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Liu L, Wang X, Liu O, Li Y, Jin Z, Tang S, Wang X. Valuation and comparison of the actual and optimal control strategy in an emerging infectious disease: Implication from a COVID-19 transmission model. Infect Dis Model 2024; 9:354-372. [PMID: 38385019 PMCID: PMC10879675 DOI: 10.1016/j.idm.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 02/03/2024] [Accepted: 02/03/2024] [Indexed: 02/23/2024] Open
Abstract
To effectively combat emerging infectious diseases like COVID-19, it is crucial to adopt strict prevention and control measures promptly to effectively contain the spread of the epidemic. In this paper, we propose a transmission model to investigate the influence of two control strategies: reducing contact numbers and improving medical resources. We examine these strategies in terms of constant control and time-varying control. Through sensitivity analysis on two reproduction numbers of the model with constant control, we demonstrate that reducing contact numbers is more effective than improving medical resources. Furthermore, these two constant controls significantly influence the peak values and timing of infections. Specifically, intensifying control measures can reduce peak values, albeit at the expense of delaying the peak time. In the model with time-varying control, we initially explore the corresponding optimal control problem and derive the characteristic expression of optimal control. Subsequently, we utilize real data from January 10th to April 12th, 2020, in Wuhan city as a case study to perform parameter estimation by using our proposed improved algorithm. Our findings illustrate that implementing optimal control measures can effectively reduce infections and deaths, and shorten the duration of the epidemic. Then, we numerically explore that implementing control measures promptly and increasing intensity to reduce contact numbers can make actual control be more closer to optimized control. Finally, we utilize the real data from October 31st to November 18th, 2021, in Hebei province as a second case study to validate the feasibility of our proposed suggestions.
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Affiliation(s)
- Lili Liu
- Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Complex Systems Research Center, Shanxi University, Taiyuan, 030006, China
| | - Xi Wang
- Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Complex Systems Research Center, Shanxi University, Taiyuan, 030006, China
- School of Mathematical Sciences, Shanxi University, Taiyuan, 030006, China
| | - Ou Liu
- School of Mathematical Sciences, Shanxi University, Taiyuan, 030006, China
| | - Yazhi Li
- School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Guizhou, Duyun, 558000, China
| | - Zhen Jin
- Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Complex Systems Research Center, Shanxi University, Taiyuan, 030006, China
| | - Sanyi Tang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710119, China
| | - Xia Wang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710119, China
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Martignoni MM, Arino J, Hurford A. Is SARS-CoV-2 elimination or mitigation best? Regional and disease characteristics determine the recommended strategy. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240186. [PMID: 39100176 PMCID: PMC11295893 DOI: 10.1098/rsos.240186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/01/2024] [Indexed: 08/06/2024]
Abstract
Public health responses to the COVID-19 pandemic varied across the world. Some countries (e.g. mainland China, New Zealand and Taiwan) implemented elimination strategies involving strict travel measures and periods of rigorous non-pharmaceutical interventions (NPIs) in the community, aiming to achieve periods with no disease spread; while others (e.g. many European countries and the USA) implemented mitigation strategies involving less strict NPIs for prolonged periods, aiming to limit community spread. Travel measures and community NPIs have high economic and social costs, and there is a need for guidelines that evaluate the appropriateness of an elimination or mitigation strategy in regional contexts. To guide decisions, we identify key criteria and provide indicators and visualizations to help answer each question. Considerations include determining whether disease elimination is: (1) necessary to ensure healthcare provision; (2) feasible from an epidemiological point of view and (3) cost-effective when considering, in particular, the economic costs of travel measures and treating infections. We discuss our recommendations by considering the regional and economic variability of Canadian provinces and territories, and the epidemiological characteristics of different SARS-CoV-2 variants. While elimination may be a preferable strategy for regions with limited healthcare capacity, low travel volumes, and few ports of entry, mitigation may be more feasible in large urban areas with dense infrastructure, strong economies, and with high connectivity to other regions.
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Affiliation(s)
- Maria M. Martignoni
- Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, Canada
- Department of Ecology, Evolution and Behavior, A. Silberman Institute of Life Sciences, Faculty of Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Julien Arino
- Department of Mathematics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Amy Hurford
- Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, Canada
- Biology Department and Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, Canada
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