|
|
|
|
| LEADER |
01000caa a22002652c 4500 |
| 001 |
NLM360293301 |
| 003 |
DE-627 |
| 005 |
20250305030748.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2023 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1080/02664763.2021.1976119
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n1200.xml
|
| 035 |
|
|
|a (DE-627)NLM360293301
|
| 035 |
|
|
|a (NLM)37529559
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Zhang, Xiaolei
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Forecasting waved daily COVID-19 death count series with a novel combination of segmented Poisson model and ARIMA models
|
| 264 |
|
1 |
|c 2023
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 11.09.2023
|
| 500 |
|
|
|a published: Electronic-eCollection
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a © 2021 Informa UK Limited, trading as Taylor & Francis Group.
|
| 520 |
|
|
|a Autoregressive Integrated Moving Average (ARIMA) models have been widely used to forecast and model the development of various infectious diseases including COVID-19 outbreaks; however, such use of ARIMA models does not respect the count nature of the pandemic development data. For example, the daily COVID-19 death count series data for Canada and the United States (USA) are generally skewed with lots of low counts. In addition, there are generally waved patterns with turning points influenced by government major interventions against the spread of COVID-19 during different periods and seasons. In this study, we propose a novel combination of the segmented Poisson model and ARIMA models to handle these features and correlation structures in a two-stage process. The first stage of this process is a generalization of trend analysis of time series data. Our approach is illustrated with forecasting and modeling of daily COVID-19 death count series data for Canada and the USA
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Coronavirus disease
|
| 650 |
|
4 |
|a incidence
|
| 650 |
|
4 |
|a infectious diseases
|
| 650 |
|
4 |
|a prevalence
|
| 650 |
|
4 |
|a time series
|
| 700 |
1 |
|
|a Ma, Renjun
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 50(2023), 11-12 vom: 01., Seite 2561-2574
|w (DE-627)NLM098188178
|x 0266-4763
|7 nnas
|
| 773 |
1 |
8 |
|g volume:50
|g year:2023
|g number:11-12
|g day:01
|g pages:2561-2574
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1080/02664763.2021.1976119
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 50
|j 2023
|e 11-12
|b 01
|h 2561-2574
|