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Wang YY, Zhang WW, Lu ZX, Sun JL, Jing MX. Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study. Infect Dis Ther 2024; 13:813-826. [PMID: 38498107 PMCID: PMC11058130 DOI: 10.1007/s40121-024-00954-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 02/26/2024] [Indexed: 03/20/2024] Open
Abstract
INTRODUCTION The 2019 novel coronavirus (COVID-19) has been recognized as the most severe human infectious disease pandemic in the past century. To enhance our ability to control potential infectious diseases in the future, this study simulated the influence of nucleic acid testing on the transmission of COVID-19 across varied scenarios. Additionally, it assessed the demand for nucleic acid testing under different circumstances, aiming to furnish a decision-making foundation for the implementation of nucleic acid screening measures, the provision of emergency materials, and the allocation of human resources. METHODS Considering the transmission dynamics of COVID-19 and the preventive measures implemented by countries, we explored three distinct levels of epidemic intensity: community transmission, outbreak, and sporadic cases. Integrating the theory of scenario analysis, we formulated six hypothetical epidemic scenarios, each corresponding to possible occurrences during different phases of the pandemic. We developed an improved SEIR model, validated its accuracy using real-world data, and conducted a comprehensive analysis and prediction of COVID-19 infections under these six scenarios. Simultaneously, we assessed the testing resource requirements associated with each scenario. RESULTS We compared the predicted number of infections simulated by the modified SEIR model with the actual reported cases in Israel to validate the model. The root mean square error (RMSE) was 350.09, and the R-squared (R2) was 0.99, indicating a well-fitted model. Scenario 4 demonstrated the most effective prevention and control outcomes. Strengthening non-pharmaceutical interventions and increasing nucleic acid testing frequency, even under low testing capacity, resulted in a delayed epidemic peak by 78 days. The proportion of undetected cases decreased from 77.83% to 31.21%, and the overall testing demand significantly decreased, meeting maximum demand even with low testing capacity. The initiation of testing influenced case detection probability. Under high testing capacity, increasing testing frequency elevated the detection rate from 36.40% to 77.83%. Nucleic acid screening proved effective in reducing the demand for testing resources under diverse epidemic prevention and control strategies. While effective interventions and nucleic acid screening measures substantially diminished the demand for testing-related resources, varying degrees of insufficient testing capacity may still persist. CONCLUSIONS The nucleic acid detection strategy proves effective in promptly identifying and isolating infected individuals, thereby mitigating the infection peak and extending the time to peak. In situations with constrained testing capacity, implementing more stringent measures can notably decrease the number of infections and alleviate resource demands. The improved SEIR model demonstrates proficiency in predicting both reported and unreported cases, offering valuable insights for future infection risk assessments. Rapid evaluations of testing requirements across diverse scenarios can aptly address resource limitations in specific regions, offering substantial evidence for the formulation of future infectious disease testing strategies.
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Affiliation(s)
- Yu-Yuan Wang
- Department of Preventive Medicine, School of Medicine, Shihezi University, 221 Beisi Road, Shihezi, 832003, People's Republic of China
- Key Laboratory for Prevention and Control of Emerging Infectious Diseases and Public Health Security, The Xinjiang Production and Construction Corps, Xinjiang, People's Republic of China
| | - Wei-Wen Zhang
- Department of Preventive Medicine, School of Medicine, Shihezi University, 221 Beisi Road, Shihezi, 832003, People's Republic of China
- Key Laboratory for Prevention and Control of Emerging Infectious Diseases and Public Health Security, The Xinjiang Production and Construction Corps, Xinjiang, People's Republic of China
| | - Ze-Xi Lu
- Department of Preventive Medicine, School of Medicine, Shihezi University, 221 Beisi Road, Shihezi, 832003, People's Republic of China
- Key Laboratory for Prevention and Control of Emerging Infectious Diseases and Public Health Security, The Xinjiang Production and Construction Corps, Xinjiang, People's Republic of China
| | - Jia-Lin Sun
- Department of Preventive Medicine, School of Medicine, Shihezi University, 221 Beisi Road, Shihezi, 832003, People's Republic of China.
