{"title":"针对具体国家的COVID-19模型","authors":"G. Meissner, Hong Sherwin","doi":"10.2139/ssrn.4043977","DOIUrl":null,"url":null,"abstract":"Abstract Objectives To dynamically measure COVID-19 transmissibility consistently normalized by population size in each country. Methods A reduced-form model enhanced from the classical SIR is proposed to stochastically represent the Reproduction Number and Mortality Rate, directly measuring the combined effects of viral evolution and population behavioral response functions. Results Evidences are shown that this e(hanced)-SIR model has the power to fit country-specific empirical data, produce interpretable model parameters to be used for generating probabilistic scenarios adapted to the still unfolding pandemic. Conclusions Stochastic processes embedded within compartmental epidemiological models can produce measurables and actionable information for surveillance and planning purposes.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":"20 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A country-specific COVID-19 model\",\"authors\":\"G. Meissner, Hong Sherwin\",\"doi\":\"10.2139/ssrn.4043977\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Objectives To dynamically measure COVID-19 transmissibility consistently normalized by population size in each country. Methods A reduced-form model enhanced from the classical SIR is proposed to stochastically represent the Reproduction Number and Mortality Rate, directly measuring the combined effects of viral evolution and population behavioral response functions. Results Evidences are shown that this e(hanced)-SIR model has the power to fit country-specific empirical data, produce interpretable model parameters to be used for generating probabilistic scenarios adapted to the still unfolding pandemic. Conclusions Stochastic processes embedded within compartmental epidemiological models can produce measurables and actionable information for surveillance and planning purposes.\",\"PeriodicalId\":37999,\"journal\":{\"name\":\"Epidemiologic Methods\",\"volume\":\"20 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Epidemiologic Methods\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.4043977\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epidemiologic Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.4043977","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
Abstract Objectives To dynamically measure COVID-19 transmissibility consistently normalized by population size in each country. Methods A reduced-form model enhanced from the classical SIR is proposed to stochastically represent the Reproduction Number and Mortality Rate, directly measuring the combined effects of viral evolution and population behavioral response functions. Results Evidences are shown that this e(hanced)-SIR model has the power to fit country-specific empirical data, produce interpretable model parameters to be used for generating probabilistic scenarios adapted to the still unfolding pandemic. Conclusions Stochastic processes embedded within compartmental epidemiological models can produce measurables and actionable information for surveillance and planning purposes.
期刊介绍:
Epidemiologic Methods (EM) seeks contributions comparable to those of the leading epidemiologic journals, but also invites papers that may be more technical or of greater length than what has traditionally been allowed by journals in epidemiology. Applications and examples with real data to illustrate methodology are strongly encouraged but not required. Topics. genetic epidemiology, infectious disease, pharmaco-epidemiology, ecologic studies, environmental exposures, screening, surveillance, social networks, comparative effectiveness, statistical modeling, causal inference, measurement error, study design, meta-analysis