Mathematical modelling of the West African Ebola virus epidemic
Bibliographic record
Abstract
We present a variant of the SEIR (susceptible-exposed-infectious-recovered) stochastic population-based compartment model of epidemiology to capture the spatial transmission dynamics of the Ebola virus disease epidemic in Sierra Leone, Liberia, and Guinea. Using registered data from the World Health Organization (WHO) situation reports we attempt to capture the transmission dynamics and the spatial spread of the Ebola epidemic. The projected number of newly infected and death cases are estimated and presented. Our objective is to achieve optimal Bayesian tracking of Ebola epidemic in both space and time with data that is (a) irregularly aggregated, and (b) only episodic in its availability. We use Ebola disease incidence as a posterior from the WHO reports. The ensemble optimal statistical interpolation (EnOSI) data assimilation method has been shown to produce optimal Bayesian statistical tracking of emerging epidemics (Cobb et al., 2014). We observe that the prediction improves as data is assimilated over time. The analysis thus provides a realization conditioned on all prior data and newly arrived data. We also found that EnOSI can efficiently adjust its estimated spatial distribution of the number of infected, if and when the epidemic jumps to a new city. * Indicates faculty mentor.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".