病毒学：当前研究

Consistence Condition of Kernel Selection in Regular Linear Kernel Regression and Its Application in COVID-19 High-risk Areas Exploration

Abstract

Lu xan , Ba lin

With the long-term outbreak of the COVID-19 around the world, identi- fying high-risk areas is becoming a new research boom. In this paper, we propose a novel regression method namely Regular Linear Kernel Regression (RLKR) for COVID-19 high-risk areas exploration. We explain in detail how the canonical linear kernel regression method is linked to the identification of high-risk areas for COVID-19. Furthermore, the consistence condition of Kernel Selection, which is closely related to the identification of high-risk areas, is given with two mild assumptions. Finally, the RLKR method was verified by simulation experiments and applied for COVID-19 high-risk area Exploration