The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach
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 a...
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| Format: | Article |
| Language: | English |
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Springer
2024-01-01
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| Series: | Saudi Pharmaceutical Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S131901642300381X |
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| author | Ahmad Alhomaid Abdullah H. Alzeer Fahad Alsaawi Abdulaziz Aljandal Rami Al-Jafar Marwan Albalawi Dana Alotaibi Raghad Alabdullatif Razan AlGhassab Dalia M. Mominkhan Muaddi Alharbi Ahmad A. Alghamdi Maryam Almoklif Mohammed K. Alabdulaali |
| author_facet | Ahmad Alhomaid Abdullah H. Alzeer Fahad Alsaawi Abdulaziz Aljandal Rami Al-Jafar Marwan Albalawi Dana Alotaibi Raghad Alabdullatif Razan AlGhassab Dalia M. Mominkhan Muaddi Alharbi Ahmad A. Alghamdi Maryam Almoklif Mohammed K. Alabdulaali |
| author_sort | Ahmad Alhomaid |
| collection | DOAJ |
| description | 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 (R0). 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 R0. Our breakdown analysis of the impact of each NPI indicates that banning going to schools had the greatest impact on the infection reproduction rate (24 %). 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. |
| format | Article |
| id | doaj-art-3b98cd63d10a48b09be7c55bafd1a031 |
| institution | Kabale University |
| issn | 1319-0164 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Saudi Pharmaceutical Journal |
| spelling | doaj-art-3b98cd63d10a48b09be7c55bafd1a0312025-08-20T03:55:41ZengSpringerSaudi Pharmaceutical Journal1319-01642024-01-0132110188610.1016/j.jsps.2023.101886The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approachAhmad Alhomaid0Abdullah H. Alzeer1Fahad Alsaawi2Abdulaziz Aljandal3Rami Al-Jafar4Marwan Albalawi5Dana Alotaibi6Raghad Alabdullatif7Razan AlGhassab8Dalia M. Mominkhan9Muaddi Alharbi10Ahmad A. Alghamdi11Maryam Almoklif12Mohammed K. Alabdulaali13Data Services Sector, Lean Business Services, Riyadh, Saudi ArabiaData Services Sector, Lean Business Services, Riyadh, Saudi ArabiaData Services Sector, Lean Business Services, Riyadh, Saudi ArabiaData Services Sector, Lean Business Services, Riyadh, Saudi ArabiaData Services Sector, Lean Business Services, Riyadh, Saudi Arabia; School of Public Health, Imperial College London, London, UK; Corresponding author.Department of Digital Health, Lean Business Services, Riyadh, Saudi ArabiaData Services Sector, Lean Business Services, Riyadh, Saudi ArabiaData Services Sector, Lean Business Services, Riyadh, Saudi ArabiaData Services Sector, Lean Business Services, Riyadh, Saudi ArabiaNational Health Command Center, Ministry of Health, Riyadh, Saudi ArabiaNational Health Command Center, Ministry of Health, Riyadh, Saudi ArabiaNational Health Command Center, Ministry of Health, Riyadh, Saudi ArabiaNational Health Command Center, Ministry of Health, Riyadh, Saudi ArabiaMinistry of Health, Riyadh, Saudi ArabiaObjectives: 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 (R0). 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 R0. Our breakdown analysis of the impact of each NPI indicates that banning going to schools had the greatest impact on the infection reproduction rate (24 %). 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.http://www.sciencedirect.com/science/article/pii/S131901642300381XCOVID-19 preventionHybrid simulationNon-pharmaceutical intervention |
| spellingShingle | Ahmad Alhomaid Abdullah H. Alzeer Fahad Alsaawi Abdulaziz Aljandal Rami Al-Jafar Marwan Albalawi Dana Alotaibi Raghad Alabdullatif Razan AlGhassab Dalia M. Mominkhan Muaddi Alharbi Ahmad A. Alghamdi Maryam Almoklif Mohammed K. Alabdulaali The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach Saudi Pharmaceutical Journal COVID-19 prevention Hybrid simulation Non-pharmaceutical intervention |
| title | The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach |
| title_full | The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach |
| title_fullStr | The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach |
| title_full_unstemmed | The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach |
| title_short | The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach |
| title_sort | impact of non pharmaceutical interventions on the spread of covid 19 in saudi arabia simulation approach |
| topic | COVID-19 prevention Hybrid simulation Non-pharmaceutical intervention |
| url | http://www.sciencedirect.com/science/article/pii/S131901642300381X |
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