{"title":"新冠肺炎超级传播事件的混沌：基于数据驱动方法的分析","authors":"N. Ganegoda, S. Perera","doi":"10.1177/09720634221150964","DOIUrl":null,"url":null,"abstract":"Superspreading has become a key mechanism of COVID-19 transmission which creates chaos. The classical approach of compartmental models may not sufficiently reflect the epidemiological situation amid superspreading events (SSEs). We perform a data-driven approach and recognise the deterministic chaos of confirmed cases. The first derivative (≈difference of total confirmed cases) and the second derivative (≈difference of the first derivative) are used upon SSEs to showcase the chaos. Varying solution trajectories, sensitivity and numerical unpredictability are the chaotic characteristics discussed here.","PeriodicalId":45421,"journal":{"name":"Journal of Health Management","volume":"25 1","pages":"514 - 525"},"PeriodicalIF":1.0000,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Chaos of COVID-19 Superspreading Events: An Analysis Via a Data-driven Approach\",\"authors\":\"N. Ganegoda, S. Perera\",\"doi\":\"10.1177/09720634221150964\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Superspreading has become a key mechanism of COVID-19 transmission which creates chaos. The classical approach of compartmental models may not sufficiently reflect the epidemiological situation amid superspreading events (SSEs). We perform a data-driven approach and recognise the deterministic chaos of confirmed cases. The first derivative (≈difference of total confirmed cases) and the second derivative (≈difference of the first derivative) are used upon SSEs to showcase the chaos. Varying solution trajectories, sensitivity and numerical unpredictability are the chaotic characteristics discussed here.\",\"PeriodicalId\":45421,\"journal\":{\"name\":\"Journal of Health Management\",\"volume\":\"25 1\",\"pages\":\"514 - 525\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Health Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/09720634221150964\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"HEALTH POLICY & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Health Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/09720634221150964","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"HEALTH POLICY & SERVICES","Score":null,"Total":0}
Chaos of COVID-19 Superspreading Events: An Analysis Via a Data-driven Approach
Superspreading has become a key mechanism of COVID-19 transmission which creates chaos. The classical approach of compartmental models may not sufficiently reflect the epidemiological situation amid superspreading events (SSEs). We perform a data-driven approach and recognise the deterministic chaos of confirmed cases. The first derivative (≈difference of total confirmed cases) and the second derivative (≈difference of the first derivative) are used upon SSEs to showcase the chaos. Varying solution trajectories, sensitivity and numerical unpredictability are the chaotic characteristics discussed here.
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