Estimating SARS-CoV-2 infection incidence and detection rates: Demonstrating a novel surveillance method
Bibliographic record
Abstract
OBJECTIVES: Assessing the cumulative incidence of infection conventionally relies on documented infections or serological surveys, both of which have limitations. This study introduces a novel and practical method leveraging testing variation in a population to estimate SARS-CoV-2 infection rates in the population of Qatar. STUDY DESIGN: Cohort study and mathematical modeling. METHODS: A cohort study was conducted from February 28, 2020, to March 04, 2024, to derive testing rates and estimate cumulative incidence of documented infection and hazard rates of documented infection in different testing groups. A deterministic mathematical model, applied to the cohort study data, was employed to simulate infection transmission, testing and infection documentation, and estimate the cumulative incidence of documented and undocumented infections, along with the infection detection rate. RESULTS: At the conclusion of the pre-Omicron phase, the model-estimated cumulative incidence of documented infection, undocumented infection, and all infections was 9.8 %, 29.7 %, and 39.5 %, respectively. By the end of the first-Omicron wave, cumulatively from the onset of the pandemic, these figures rose to 16.9 %, 56.3 %, and 73.2 %, and in the post-first Omicron phase, to 18.8 %, 77.9 %, and 96.7 %, respectively. The infection detection rate in the population was 24.9 %, 21.0 %, and 9.1 % in each of the pre-Omicron phase, first-Omicron wave, and post-first Omicron phase, respectively. Uncertainty and sensitivity analyses confirmed these results. CONCLUSIONS: Leveraging readily available testing data, the introduced method was validated in a real-world setting and has the potential for diverse applications to enhance infectious disease surveillance for both emerging and endemic infections.
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 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 | 0.003 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".