The cost-effectiveness of conducting three versus two reverse transcription-polymerase chain reaction tests for diagnosing and discharging people with COVID-19: evidence from the epidemic in Wuhan, China
Bibliographic record
Abstract
OBJECTIVES: The objectives were to evaluate the effectiveness of conducting three versus two reverse transcription-PCR (RT-PCR) tests for diagnosing and discharging people with COVID-19 with regard to public health and clinical impacts by incorporating asymptomatic and presymptomatic infection and to compare the medical costs associated with the two strategies. METHODS: A model that consisted of six compartments was built. The compartments were the susceptible (S), the asymptomatic infective (A), the presymptomatic infective (L), the symptomatic infective (I), the recovered (R), and the deceased (D). The A, L and I classes were infective states. To construct the model, several parameters were set as fixed using existing evidence and the rest of the parameters were estimated by fitting the model to a smoothed curve of the cumulative confirmed cases in Wuhan from 24 January 2020 to 6 March 2020. Input data about the cost-effectiveness analysis were retrieved from the literature. RESULTS: Conducting RT-PCR tests three times for diagnosing and discharging people with COVID-19 reduced the estimated total number of symptomatic cases to 45 013 from 51 144 in the two-test strategy over 43 days. The former strategy also led to 850.1 quality-adjusted life years (QALYs) of health gain and a net healthcare expenditure saving of CN¥49.1 million. About 100.7 QALYs of the health gain were attributable to quality-adjusted life day difference between the strategies during the analytic period and 749.4 QALYs were attributable to years of life saved. CONCLUSIONS: More accurate strategies and methods of testing for the control of COVID-19 may reduce both the number of infections and the total medical costs. Increasing the number of tests should be considered in regions with relatively severe epidemics when existing tests have moderate sensitivity.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".