Within-laboratory SARS-CoV-2 real time PCR testing operations in Nepal: a simulation-based analysis
Bibliographic record
Abstract
Background: COVID-19 has challenged entire health systems, including laboratories. To address the increasing demand for tests to inform the epidemiology of the disease and for case management purposes, many countries made significant investments to rapidly expand laboratory capacity for detecting SARS-CoV-2. In this study, we used a simulated laboratory environment, based on a model of operating laboratories in Nepal, to identify opportunities for improvement. Methods: We developed a discrete event simulation (DES) model, based on data from and in collaboration with Nepali health authorities, to analyse laboratory operations in Nepal. We used a series of "what-if" scenarios under different levels of testing demand and staffing to investigate bottlenecks in the processing of COVID-19 samples in a simulated laboratory environment, assess the impact of potential reagent shortages and increased automation, and more generally, explore the key factors that drive the performance and resilience of the testing system. Findings: Suboptimal staff allocation and scheduling can limit the timely return of laboratory results; however, better staff allocation can mitigate bottlenecks and reduce the impact of reagent shortages. For example, when the demand is 720 samples per day and seven staff members are on duty, adding one additional staff member improves reporting time (reduction from 48 h to approximately 32 h). However, changes in scheduling can increase the average time to return the results to over 200 h. A one-day reagent shortage appears to have minimal impact, but a delay of five days significantly increases the reporting time, reaching nearly 150 h. Increasing automation or better process coordination for sample registration can also lead to better performance, reducing the average reporting time from over 60 h to just under 24 h. Interpretation: Our findings identify important bottlenecks and challenges, along with ways to address them, and thus provide important lessons for improving disease testing operations for this and future pandemics. Funding: WHO Special Programme for Research and Training in Tropical Diseases (TDR).
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".