Spatially Refined Time-Varying Reproduction Numbers of COVID-19 by Health District in Georgia, USA, March–December 2020

Abstract

This study quantifies the transmission potential of SARS-CoV-2 across public health districts in Georgia, USA, and tests if per capita cumulative case count varies across counties. To estimate the time-varying reproduction number, Rt of SARS-CoV-2 in Georgia and its 18 public health districts, we apply the R package ‘EpiEstim’ to the time series of historical daily incidence of confirmed cases, 2 March–15 December 2020. The epidemic curve is shifted backward by nine days to account for the incubation period and delay to testing. Linear regression is performed between log10-transformed per capita cumulative case count and log10-transformed population size. We observe Rt fluctuations as state and countywide policies are implemented. Policy changes are associated with increases or decreases at different time points. Rt increases, following the reopening of schools for in-person instruction in August. Evidence suggests that counties with lower population size had a higher per capita cumulative case count on June 15 (slope = −0.10, p = 0.04) and October 15 (slope = −0.05, p = 0.03), but not on August 15 (slope = −0.04, p = 0.09), nor December 15 (slope = −0.02, p = 0.41). We found extensive community transmission of SARS-CoV-2 across all 18 health districts in Georgia with median 7-day-sliding window Rt estimates between 1 and 1.4 after March 2020.

SARS-CoV-2 Transmission Potential and Policy Changes in South Carolina, February 2020 – January 2021

“…17 We performed linear regression analysis between the log 10transformed per capita cumulative case count and the log 10 -transformed population size, i.e., log 10 (C/N) = m log 10 (N) where m = g-1. [17][18][19] We computed linear regression between the log 10transformed per capita cumulative case count and the log 10 -transformed population size, at four different dates: June 30 th , August 31 st , October 31 st , and December 31 st .…”

SARS-CoV-2 Transmission Potential and Policy Changes in South Carolina, February 2020 – January 2021

“…17 We performed linear regression analysis between the log10transformed per capita cumulative case count and the log10-transformed population size, i.e., log10(C/N) = m log10(N) where m = g-1. [17][18][19] We computed linear regression between the log10-transformed per capita cumulative case count and the log10-transformed population size, at four different dates: June 30 th , August 31 st , October 31 st , and December 31 st .…”

“…The power-law relationship between the county-level cumulative number of COVID-19 cases and population size can be transformed into a linear relationship between the log10-transformed cumulative case count and the log10-transformed population size as follows 1,2 : In this paper, we performed linear regression models between log10-transformed per capita cumulative case count and log10-transformed population size of counties in South Carolina. The data analyzed were by the dates of report of June 30, August 31, October 31 and December 31, 2020.…”

“…By January 2020, this virus had been identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19). 1 The disease was first reported in the United States in January 2020. 2 With an estimated population of 5,190,705, South Carolina is in the southeastern United States, along the Atlantic coast, and shares borders with North Carolina and Georgia.…”

Data-Driven Deep-Learning Algorithm for Asymptomatic COVID-19 Model with Varying Mitigation Measures and Transmission Rate

“…A model-fitting approach for lockdown and lockdown relaxation is presented in [11], which requires good estimation of the model parameters as well as quantification of the impact of relaxation. In [12], the time-varying reproduction number R t is estimated for counties in Georgia, USA, with a 95% confidence credible interval.…”