Real Time Viral Sub-Strains Discovery in Emerging Infectious Disease Situation – The African Perspective
Bibliographic record
Abstract
Abstract Background: The increased number of accessible genomes has prompted large-scale comparative studies for discerning evolutionary knowledge of infectious diseases, but challenges such as non-availability of close reference sequence(s), incompletely assembled or large number of genomes, preclude real time multiple sequence alignment and sub-strain(s) discovery. This paper introduces a cooperatively inspired open-source framework, for intelligent mining of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genomes. We situate this study within the African context, to drive advancement on state-of-the-art, towards intelligent infectious disease characterization and prediction. The outcome is an enriched Knowledge Base, sufficient to provide deep understanding of the viral sub-strains’ identification problem. We also open investigation by gender, which to the best of our knowledge has been ignored in related research. Data for the study came from the Global Initiative on Sharing All Influenza Data database (https://gisaid.org) and processed for precise discovery of viral sub-strains transmission between and within African countries. To localize the transmission route(s) of each isolate excavated and provide appropriate links to similar isolate strain(s), a cognitive solution was imposed on the genome expression patterns discovered by unsupervised self-organizing map (SOM) component planes visualization. The Freidman-Nemenyi’s test was finally performed to validate our claim. Results: Evidence of inter- and intra-genome diversity was noticed. While some isolates (or genomes) clustered differently, implying different evolutionary source (or high-diversity), others clustered closely together, indicating similar evolutionary source (or less-diversity). SOM component planes analysis revealed multiple sub-strains patterns, strongly suggesting local- or intra-community and country to country transmissions. Cognitive maps of both male and female isolates revealed multiple transmission routes. Freidman’s test results showed highly significant difference (p<0.01) among the various isolate groups. Nemenyi’s test revealed groups that differed in their isolates.Conclusion: The proposed framework offers explanations to SARS-CoV-2 diversity and provides real time identification to disease transmission routes, as well as rapid decision support for facilitating inter- and intra-country contact tracing of infected case(s). Intermediate data produced in this paper are helpful to enrich the genome datasets for intelligent characterization and prediction of COVID-19 and related pandemics, as well as the construction of intelligent device for accurate infectious disease monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".