Fast screening framework for infection control scenario identification
Due to the emergence of the novel coronavirus disease, many recent studies have investigated prediction methods for infectious disease transmission. This paper proposes a framework to quickly screen infection control scenarios and identify the most effective scheme for reducing the number of infecte...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-08-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | http://www.aimspress.com/article/doi/10.3934/mbe.2022574?viewType=HTML |
| _version_ | 1818480953822019584 |
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| author | Yohei Kakimoto Yuto Omae Jun Toyotani Hirotaka Takahashi |
| author_facet | Yohei Kakimoto Yuto Omae Jun Toyotani Hirotaka Takahashi |
| author_sort | Yohei Kakimoto |
| collection | DOAJ |
| description | Due to the emergence of the novel coronavirus disease, many recent studies have investigated prediction methods for infectious disease transmission. This paper proposes a framework to quickly screen infection control scenarios and identify the most effective scheme for reducing the number of infected individuals. Analytical methods, as typified by the SIR model, can conduct trial-and-error verification with low computational costs; however, they must be reformulated to introduce additional constraints, and thus are inappropriate for case studies considering detailed constraint parameters. In contrast, multi-agent system (MAS) simulators introduce detailed parameters but incur high computation costs per simulation, making them unsuitable for extracting effective measures. Therefore, we propose a framework that implements an MAS for constructing a training dataset, and then trains a support vector regression (SVR) model to obtain effective measure results. The proposed framework overcomes the weaknesses of conventional methods to produce effective control measure recommendations. The constructed SVR model was experimentally verified by comparing its performance on datasets with expected and unexpected outputs. Although datasets producing an unexpected output decreased the prediction accuracy, by removing randomness from the training dataset, the accuracy of the proposed method was still high in these cases. High-precision predictions of the MAS-based simulation output were obtained for both test datasets in under one second of the computational time. Furthermore, the experimental results establish that the proposed framework can obtain intuitively correct outputs for unknown inputs, and produces sufficiently high-precision prediction with lower computation costs than an existing method. |
| first_indexed | 2024-12-10T11:28:58Z |
| format | Article |
| id | doaj.art-886ac8ebb17143c1a1a8539a5dc48523 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-10T11:28:58Z |
| publishDate | 2022-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-886ac8ebb17143c1a1a8539a5dc485232022-12-22T01:50:39ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-08-011912123161233310.3934/mbe.2022574Fast screening framework for infection control scenario identificationYohei Kakimoto 0Yuto Omae1 Jun Toyotani 2Hirotaka Takahashi31. College of Industrial Technology, Nihon University, 1-2-1 Izumicho, Narashino, Chiba 275-8575, Japan1. College of Industrial Technology, Nihon University, 1-2-1 Izumicho, Narashino, Chiba 275-8575, Japan1. College of Industrial Technology, Nihon University, 1-2-1 Izumicho, Narashino, Chiba 275-8575, Japan2. Research Center for Space Science, Advanced Research Laboratories, Tokyo City University, 8-15-1 Todoroki, Setagaya, Tokyo 158-0082, JapanDue to the emergence of the novel coronavirus disease, many recent studies have investigated prediction methods for infectious disease transmission. This paper proposes a framework to quickly screen infection control scenarios and identify the most effective scheme for reducing the number of infected individuals. Analytical methods, as typified by the SIR model, can conduct trial-and-error verification with low computational costs; however, they must be reformulated to introduce additional constraints, and thus are inappropriate for case studies considering detailed constraint parameters. In contrast, multi-agent system (MAS) simulators introduce detailed parameters but incur high computation costs per simulation, making them unsuitable for extracting effective measures. Therefore, we propose a framework that implements an MAS for constructing a training dataset, and then trains a support vector regression (SVR) model to obtain effective measure results. The proposed framework overcomes the weaknesses of conventional methods to produce effective control measure recommendations. The constructed SVR model was experimentally verified by comparing its performance on datasets with expected and unexpected outputs. Although datasets producing an unexpected output decreased the prediction accuracy, by removing randomness from the training dataset, the accuracy of the proposed method was still high in these cases. High-precision predictions of the MAS-based simulation output were obtained for both test datasets in under one second of the computational time. Furthermore, the experimental results establish that the proposed framework can obtain intuitively correct outputs for unknown inputs, and produces sufficiently high-precision prediction with lower computation costs than an existing method.http://www.aimspress.com/article/doi/10.3934/mbe.2022574?viewType=HTMLfast screening frameworkmulti-agent systemsupport vector regressioninfection controlidentifying effective scheme |
| spellingShingle | Yohei Kakimoto Yuto Omae Jun Toyotani Hirotaka Takahashi Fast screening framework for infection control scenario identification Mathematical Biosciences and Engineering fast screening framework multi-agent system support vector regression infection control identifying effective scheme |
| title | Fast screening framework for infection control scenario identification |
| title_full | Fast screening framework for infection control scenario identification |
| title_fullStr | Fast screening framework for infection control scenario identification |
| title_full_unstemmed | Fast screening framework for infection control scenario identification |
| title_short | Fast screening framework for infection control scenario identification |
| title_sort | fast screening framework for infection control scenario identification |
| topic | fast screening framework multi-agent system support vector regression infection control identifying effective scheme |
| url | http://www.aimspress.com/article/doi/10.3934/mbe.2022574?viewType=HTML |
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