tetano
Editor, Senior Moderator
Math Biosci Eng
. 2021 Mar 16;18(3):2579-2598.
doi: 10.3934/mbe.2021131.
Linking dynamic patterns of COVID-19 spreads in Italy with regional characteristics: a two level longitudinal modelling approach
Youtian Hao[SUP] 1 [/SUP], Guohua Yan[SUP] 1 [/SUP], Renjun Ma[SUP] 1 [/SUP], M Tariqul Hasan[SUP] 1 [/SUP]
Affiliations
Abstract
The current statistical modeling of coronavirus (COVID-19) spread has mainly focused on spreading patterns and forecasting of COVID-19 development; these patterns have been found to vary among locations. As the survival time of coronaviruses on surfaces depends on temperature, some researchers have explored the association of daily confirmed cases with environmental factors. Furthermore, some researchers have studied the link between daily fatality rates with regional factors such as health resources, but found no significant factors. As the spreading patterns of COVID-19 development vary a lot among locations, fitting regression models of daily confirmed cases or fatality rates directly with regional factors might not reveal important relationships. In this study, we investigate the link between regional spreading patterns of COVID-19 development in Italy and regional factors in two steps. First, we characterize regional spreading patterns of COVID-19 daily confirmed cases by a special patterned Poisson regression model for longitudinal count; the varying growth and declining patterns as well as turning points among regions in Italy have been well captured by regional regression parameters. We then associate these regional regression parameters with regional factors. The effects of regional factors on spreading patterns of COVID-19 daily confirmed cases have been effectively evaluated.
Keywords: daily confirmed cases; hierarchical model; novel coronavirus disease; temperature effect; turning point.
. 2021 Mar 16;18(3):2579-2598.
doi: 10.3934/mbe.2021131.
Linking dynamic patterns of COVID-19 spreads in Italy with regional characteristics: a two level longitudinal modelling approach
Youtian Hao[SUP] 1 [/SUP], Guohua Yan[SUP] 1 [/SUP], Renjun Ma[SUP] 1 [/SUP], M Tariqul Hasan[SUP] 1 [/SUP]
Affiliations
- PMID: 33892561
- DOI: 10.3934/mbe.2021131
Abstract
The current statistical modeling of coronavirus (COVID-19) spread has mainly focused on spreading patterns and forecasting of COVID-19 development; these patterns have been found to vary among locations. As the survival time of coronaviruses on surfaces depends on temperature, some researchers have explored the association of daily confirmed cases with environmental factors. Furthermore, some researchers have studied the link between daily fatality rates with regional factors such as health resources, but found no significant factors. As the spreading patterns of COVID-19 development vary a lot among locations, fitting regression models of daily confirmed cases or fatality rates directly with regional factors might not reveal important relationships. In this study, we investigate the link between regional spreading patterns of COVID-19 development in Italy and regional factors in two steps. First, we characterize regional spreading patterns of COVID-19 daily confirmed cases by a special patterned Poisson regression model for longitudinal count; the varying growth and declining patterns as well as turning points among regions in Italy have been well captured by regional regression parameters. We then associate these regional regression parameters with regional factors. The effects of regional factors on spreading patterns of COVID-19 daily confirmed cases have been effectively evaluated.
Keywords: daily confirmed cases; hierarchical model; novel coronavirus disease; temperature effect; turning point.