, computed from the transmission network data. RESULTS Using COVID-19 contact tracing data of confirmed cases reported between July 31, and October 29, 2021 in Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63570 nodes comprising 1.75% (1115/63570) events, 19.59% (12452/63570) case persons, and 78.66% (50003/63570) exposed persons, 1238 infector-infectee pairs, 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with best fit to the observed SI data was lognormal distribution with mean 4.32 days (95% CI, 4.10–4.53 days). We estimated the dispersion parameter, k of 21.11 (95% CI, 7.57–34.66) and a reproductive number, R of 0.9 (95% CI, 0.58–0.60). The weekly estimated Rt values ranged from 0.80 to 1.61. CONCLUSIONS We provide an application for real-time estimation of epidemiological parameters, which are essential for informing outbreak response strategies. These estimates are commensurate with findings from previous studies. SORMAS-Stats application would greatly assist public health authorities in the regions using SORMAS or similar applications by providing extensive visualisations and computation of surveillance indicators.

Based on Network Pharmacology to Explore the Mechanisms of Radix Astragali Combined with Prepared Radix Rehmanniain for Treating Osteoporosis

Background: With the improvement of people's living standards, the aging population in China has gradually increased, and the treatment of osteoporosis (OP) has become a major problem afflicting the medical field. Radix Astragali(RA) and Prepared Radix Rehmannia(PRR) are commonly used Chinese herbal medicines. The combination of them can achieve a synergistic effect in the treatment of osteoporosis. However, its mechanism of action remains uncertain.Objective: This study aims to investigate the possible molecular mechanism of RA combined with PRR in the treatment of OP using an integrated strategy of network pharmacology and experimental validation.Methods: The active ingredients of RA combined with PRR were searched and screened by TCMSP database, and the targets of active ingredients were predicted and supplemented by TCMSP and SwissTargetPrediction databases. The target genes related to OP diseases were searched in GeneCards and OMIM comprehensive databases. The intersection targets of drugs and diseases were imported into String database to obtain the interaction information of intersection target genes. Cytoscape3.7.1 software was used to construct the protein interaction network diagram and "drug component-target-disease" network diagram. Then DAVID database was used for GO gene enrichment analysis and KEGG metabolic pathway analysis. Finally, in vivo experiments were also performed to validate the findings of network pharmacology.Results: A total of 98 active components of RA combined with PRR were finally retrieved and integrated into the TCMSP database, 1700 target genes related to OP were obtained through the disease database, 149 gene targets were obtained by taking the intersection of disease genes, and drug targets, 122 core targets and 514 interaction relationships were obtained after protein interaction network and topology analysis. GO analysis and KEGG pathway enrichment analysis showed that RA combined with PRR intervention for OP mainly through multiple pathways such as PI3K-Akt, MAPK, TNF, Rap1, and Toll-like receptors. In addition, in vivo experiments confirmed that RA combined with PRR could significantly increase bone mineral density, reduce bone spacing, improve bone tissue structure, and improve osteoporosis in ovariectomized rats.Conclusion: In this study, the network pharmacological approach was used to reveal the potential targets and key signal pathways of RA combined with PRR in treating osteoporosis. This study was also verified by animal experiments, which provided a reliable basis for clinical application.

Comprehensive Analysis of Prognostic Alternative Splicing Signature Reveals Recurrence Predictor for Papillary Thyroid Cancer

BackgroundAlternative splicing (AS) plays a key role in the diversity of proteins and is closely associated with tumorigenicity. The aim of this study was to systemically analyze RNA alternative splicing (AS) and identify its prognostic value for papillary thyroid cancer (PTC).MethodsAS percent-splice-in (PSI) data of 430 patients with PTC were downloaded from the TCGA SpliceSeq database. We successfully identified recurrence-free survival (RFS)-associated AS events through univariate Cox regression, LASSO regression and multivariate regression and then constructed different types of prognostic prediction models. Gene function enrichment analysis revealed the relevant signaling pathways involved in RFS-related AS events. Simultaneously, a regulatory network diagram of AS and splicing factors (SFs) was established.ResultsWe identified 1397 RFS-related AS events which could be used as the potential prognostic biomarkers for PTC. Based on these RFS-related AS events, we constructed a ten-AS event prognostic prediction signature that could distinguish high-and low-risk patients and was highly capable of predicting PTC patient prognosis. ROC curve analysis revealed the excellent predictive ability of the ten-AS events model, with an area under the curve (AUC) value of 0.889; the highest prediction intensity for one-year RFS was 0.923, indicating that the model could be used as a prognostic biomarker for PTC. In addition, the nomogram constructed by the risk score of the ten-AS model also showed high predictive efficiency for the prognosis of PTC patients. Finally, the constructed SF-AS network diagram revealed the regulatory role of SFs in PTC.ConclusionThrough the limited analysis, AS events could be regarded as reliable prognostic biomarkers for PTC. The splicing correlation network also provided new insight into the potential molecular mechanisms of PTC.

