Characteristics of immunity and disease-induced mortality synergistically complicate epidemiological dynamics
Bibliographic record
Abstract
Disease-induced mortality and immunity are two key pathogen-specific drivers of epidemiological dynamics. As highlighted by the COVID-19 pandemic, disease-induced mortality can occur not only during active infection, but also following recovery during a period of immunity or after returning to susceptibility. In parallel, this period of immunity can vary in its average duration and, importantly, in its distribution. While these uncertainties underlie the dynamics of many pathogens, their combined effects remain unknown. To address this gap, we formulate a general framework where individuals return to (potentially partial) susceptibility after one or more recovery classes. We show analytically that disease-induced mortality either during infection or while fully-immune has no qualitative effects if there are two or fewer recovered compartments and individuals return to complete susceptibility. However, with four, five, or six recovered compartments, we numerically find that disease-induced mortality during infection or while immune can be either stabilizing or destabilizing, and that the presence of post-infection mortality while susceptible can enable three switches in stability. Thus, our models reveal that immunity combined with disease-induced mortality can have important epidemiological effects. Our findings therefore illustrate the need to include these effects in pathogen-specific models, and for immuno-epidemiological cohort studies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".