COVID-19 and other viruses: Holding back its spreading by massive testing

Sainz-Pardo, José L.; Valero, José

doi:10.1016/j.eswa.2021.115710

Cited by 8 publications

(2 citation statements)

References 41 publications

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“…It is helpful to optimize the allocation of testing resources by estimating the testing needs in different situations. Sainz-Pardo et al [ 29 ] introduced a heuristic to minimize the spread of COVID-19 by planning an effective distribution of tests in a population in an area over time. Some studies have pointed out that in the case of limited medical resources, the optimal allocation of resources in multiple regions depends on the state of the whole region, and a region may change from limited testing resources to having enough testing resources [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study

Wang,

Zhang,

et al. 2024

Infect Dis Ther

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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 ( R 2 ) 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...

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Section: Discussionmentioning

confidence: 99%

Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study

Wang,

Zhang,

et al. 2024

Infect Dis Ther

View full text Add to dashboard Cite

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“…The two important approaches to managing the pandemic involve the following: (1) preventing the spread of the virus and controlling virus resurgence through sound government directives and tracking (Lee and Trimi 2021 ; Sainz-Pardo and Valero 2021 ), while concurrently developing effective vaccines and rapidly administering them to the population for reaching herd immunity; and (2) managing infection cases by finding right treatment methods and cures. For the first approach, implementing sound practices recommended by credible agencies such as CDC, as well as a set of directives issued by the federal government and further reinforced by state/local governments, is imperative.…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19 vaccine distribution: exploring strategic alternatives for the greater good

Asllani

Trimi

2022

Serv Bus

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The dire state of the COVID-19 pandemic crisis symbolized the urgency for efficient distribution and administration of vaccines to combat the virus as the most urgent public health service. This paper presents a prototype multi-criteria decision support model based on goal programming that can effectively support vaccination plans for the greater good of society. The optimization goals of the model include minimizing the number of fatalities and risk of spreading the disease, while complying with government health agency’s priority guidelines for vaccination. This study applied the model to a real-world dataset to demonstrate how it can be effectively applied as a decision support tool for vaccine distribution plans and manage future pandemics.

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Differential evolution and particle swarm optimization against COVID-19

Piotrowski

Piotrowska

2021

Artif Intell Rev

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COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used.

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COVID-19 and other viruses: Holding back its spreading by massive testing

Cited by 8 publications

References 41 publications

Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study

Evaluating the Demand for Nucleic Acid Testing in Different Scenarios of COVID-19 Transmission: A Simulation Study

COVID-19 vaccine distribution: exploring strategic alternatives for the greater good

Differential evolution and particle swarm optimization against COVID-19

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