Modeling Infectious Diseases in Humans and Animals:Modeling Infectious Diseases in Humans and Animals
Bibliographic record
Abstract
Mathematical modeling of infectious diseases has progressed dramatically over the past 3 decades and continues to flourish at the nexus of mathematics, epidemiology, and infectious diseases research. Now recognized as a valuable tool, mathematical models are being integrated into the public health decision-making process more than ever before. However, despite rapid advancements in this area, a formal training program for mathematical modeling is lacking, and there are very few books suitable for a broad readership. To support this bridging science, a common language that is understood in all contributing disciplines is required. Modeling Infectious Diseases in Humans and Animals is a timely and successful attempt to fill this gap. In this volume, Keeling and Rohani cover many important topics in mathematical modeling of infectious diseases epidemiology and introduce a number of classic and modern techniques, with a vigilant approach that introduces and emphasizes the concepts but avoids the inclusion of extensive mathematical details. This recipe is ideal for a multidisciplinary field of research like infectious diseases epidemiology. To introduce basic modeling concepts to readers who may not be familiar with mathematical modeling literature, Keeling and Rohani begin with simple deterministic models. In addition, they establish fundamental notions, such as the basic reproductive number, epidemic curve, dynamic equilibrium, age of infection, and oscillatory dynamics. They further introduce more-refined and advanced models by incorporating heterogeneity with behavior or age, which accounts for variability in transmission risk in real populations.
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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".