Deviation from the recommended schedule: optimal dosing interval for a two-dose vaccination programme
Bibliographic record
Abstract
Optimal dosing interval: Program in matlab associated with paper "Deviation from the recommended schedule: Optimal dosing interval for a two-dose vacciantion programme". Model details: A delay-differential model to describe the dynamics of disease spread with a two-dose vaccination. The model incorporated variables such as waning vaccine-induced and naturally-acquired immunity, as well as the capacity for vaccine distribution. We simulated the model and determined the optimal dosing interval as a function of vaccien efficacy against infection and outcomes by comparing the disease burden in delayed vaccination scenarios to that of following the recommended schedule. Repository contents and how to use them: There are five m files and eight data files: RRtau_vacsR: run directly and save a .mat file with total number of incidence/hosp/death of primary infection during the DSD (delayed second dose) period when the second dose is administrated in a recommended schedule; RRtau_vacsD: run directly and save a .mat file with total number of incidence/hosp/death of primary infection during the DSD period when the second dose is adminstrited in a delayed schedule; RR100_vacsR: run directly and save a .mat file with total number of incidence/hosp/death of primary infection during the first 100 days when the second dose is administrated in a recommended schedule; RR100_vacsD: run directly and save a .mat file with total number of incidence/hosp/death of primary infection during the first 100 days when the second dose is adminstrited in a delayed schedule; plot_RR: load different sets of data, and produce the relative reproduction plot during the DSD or first 100 days; R1_400: data file for inci/hosp/death during DSD period when second dose is in a recommended schedule with R0 = 1.1; D1_400: data file for inci/hosp/death during DSD period when second dose is in a delayed schedule with R0 = 1.1; RR100_R1400: data file for inci/hosp/death during first 100 days when second dose is in a recommended schedule with R0 = 1.1; RR100_D1400: data file for inci/hosp/death during first 100 days when second dose is in a delayed schedule with R0 = 1.1; R1_400_R18: data file for inci/hosp/death during DSD period when second dose is in a recommended schedule with R0 = 1.8; D1_400_R18: data file for inci/hosp/death during DSD period when second dose is in a delayed schedule with R0 = 1.8; RR100_R1400_R18: data file for inci/hosp/death during first 100 days when second dose is in a recommended schedule with R0 = 1.8; RR100_D1400_R18: data file for inci/hosp/death during first 100 days when second dose is in a delayed schedule with R0 = 1.8
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".