Modeling post-holiday surge in COVID-19 cases in Pennsylvania counties
COVID-19 arrived in the United States in early 2020, with cases quickly being reported in many states including Pennsylvania. Many statistical models have been proposed to understand the trends of the COVID-19 pandemic and factors associated with increasing cases. While Poisson regression is a natur...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762594/?tool=EBI |
| _version_ | 1828087836202500096 |
|---|---|
| author | Benny Ren Wei-Ting Hwang |
| author_facet | Benny Ren Wei-Ting Hwang |
| author_sort | Benny Ren |
| collection | DOAJ |
| description | COVID-19 arrived in the United States in early 2020, with cases quickly being reported in many states including Pennsylvania. Many statistical models have been proposed to understand the trends of the COVID-19 pandemic and factors associated with increasing cases. While Poisson regression is a natural choice to model case counts, this approach fails to account for correlation due to spatial locations. Being a contagious disease and often spreading through community infections, the number of COVID-19 cases are inevitably spatially correlated as locations neighboring counties with a high COVID-19 case count are more likely to have a high case count. In this analysis, we combine generalized estimating equations (GEEs) for Poisson regression, a popular method for analyzing correlated data, with a semivariogram to model daily COVID-19 case counts in 67 Pennsylvania counties between March 20, 2020 to January 23, 2021 in order to study infection dynamics during the beginning of the pandemic. We use a semivariogram that describes the spatial correlation as a function of the distance between two counties as the working correlation. We further incorporate a zero-inflated model in our spatial GEE to accommodate excess zeros in reported cases due to logistical challenges associated with disease monitoring. By modeling time-varying holiday covariates, we estimated the effect of holiday timing on case count. Our analysis showed that the incidence rate ratio was significantly greater than one, 6-8 days after a holiday suggesting a surge in COVID-19 cases approximately one week after a holiday. |
| first_indexed | 2024-04-11T05:17:42Z |
| format | Article |
| id | doaj.art-bce1883cec0644dc9cb08af44918a28c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T05:17:42Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-bce1883cec0644dc9cb08af44918a28c2022-12-24T05:33:25ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011712Modeling post-holiday surge in COVID-19 cases in Pennsylvania countiesBenny RenWei-Ting HwangCOVID-19 arrived in the United States in early 2020, with cases quickly being reported in many states including Pennsylvania. Many statistical models have been proposed to understand the trends of the COVID-19 pandemic and factors associated with increasing cases. While Poisson regression is a natural choice to model case counts, this approach fails to account for correlation due to spatial locations. Being a contagious disease and often spreading through community infections, the number of COVID-19 cases are inevitably spatially correlated as locations neighboring counties with a high COVID-19 case count are more likely to have a high case count. In this analysis, we combine generalized estimating equations (GEEs) for Poisson regression, a popular method for analyzing correlated data, with a semivariogram to model daily COVID-19 case counts in 67 Pennsylvania counties between March 20, 2020 to January 23, 2021 in order to study infection dynamics during the beginning of the pandemic. We use a semivariogram that describes the spatial correlation as a function of the distance between two counties as the working correlation. We further incorporate a zero-inflated model in our spatial GEE to accommodate excess zeros in reported cases due to logistical challenges associated with disease monitoring. By modeling time-varying holiday covariates, we estimated the effect of holiday timing on case count. Our analysis showed that the incidence rate ratio was significantly greater than one, 6-8 days after a holiday suggesting a surge in COVID-19 cases approximately one week after a holiday.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762594/?tool=EBI |
| spellingShingle | Benny Ren Wei-Ting Hwang Modeling post-holiday surge in COVID-19 cases in Pennsylvania counties PLoS ONE |
| title | Modeling post-holiday surge in COVID-19 cases in Pennsylvania counties |
| title_full | Modeling post-holiday surge in COVID-19 cases in Pennsylvania counties |
| title_fullStr | Modeling post-holiday surge in COVID-19 cases in Pennsylvania counties |
| title_full_unstemmed | Modeling post-holiday surge in COVID-19 cases in Pennsylvania counties |
| title_short | Modeling post-holiday surge in COVID-19 cases in Pennsylvania counties |
| title_sort | modeling post holiday surge in covid 19 cases in pennsylvania counties |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762594/?tool=EBI |
| work_keys_str_mv | AT bennyren modelingpostholidaysurgeincovid19casesinpennsylvaniacounties AT weitinghwang modelingpostholidaysurgeincovid19casesinpennsylvaniacounties |