- Key Laboratory for Prevention and Control of Emerging Infectious Diseases and Public Health Security, The Xinjiang Production and Construction Corps, Xinjiang, People's Republic of China.
- Department of Nutrition and Food Hygiene, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Ming-Xia Jing
- Department of Preventive Medicine, School of Medicine, Shihezi University, 221 Beisi Road, Shihezi, 832003, People's Republic of China.
- Key Laboratory for Prevention and Control of Emerging Infectious Diseases and Public Health Security, The Xinjiang Production and Construction Corps, Xinjiang, People's Republic of China.
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Zuccarelli J, Seaman L, Rader K. Assessing the Impact of Non-Pharmaceutical Interventions on Consumer Mobility Patterns and COVID-19 Transmission in the US. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2024; 21:67. [PMID: 38248532 PMCID: PMC10815148 DOI: 10.3390/ijerph21010067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/02/2024] [Accepted: 01/05/2024] [Indexed: 01/23/2024]
Abstract
The initial outbreak of COVID-19 during late December 2019 and the subsequent global pandemic markedly changed consumer mobility patterns worldwide, largely in response to government-ordered non-pharmaceutical interventions (NPIs). In this study, we investigate these changes as they relate to the initial spread of COVID-19 within two states-Massachusetts and Michigan. Specifically, we use linear and generalized linear mixed-effects models to quantify the relationship between four NPIs and individuals' point-of-sale (POS) credit card transactions, as well as the relationship between subsequent changes in POS transactions and county-level COVID-19 case growth rates. Our analysis reveals a significant negative association between NPIs and daily POS transactions, particularly a dose-response relationship, in which stringent workplace closures, stay-at-home requirements, and gathering restrictions were all associated with decreased POS transactions. We also uncover a significant positive association between 12-day lagged changes in POS transactions compared to pre-pandemic baselines and county-level COVID-19 case growth rates. Overall, our study supports previous findings that early NPIs reduced human mobility and COVID-19 transmission in the US, providing policymakers with quantitative evidence concerning the effectiveness of NPIs.
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Affiliation(s)
- Joseph Zuccarelli
- The Charles Stark Draper Laboratory, Cambridge, MA 02139, USA;
- Department of Statistics, Harvard University, Cambridge, MA 02139, USA;
| | - Laura Seaman
- The Charles Stark Draper Laboratory, Cambridge, MA 02139, USA;
| | - Kevin Rader
- Department of Statistics, Harvard University, Cambridge, MA 02139, USA;
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Alhomaid A, Alzeer AH, Alsaawi F, Aljandal A, Al-Jafar R, Albalawi M, Alotaibi D, Alabdullatif R, AlGhassab R, Mominkhan DM, Alharbi M, Alghamdi AA, Almoklif M, Alabdulaali MK. The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach. Saudi Pharm J 2024; 32:101886. [PMID: 38162709 PMCID: PMC10755097 DOI: 10.1016/j.jsps.2023.101886] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 11/25/2023] [Indexed: 01/03/2024] Open
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 (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.
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Affiliation(s)
- Ahmad Alhomaid
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
| | | | - Fahad Alsaawi
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
| | | | - Rami Al-Jafar
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
- School of Public Health, Imperial College London, London, UK
| | - Marwan Albalawi
- Department of Digital Health, Lean Business Services, Riyadh, Saudi Arabia
| | - Dana Alotaibi
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
| | | | - Razan AlGhassab
- Data Services Sector, Lean Business Services, Riyadh, Saudi Arabia
| | - Dalia M. Mominkhan
- National Health Command Center, Ministry of Health, Riyadh, Saudi Arabia
| | - Muaddi Alharbi
- National Health Command Center, Ministry of Health, Riyadh, Saudi Arabia
| | - Ahmad A. Alghamdi
- National Health Command Center, Ministry of Health, Riyadh, Saudi Arabia
| | - Maryam Almoklif
- National Health Command Center, Ministry of Health, Riyadh, Saudi Arabia
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