18656 7 100962 2 309022 document 100962 7382033.pdf application/pdf c876c1236d06e10477f2383295bdb68c MD5 6757224 2023-03-06 14:25:57 https://pure.iiasa.ac.at/id/eprint/18656/1/7382033.pdf 18656 1 1 application/pdf text en public cc_by_4

7382033.pdf

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/100962 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/100962 http://eprints.org/relation/ismediumThumbnailVersionOf https://pure.iiasa.ac.at/id/document/100962 100966 2 309032 document 100966 small.jpg image/png f3c7c52fe709c81308c862c3f1a37b8c MD5 1837 2023-03-06 14:36:05 https://pure.iiasa.ac.at/id/eprint/18656/5/small.jpg 18656 5 5 image/png other Thumbnails conversion from text to thumbnail_small en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/100962 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/100962 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/100962 100967 1 309033 document 100967 indexcodes.txt text/x-c f9ac2748d6d6700b893bec78b328be16 MD5 17569 2023-03-06 14:36:09 https://pure.iiasa.ac.at/id/eprint/18656/6/indexcodes.txt 18656 6 6 text/x-c other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/100962 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/100962 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/100962 archive 353 disk0/00/01/86/56 2023-03-06 14:27:28 2023-03-06 14:27:29 2023-03-06 14:27:28 article show Park C. 9021 pub prog_eep This study estimates the consequences of risk propagation, such as that of COVID-19, using network-agent dynamics. Given several scenarios, the network-agent model provides critical insights into infection risk using a model-driven approach to interconnected interventions. The simulation results suggest that employing a nonevolutionary governing structure with evolutionary individual interaction parameters guided by testing can help suppress outbreaks to levels below the standard critical-care capacity. Furthermore, setting the protection level as the macroscale and the shrinking of individual interactions as the microscale, the effects of social distancing on transmission rates are reflected in the disease model. In addition, the parameters that reflect the best feasible scenarios can be determined. These findings are relevant to COVID-19 pandemic policies wherein interconnected interventions reduce the socioeconomic costs of risk propagation. 2022-11 published Wiley-Blackwell doi:10.1155/2022/7382033 2940 public Complexity 2022 e7382033 TRUE 1099-0526 no info:eu-repo/semantics/article