Spatial age-stratified epidemiological model with applications to South African COVID-19 pandemic
Bibliographic record
Abstract
Objectives This study aims to address the heterogeneity among different age groups in terms of their susceptibility to and transmissibility of infectious diseases. It also seeks to understand how spatial disparities affect disease spread and local population responses to emerging and re-emerging infectious diseases (EIDs and REIDs). Design/Methods We developed a spatial age-stratified SEIR model using COVID-19 hospitalisation data from South Africa, focusing on the first two waves of the pandemic. This model incorporates contact matrices and demographic data to capture age-dependent and spatial variations in disease dynamics. Results The spatial age-stratified model produced more biologically plausible and accurate predictions compared to non-stratified models investigated. It highlighted significant differences in COVID-19 risk and transmission across different age groups and regions, offering insights into targeted intervention strategies. Conclusions The proposed model demonstrates the importance of considering both age and spatial heterogeneity in mathematical models for infectious disease prediction. It provides a valuable tool for governments and public health officials, particularly in resource-limited settings, to develop more effective and targeted interventions. This model can be adapted for other EIDs and REIDs with similar dynamics, enhancing preparedness and response strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".