4 Reproduction
4.1 Studies
Shoaib and Ramamohan (2021): Uses python (salabim) to model primary health centres (PHCs) in India. The model has four patient types: outpatients, inpatients, childbirth cases and antenatal care patients. Four model configurations are developed based on observed PHC practices or government-mandated operational guidelines. The paper explores different operational patterns for scenarios where very high utilisation was observed, to explore what might help reduce utilisation of these resources. Note: The article was as Shoaib and Ramamohan (2022), but we used the green open access pre-print Shoaib and Ramamohan (2021). Link to reproduction.
Huang et al. (2019): Uses R (simmer) to model an endovascular clot retrieval (ECR) service. ECR is a treatment for acute ischaemic stroke. The model includes the stroke pathway, as well as three other pathways that share resources with the stroke pathway: an elective non-stroke interventional neuroradiology pathway, an emergency interventional radiology pathway, and an elective interventional radiology pathway. The paper explores waiting times and resource utilisation - particularly focussing on the biplane angiographic suite (angioINR). A few scenarios are tried to help examine why the wait times are so high for the angioINR. Link to reproduction.
Lim et al. (2020): Uses python (numpy and pandas) to model the transmission of COVID-19 in a laboratory. It examines the proportion of staff infected in scenarios varying the: number of shifts per day; number of staff per shift; overall staff pool; shift patterns; secondary attack rate of the virus; introduction of protective measures (social distancing and personal protective equipment). Link to reproduction.
Kim et al. (2021): Adapts a previously developed R (Rcpp, expm, msm, foreach, iterators, doParallel) model for abdominal aortic aneurysm (AAA) screening of men in England. The model is adapted/used to explore different approaches to resuming screening and surgical repair for AAA, as these survives were paused or substantially reduced during COVID-19 due to concerns about virus transmission. Link to reproduction.
Anagnostou et al. (2022): This paper includes two models - we have focussed just on the dynamiC Hospital wARd Management (CHARM) model. CHARM uses Python (simpy) to model intensive care units (ICU) in the COVID-19 pandemic (as well as subsequent stays in a recovery bed). It includes three types of admission to the ICU (emergency, elective or COVID-19). COVID-19 patients are kept seperate, and if they run out of capacity due to a surge in COVID-19 admissions, additional capacity can be pooled from the elective and emergency capacity. Link to reproduction.
Johnson et al. (2021): TBC
4.2 Scope
Study | Scope | Success | Time |
---|---|---|---|
Shoaib and Ramamohan 2022 | 17 items: • 1 table • 9 figures • 7 in-text results |
16 out of 17 (94%) | 28h 14m |
Huang et al. 2019 | 8 items: • 5 figures • 3 in-text results |
3 out of 8 (37.5%) | 24h 10m |
Lim et al. 2020 | 9 items: • 5 tables • 4 figures |
9 out of 9 (100%) | 12h 27m |
Kim et al. 2021 | 10 items: • 3 tables • 6 figures • 1 in-text result |
10 out of 10 (100%) | 14h 42m |
Anagnostou et al. 2022 | 1 item: • 1 figure |
1 out of 1 (100%) | 2h 10m |
Johnson et al. 2021 | 5 items: • 1 table • 4 figures |
TBC | TBC |
4.3 Time to completion
Non-interactive figure:
Interactive figure: