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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1080/02664763.2021.1953449
|2 doi
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|a eng
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| 100 |
1 |
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|a Zhang, Peiyi
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Identification of factors impacting on the transmission and mortality of COVID-19
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Revised 11.09.2023
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a © 2021 Informa UK Limited, trading as Taylor & Francis Group.
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|a This paper proposes a dynamic infectious disease model for COVID-19 daily counts data and estimate the model using the Langevinized EnKF algorithm, which is scalable for large-scale spatio-temporal data, converges to the right filtering distribution, and is thus suitable for performing statistical inference and quantifying uncertainty for the underlying dynamic system. Under the framework of the proposed dynamic infectious disease model, we tested the impact of temperature, precipitation, state emergency order and stay home order on the spread of COVID-19 based on the United States county-wise daily counts data. Our numerical results show that warm and humid weather can significantly slow the spread of COVID-19, and the state emergency and stay home orders also help to slow it. This finding provides guidance and support to future policies or acts for mitigating the community transmission and lowering the mortality rate of COVID-19
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|a Journal Article
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|a Dynamic infectious disease model
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| 650 |
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|a Langevinized ensemble Kalman filter
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| 650 |
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|a spatio-temporal data
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| 650 |
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4 |
|a state-space model
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| 650 |
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4 |
|a surveillance data
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| 700 |
1 |
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|a Dong, Tianning
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Ninghui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liang, Faming
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of applied statistics
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|g 50(2023), 11-12 vom: 01., Seite 2624-2647
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|g volume:50
|g year:2023
|g number:11-12
|g day:01
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|u http://dx.doi.org/10.1080/02664763.2021.1953449
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