Modeling post-holiday surge in COVID-19 cases in Pennsylvania counties

Abstract

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.

Comparative Analysis of Epidemiological Outcome of Incidence, Mortality and Lethality by COVID-19 between the States of Espírito Santo and Minas Gerais, Brazil

“…Regarding the increase in the number of cases and deaths in months with specific holidays, a study carried out by Ren and Hwang [20] corroborates this research, which shows that approximately six to eight days after a holiday, there was an increase of 1.30 to 1.41 times the incidence rate. This expected time is directly related to the period of manifestation of symptoms and/or the results of the exam performed.…”

“…A study conducted in the United States revealed that the period significantly impacting the number of confirmed cases corresponds to holidays such as New Year’s Day, Martin Luther King Day, Good Friday, Independence Day, Memorial Day, Labor Day, Christmas, elections, vacations, among others [ 20 ]. In this regard, it aligns with the data found in our research, considering that these months also have similar holidays in Brazil, such as Christmas, New Year’s, elections, and vacation periods, leading to an increase in the number of people circulating, as well as group gatherings.…”

Comparative Analysis of Epidemiological Outcome of Incidence, Mortality and Lethality by COVID-19 between the States of Espírito Santo and Minas Gerais, Brazil

“…Regarding the increase in the number of cases and deaths in months with specific holidays, a study carried out by Ren and Hwang [20] corroborates this research, which shows that in approximately six to eight days after a holiday, there was an increase of 1.30 to 1.41 times the incidence rate. This expected time is directly related to the period of manifestation of symptoms and/or the results of the exam performed.…”

“…For the analyzes of a study carried out in the United States, federal holidays were considered, such as New Year, Martin Luther King Day, Good Friday, Independence Day, Memorial Day, Labor Day, Christmas, elections, vacations, among others [20]. This finding is in line with the results of the current study, considering that these are months that have similar holidays in Brazil, such as Christmas, New Year, elections, in addition to the vacation period, which leads to an increase in the number of people circulating, as well as, group meetings.…”