Infectious Disease Forecasting using Multivariate Incomplete Time-series: A Hybrid Architecture with Stacked Dilated Causal Convolutions
Bibliographic record
Abstract
The COVID-19 pandemic brought into focus the importance of accurately predicting the future spread of an infectious disease. Hybrid neural networks combining standard convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have previously been utilized for infectious disease forecasting. This study enhances previous research by introducing a novel architecture using a Stacked-dilated-causal Convolutional Neural Network and Bidirectional Long Short-Term Memory (SCNN-BiLSTM) to forecast the spread of an infectious disease by accommodating incomplete multivariate temporal sequences. Unlike standard CNN, stacked-dilated-causal convolutions provide full coverage history, where all previous elements in the time-series input window are modelled to predict the next output.COVID-19 infection data in Ontario, Canada was selected to demonstrate the effectiveness of our approach in infectious-disease forecasting. The two main contributions of this study are the following: (1) proposing Stacked-dilated-causal Convolution (SCNN) along with Bidirectional Long Short-Term Memory to create a hybrid architecture (SCNN-BiLSTM) that can model full coverage history for infectious disease forecasting; (2) developing a multivariate model that is capable of incorporating multiple incomplete input sequences. The results obtained from this study show that the proposed architecture can successfully predict the spread of infectious disease with a long forecasting horizon by utilizing multivariate data even in the absence of complete temporal sequences for all variables.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".