A Hybrid Outbreak Detection using Ontology-based Data Collection from Social Media
Bibliographic record
Abstract
Rapidly spreading diseases pose a significant threat, leading to substantial loss of life and economic devastation, as seen in the global COVID-19 outbreaks. Developing disease prediction models is crucial for preemptive pandemic control and minimizing their impact. As internet accessibility grows through computers and mobile devices, social media platforms provide a direct conduit to disseminate vital health information to the public. Unlike traditional methods that rely on bureaucratic channels, these platforms offer accurate and timely information distribution. We propose a framework that employs ontology to identify these symptoms and gather relevant tweets. Subsequently, the XGBoost-BiLSTM hybrid model harnesses this data to predict the count of infected cases. This hybrid model capitalizes on XGBoost’s prowess in handling limited dataset sizes, a prevalent challenge during outbreaks with insufficient time series data. Moreover, it enriches data for BiLSTM, amplifying its efficacy in predicting and monitoring outbreaks. To construct our dataset, we extracted tweets discussing symptoms from six distinct infectious disease outbreaks (Ebola, Zika, MERS, H1N1, Chikungunya, COVID-19) spanning from 2012 to 2021. Our results demonstrate that the proposed hybrid model outperforms nine cutting-edge and baseline models. This advancement can significantly assist health authorities in minimizing fatalities and preparing preemptively for potential outbreaks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".