Evidence map›Paper›PMID 40337599›Full record

ArticleThe Lancet regional health. Southeast Asia2025

Within-laboratory SARS-CoV-2 real time PCR testing operations in Nepal: a simulation-based analysis.

Fannie L Côté, Nadia Lahrichi, Erica Gralla, Hannah Bakker, Parvathy Krishnan Krishnakumari, Joaquim Gromicho, Arunkumar Govindakarnavar, Runa Jha, Lilee Shrestha, Nirajan Bhusal and 6 more

Abstract read
In one paragraph

Article in The Lancet regional health. Southeast Asia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Fannie L CôtéCIRRELT & Polytechnique Montreal, Montreal, Canada.
Nadia LahrichiCIRRELT & Polytechnique Montreal, Montreal, Canada.
Erica GrallaGeorge Washington University, Washington DC, USA.
Hannah BakkerKarlsruhe Institute of Technology, Karlsruhe, Germany.
Parvathy Krishnan KrishnakumariAnalytics for a Better World, Amsterdam, Netherlands.
Joaquim GromichoAnalytics for a Better World, Amsterdam, Netherlands.
Arunkumar GovindakarnavarWorld Health Organization Country Office for Nepal, Lalitpur, Nepal.
Runa JhaNational Public Health Laboratory, Kathmandu, Nepal.
Lilee ShresthaNational Public Health Laboratory, Kathmandu, Nepal.
Nirajan BhusalWorld Health Organization Country Office for Nepal, Lalitpur, Nepal.
Saugat ShresthaWorld Health Organization Country Office for Nepal, Lalitpur, Nepal.
Rashmi MulmiWorld Health Organization Country Office for Nepal, Lalitpur, Nepal.
Priya JhaWorld Health Organization Country Office for Nepal, Lalitpur, Nepal.
Reuben SamuelWorld Health Organization South-East Asia Region, New Delhi, India.
Dhamari NaidooWorld Health Organization South-East Asia Region, New Delhi, India.
Victor J Del Rio VilasUK Health Security Agency, Public Health Rapid Support Team, London, UK.

Funding

World Health Organization 001
6 · The paper itself

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).

Indexed as

COVID-19Discrete event simulation (DES)Laboratory capacity assessmentPCRSARS-CoV-2Sensitivity analysis

Identifiers

PMID40337599
PMCPMC12056959

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.