Evidence map›Paper›PMID 38162709›Full record

ArticleSaudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society2024

The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach.

Ahmad Alhomaid, Abdullah H Alzeer, Fahad Alsaawi, Abdulaziz Aljandal, Rami Al-Jafar, Marwan Albalawi, Dana Alotaibi, Raghad Alabdullatif, Razan AlGhassab, Dalia M Mominkhan and 4 more

Open access · hybridAbstract read
In one paragraph

Article in Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 48% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

14 authors at 2 institutions in 2 countries.

Ahmad AlhomaidData Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
Abdullah H AlzeerData Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
Fahad AlsaawiData Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
Abdulaziz AljandalData Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
Rami Al-JafarData Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
Marwan AlbalawiDepartment of Digital Health, Lean Business Services, Riyadh, Saudi Arabia.
Dana AlotaibiData Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
Raghad AlabdullatifData Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
Razan AlGhassabData Services Sector, Lean Business Services, Riyadh, Saudi Arabia.
Dalia M MominkhanNational Health Command Center, Ministry of Health, Riyadh, Saudi Arabia.
Muaddi AlharbiNational Health Command Center, Ministry of Health, Riyadh, Saudi Arabia.
Ahmad A AlghamdiNational Health Command Center, Ministry of Health, Riyadh, Saudi Arabia.
Maryam AlmoklifNational Health Command Center, Ministry of Health, Riyadh, Saudi Arabia.
Mohammed K AlabdulaaliMinistry of Health, Riyadh, Saudi Arabia.
Ministry of Health · SAImperial College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: This paper aims to measure the impact of the implemented nonpharmaceutical interventions (NPIs) in the Kingdom of Saudi Arabia (KSA) during the pandemic using simulation modeling. Methods: To measure the impact of NPI, a hybrid agent-based and system dynamics simulation model was built and validated. Data were collected prospectively on a weekly basis. The core epidemiological model is based on a complex Susceptible-Exposed-Infectious-Recovered and Dead model of epidemic dynamics. Reverse engineering was performed on a weekly basis throughout the study period as a mean for model validation which reported on four outcomes: total cases, active cases, ICU cases, and deaths cases. To measure the impact of each NPI, the observed values of active and total cases were captured and compared to the projected values of active and total cases from the simulation. To measure the impact of each NPI, the study period was divided into rounds of incubation periods (cycles of 14 days each). The behavioral change of the spread of the disease was interpreted as the impact of NPIs that occurred at the beginning of the cycle. The behavioral change was measured by the change in the initial reproduction rate (R Results: After 18 weeks of the reverse engineering process, the model achieved a 0.4 % difference in total cases for prediction at the end of the study period. The results estimated that NPIs led to 64 % change in The R Conclusion: We used hybrid simulation modeling to measure the impact of NPIs taken by the KSA government. The finding further supports the notion that early NPIs adoption can effectively limit the spread of COVID-19. It also supports using simulation for building mathematical modeling for epidemiological scenarios.

Indexed as

COVID-19 preventionHybrid simulationNon-pharmaceutical intervention

Identifiers

PMID38162709
PMCPMC10755097
OpenAlexW4389194720

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.