A Proposed Robust Computational Network Modelling to Optimally Investigate Gene Data

Background & Objective: In this work, we introduced a mathematical network model which follows on the recommendations of 3GPP to evaluate the downlink Long Term Evolution (LTE) network utilizing Strict Frequency Reuse (FR) scheme. The network modelling bases on the establishment phase and communications of the FR scheme. The user average coverage probability is derived and analysed under Rayleigh fading environment and furthermore the closed-form formulations of the performance are found using Gauss Quadratures. Through the Monte Carlo simulation, it is proved that the proposed analytical approach is more accurate than other approaches in the literature. Conclusion: Furthermore, this paper stated that the overall system can achieve the better performance with a higher number of Cell-Edge Users (CEUs), which contrasts with other works in the literature.

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Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study

BMJ Open ◽

10.1136/bmjopen-2020-045886 ◽

2021 ◽

Vol 11 (7) ◽

pp. e045886

Author(s):

Yiying Hu ◽

Jianying Guo ◽

Guanqiao Li ◽

Xi Lu ◽

Xiang Li ◽

...

Keyword(s):

Real World ◽

Case Fatality ◽

Contact Tracing ◽

Secondary Outcome ◽

Time Interval ◽

Fatality Rate ◽

Real World Data ◽

Network Modelling ◽

World Data ◽

Quantitative Evidence

ObjectivesThis study quantified how the efficiency of testing and contact tracing impacts the spread of COVID-19. The average time interval between infection and quarantine, whether asymptomatic cases are tested or not, and initial delays to beginning a testing and tracing programme were investigated.SettingWe developed a novel individual-level network model, called CoTECT (Testing Efficiency and Contact Tracing model for COVID-19), using key parameters from recent studies to quantify the impacts of testing and tracing efficiency. The model distinguishes infection from confirmation by integrating a ‘T’ compartment, which represents infections confirmed by testing and quarantine. The compartments of presymptomatic (E), asymptomatic (I), symptomatic (Is), and death with (F) or without (f) test confirmation were also included in the model. Three scenarios were evaluated in a closed population of 3000 individuals to mimic community-level dynamics. Real-world data from four Nordic countries were also analysed.Primary and secondary outcome measuresSimulation result: total/peak daily infections and confirmed cases, total deaths (confirmed/unconfirmed by testing), fatalities and the case fatality rate. Real-world analysis: confirmed cases and deaths per million people.Results(1) Shortening the duration between Is and T from 12 to 4 days reduces infections by 85.2% and deaths by 88.8%. (2) Testing and tracing regardless of symptoms reduce infections by 35.7% and deaths by 46.2% compared with testing only symptomatic cases. (3) Reducing the delay to implementing a testing and tracing programme from 50 to 10 days reduces infections by 35.2% and deaths by 44.6%. These results were robust to sensitivity analysis. An analysis of real-world data showed that tests per case early in the pandemic are critical for reducing confirmed cases and the fatality rate.ConclusionsReducing testing delays will help to contain outbreaks. These results provide policymakers with quantitative evidence of efficiency as a critical value in developing testing and contact tracing strategies.

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Metabolic pathways inferred from a bacterial marker gene illuminate ecological changes across South Pacific frontal boundaries

Nature Communications ◽

10.1038/s41467-021-22409-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Eric J. Raes ◽

Kristen Karsh ◽

Swan L. S. Sow ◽

Martin Ostrowski ◽

Mark V. Brown ◽

...

Keyword(s):

16S Rrna ◽

Metabolic Pathways ◽

Low Cost ◽

Marker Gene ◽

South Pacific ◽

Rrna Gene ◽

South Pacific Ocean ◽

Bacterial Marker ◽

Gene 16S Rrna ◽

Gene Data

AbstractGlobal oceanographic monitoring initiatives originally measured abiotic essential ocean variables but are currently incorporating biological and metagenomic sampling programs. There is, however, a large knowledge gap on how to infer bacterial functions, the information sought by biogeochemists, ecologists, and modelers, from the bacterial taxonomic information (produced by bacterial marker gene surveys). Here, we provide a correlative understanding of how a bacterial marker gene (16S rRNA) can be used to infer latitudinal trends for metabolic pathways in global monitoring campaigns. From a transect spanning 7000 km in the South Pacific Ocean we infer ten metabolic pathways from 16S rRNA gene sequences and 11 corresponding metagenome samples, which relate to metabolic processes of primary productivity, temperature-regulated thermodynamic effects, coping strategies for nutrient limitation, energy metabolism, and organic matter degradation. This study demonstrates that low-cost, high-throughput bacterial marker gene data, can be used to infer shifts in the metabolic strategies at the community scale.

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