278 Understanding Biosecurity Perceptions in Pennsylvania Pig Producers

The group mental model of swine-related biosecurity for producers and experts was assessed and compared using network analysis. The proper implementation of biosecurity plans reduces the risk of biological hazards that could cripple the industry. Recently collected survey data show producer motivation to adopt a biosecurity protocol is not driven solely by the value of the operation (Hines and Falcone, unpublished). Other motivating factors exist for how producers perceive risk relating to biosecurity management. To identify how pig producers and experts conceptualize biosecurity, open-ended survey questions were asked. Survey responses (n = 123) were coded using a newly developed codebook. Intercoder reliability was established using Krippendorff’s a. Code co-occurrence was used to build a network diagram showing producer and expert mental models, or depiction of the interdependent relationships among values, beliefs, behavior, and cognitive processes of decision making. Analyses of code co-occurrence revealed differences between producers and experts. The results suggest PA-based producers think of biosecurity relating to the protection of their property (ie. inward protection) which was closely associated with limiting access of “outsiders.” Also, the mental model diagram suggests producers think about biosecurity more broadly due to less clustering of ideas. Whereas experts think about biosecurity more specifically relating to two to three themes. Specifically, the expert biosecurity diagram revealed record keeping as an important component of biosecurity, which was strongly related to how experts think about cleanliness and limiting outsider access. Regarding strategies to address biohazard risks, both producers and experts recognize several options. However, experts proved to have stronger connections between concepts. Specifically, the diagrams revealed experts see all strategies as connected. From an expert perspective, strategies to address biohazard risks should be implemented simultaneously. These findings are the first step to designing communication to bridge the gaps between expert and producer understanding of biosecurity.

Distribution of diabetic retinopathy in diabetes mellitus patients and its association rules with other eye diseases

The study aims to explore the distribution characteristics and influencing factors of diabetic retinopathy (DR) in diabetes mellitus (DM) patients and association rules of eye diseases in these patients. Data were obtained from 1284 DM patients at Henan Provincial People’s Hospital. Association rules were employed to calculate the probability of the common occurrence of eye-related diseases in DM patients. A web visualization network diagram was used to display the association rules of the eye-related diseases in DM patients. DR prevalence in people aged < 40 years (≥ 58.5%) was higher than that in those aged 50–60 years (≤ 43.7%). Patients with DM in rural areas were more likely to have DR than those in urban areas (56.2% vs. 35.6%, P < 0.001). DR prevalence in Pingdingshan City (68.4%) was significantly higher than in other cities. The prevalence of DR in patients who had DM for ≥ 5 years was higher than in other groups (P < 0.001). About 33.07% of DM patients had both diabetic maculopathy and DR, and 36.02% had both diabetic maculopathy and cataracts. The number of strong rules in patients ≥ 60 years old was more than those in people under 60 in age, and those in rural areas had more strong rules than those in urban areas. DM patients with one or more eye diseases are at higher risks of other eye diseases than general DM patients. These association rules are affected by factors such as age, region, disease duration, and DR severity.

Research on the coordination mechanism of major industrial project engineering and construction multi-agents based on structural holes theory

Industry is an important pillar of the national economy. Industrial projects are the most complex and difficult projects to control in the construction industry, and major industrial projects are even more complex and difficult to control. Multi-agent coordination is one of the core issues of industrial projects. Based on an analysis of the engineering and construction chains and agent relationships and agent networks of industrial projects, a complex network of the engineering and construction agents of industrial projects is established, and the complex network structural holes theory is applied to study the nonrepeated relationships among agents in industrial projects. Assuming agents are linked through contract relations and the most critical contract index between the agents in the contract amount, through structural hole analysis considering the EPC and PMC model, the aggregate constraint list is obtained, 2D network diagram and 3D network diagram are shown. According to the aggregate constraint value, the EPC contractor with the minimum aggregate constraint value and the project management company with the minimum aggregate constraint value are the critical agent in EPC and PMC model. By analyzing the complex network comprising different models of industrial projects, it is concluded that the characteristics of the agent maintain an advantage in competition, the coordination mechanism of the integration of agent interests, and multi-agent relations are considered to solve the multi-agent coordination problem in major industrial projects.

