Incorporating Time Delays in the Mathematical Modelling of the Human Immune Response in Viral Infections
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
Mathematical modelling helps to describe the functional and causal relationships between objects in the physical world. The complexity of these models increases as more components and variables are added to maintain and observe. Differential equations are regularly used in these models, as they are able to display the interactions between several variables and describe non-linear behaviour. Differential equations are commonly used in immune response mathematical models to help describe these complex and dynamic interactions within the immune system of the organism. Time delays in the immune system are common and are often disregarded due to the low-resolution of models, which provide limited description of the specific section of immune system being studied. The few models that incorporate time delays are mostly at the epidemiological level, to track the spread of the virus in the population. In this paper we review the applications of the models based on differential equations and describe their potential utilization for the studies of immune response in SARS-CoV-2.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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