Defining epidemics in computer simulation models: How do definitions influence conclusions?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Computer models have proven to be useful tools in studying epidemic disease in human populations. Such models are being used by a broader base of researchers, and it has become more important to ensure that descriptions of model construction and data analyses are clear and communicate important features of model structure. Papers describing computer models of infectious disease often lack a clear description of how the data are aggregated and whether or not non-epidemic runs are excluded from analyses. Given that there is no concrete quantitative definition of what constitutes an epidemic within the public health literature, each modeler must decide on a strategy for identifying epidemics during simulation runs. Here, an SEIR model was used to test the effects of how varying the cutoff for considering a run an epidemic changes potential interpretations of simulation outcomes. Varying the cutoff from 0% to 15% of the model population ever infected with the illness generated significant differences in numbers of dead and timing variables. These results are important for those who use models to form public health policy, in which questions of timing or implementation of interventions might be answered using findings from computer simulation models.
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.
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 | 0.003 | 0.053 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it