Discrete Inverse Method for Extracting Disease Transmission Rates from Accessible Infection Data
Bibliographic record
Abstract
Abstract. Accurate estimation of the transmissibility of an infectious disease is critical to understanding disease transmission dynamics and designing effective control strategies. However, it has always been difficult to estimate the transmission rates due to the unobservability and multiple contributing factors. In this paper, we develop a data-driven inverse method based on discretizations of compartmental differential equation models for estimating time-varying transmission rates of infectious diseases. By developing iteration algorithms for three typical classes of infectious diseases, namely, a disease with seasonal cycles, a disease with nonseasonal cycles, and a disease with no obvious periodicity, we demonstrate that the discrete inverse method is a valuable tool for extracting information from available pandemic or epidemic incidence data. We also obtain insights for some epidemiological phenomena and issues of concern based on each application. Our method is highly intuitive and generates rapid implementation even with multiple years of data instances. In particular, it can be used in conjunction with other data-driven technologies, such as machine learning, to forecast future disease dynamics based on future weather conditions, policy decisions, or human mobility trends, providing guidance to public health authorities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".