Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak
Bibliographic record
Abstract
BACKGROUND: Controlling re-emerging outbreaks such as COVID-19 is a critical concern to global health. Disease forecasting solutions are extremely beneficial to public health emergency management. This work aims to design and deploy a framework for real-time surveillance, prediction, forecasting, and early warning of respiratory disease. To this end, we selected southern African countries and Canadian provinces, along with COVID-19 and influenza as our case studies. METHODOLOGY: Six different datasets were collected for different provinces of Canada: number of influenza cases, number of COVID-19 cases, Google Trends, Reddit posts, satellite air quality data, and weather data. Moreover, five different data sources were collected for southern African countries whose COVID-19 number of cases were significantly correlated with each other: number of COVID-19 infections, Google Trends, Wiki Trends, Google News, and satellite air quality data. For each infectious disease, i.e. COVID-19 and Influenza for Canada and COVID-19 for southern African countries, data was processed, scaled, and fed into the deep learning model which included four layers, namely, a Convolutional Neural Network (CNN), a Graph Neural Network (GNN), a Gated Recurrent Unit (GRU), and a linear Neural Network (NN). Hyperparameters were optimized to provide an accurate 56-day-ahead prediction of the number of cases. RESULT: The accuracy of our models in real-time surveillance, prediction, forecasting, and early warning of respiratory diseases are evaluated against state-of-the-art models, through Root Mean Square Error (RMSE), coefficient of determination (R2-score), and correlation coefficient. Our model improves R2-score, RMSE, and correlation by up to 55.98 %, 39.71 %, and 44.47 % for 56 days-ahead COVID-19 prediction in Ontario, 34.87 %, 25.52 %, 50.91 % for 8 weeks-ahead influenza prediction in Quebec, and 51.04 %, 32.04 %, and 28.74 % for 56 days-ahead COVID-19 prediction in South Africa, respectively. CONCLUSION: This work presents a framework that automatically collects data from unconventional sources, and builds an early warning system for COVID-19 and influenza outbreaks. The result is extremely helpful to policy-makers and health officials for preparedness and rapid response against future outbreaks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".