A Mechanism-based Outbreak Projection Study of Pertussis (Whooping Cough): Combining Particle Filtering and Compartmental Models with Pre-vaccination Surveillance data
Bibliographic record
Abstract
Abstract Particle filtering is a contemporary Sequential Monte Carlo state inference and identification methodology that allows filtering of general non-Gaussian and non-linear models in light of time series of empirical observations. Several previous lines of research have demonstrated the capacity to effectively apply particle filtering to low-dimensional compartmental transmission models. We demonstrate here implementation and evaluation of particle filtering to more complex compartmental transmission models for pertussis – including application with models involving 1, 2, and 32 age groups and with two distinct functional forms for contact matrices – using over 35 years of monthly and annual pre-vaccination provincial data from the mid-western Canadian province. Following evaluation of the predictive accuracy of these four particle filtering models, we then performed prediction, intervention experiments and outbreak classification analysis based on the most accurate model. Using that model, we contribute the first full-paper description of particle filter-informed intervention evaluation in health. We conclude that applying particle filtering with relatively high-dimensional pertussis transmission models, and incorporating time series of reported counts, can serve as a valuable technique to assist public health authorities in predicting pertussis outbreak evolution and classify whether there will be an outbreak or not in the next month (Area under the ROC Curve of 0.9) in the context of even aggregate monthly incoming empirical data. Within this use, the particle filtering models can moreover perform counterfactual analysis of interventions to assist the public health authorities in intervention planning. With its grounding in an understanding of disease mechanisms and a representation of the latent state of the system, when compared with other emerging applications of artificial intelligence techniques in outbreak projection, this technique further offers the advantages of high explanatory value and support for investigation of counterfactual scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".