An Uncompleted Federal Government Infrastructure (Project): An Anathema to Economic Diversification

Infrastructure development is the hub of economic diversification, for any nation to grow economically; it must be seriously dependent on its iron and steel development. The construction of the Ajaokuta iron and steel industry can be dated and traced to the year 1979 and as of 1994, the project was near completion with 98% of the project almost completed. As of 2018, the project is yet to be completed. The gap between 1979 through 1994 to 2018 gives the Nation a whole cause to worry thereby implying whether the delay in the work is due to the incompetency of the contractors in question or is it due to bad governance and suspected looting. This paper, therefore, applies the use of the popular Critical Path Method (CPM) in monitoring the progress of the yet-to-be-completed Ajaokuta project focusing on its possible network diagram, earliest and latest time of completion.

Study of the Active Components and Molecular Mechanism of Tripterygium wilfordii in the Treatment of Diabetic Nephropathy

We aimed to explore the active ingredients and molecular mechanism of Tripterygium wilfordii (TW) in the treatment of diabetic nephropathy (DN) through network pharmacology and molecular biology. First, the active ingredients and potential targets of TW were obtained through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and related literature materials, and Cytoscape 3.7.2 software was used to construct the active ingredient-target network diagram of TW. Second, the target set of DN was obtained through the disease database, and the potential targets of TW in the treatment of DN were screened through a Venn diagram. A protein interaction network diagram (PPI) was constructed with the help of the String platform and Cytoscape 3.7.2. Third, the ClueGO plug-in tool was used to enrich the GO biological process and the KEGG metabolic pathway. Finally, molecular docking experiments and cell pathway analyses were performed. As a result, a total of 52 active ingredients of TW were screened, and 141 predicted targets and 49 target genes related to DN were identified. The biological process of GO is mediated mainly through the regulation of oxygen metabolism, endothelial cell proliferation, acute inflammation, apoptotic signal transduction pathway, fibroblast proliferation, positive regulation of cyclase activity, adipocyte differentiation and other biological processes. KEGG enrichment analysis showed that the main pathways involved were AGE-RAGE, vascular endothelial growth factor, HIF-1, IL-17, relaxin signalling pathway, TNF, Fc epsilon RI, insulin resistance and other signaling pathways. It can be concluded that TW may treat DN by reducing inflammation, reducing antioxidative stress, regulating immunity, improving vascular disease, reducing insulin resistance, delaying renal fibrosis, repairing podocytes, and reducing cell apoptosis, among others, with multicomponent, multitarget and multisystem characteristics.

A Blowout Accident Causative Model of Hydrogen Sulfide Oil and Gas Wells using DEMATEL and ISM Algorithms

Blowout is one of the most serious accidents in the drilling process of hydrogen sulfide (H2S) oil and gas wells, often accompanied by the leakage of H2S and other toxic gases, which easily causes casualties and huge economic and environmental losses. Therefore, this article uses DEMATEL and ISM hybrid algorithms to establish a blowout accident-causing network model for oil and gas wells with H2S content, thus strengthening the risk management. In this model, firstly, the general causative factors of blowout accidents are extracted by accident statistics. Secondly, expert knowledge is adopted to determine the correlation matrix among factors. Thirdly, based on the DEMATEL algorithm, the degree of the relationship among the factors is analyzed. The importance degree (centrality) of each factor and its status as well as role (causality) in the accident-causing system are given. Finally, the ISM algorithm is used to classify the factors and establish an accident-causing network diagram with hierarchical relationship. The proposed model has been applied in a gas field containing H2S in East Sichuan, China. The results show that causative factors of blowout accidents can be divided into cause group and effect group according to the influence relationship among them. The cause group implies the meaning of the causative factors, and the effect group denotes the meaning of the causative factors. Hence, it would be necessary to control and pay great attention to the cause group factors beforehand. The key causative factors of blowout accidents are geological exploration technology, safety monitoring facilities and on-site safety culture, which belong to the cause group and are at the basic level of the accident-causing network diagram. This model has provided effective decision-making guidance for HSE work in gas field and reduced the incidence of blowout accidents. This model uses a combination of qualitative and quantitative methods to analyze the causes of blowout accidents, not only considering the relationships between factors and accidents, but also considering the relationships between factors and factors. As a result, it provides decision-making basis for the prevention and control of blowout accidents in H2S oil and gas wells.