Time-varying reproduction numbers of COVID-19 in Georgia, USA, March 2-June 14, 2020

Abstract

<jats:p>Objective: In 2020, SARS-CoV-2 impacted Georgia, USA. Georgia announced state-wide shelter-in-place on April 2 (implemented the next day) and announced the partial lifting of restrictions on April 27. Time-varying reproduction number estimates might vary depending on methodology. Methods: Daily incidence of confirmed COVID-19 cases by reporting date, March 2-June 14, 2020, in Georgia, Metro Atlanta, and Dougherty County were analyzed. We estimated and compared the COVID-19 time-varying reproduction number, <jats:italic>R<jats:sub>t</jats:sub></jats:italic>, by R package EpiEstim (with a window of 1 and 2 weeks) and the renewal equation tied to the generalized growth model (GGM). Results: The EpiEstim <jats:italic>R<jats:sub>t</jats:sub></jats:italic> estimates are more sensitive to fluctuation in incident cases than the GGM estimates. The median EpiEstim <jats:italic>R<jats:sub>t</jats:sub></jats:italic> estimate (1-week window) in Georgia dropped from between 2 and 4 in mid-March, to &lt;2 in late March and early April. The median <jats:italic>R<jats:sub>t</jats:sub></jats:italic> dropped &lt;1 for the first time on April 15. Rt fluctuates around 1 from mid-April to June 14. The <jats:italic>R<jats:sub>t</jats:sub></jats:italic> estimate by GGM dropped below 2 on March 7 and stabilized to the range of 1 to 1.1 by March 31. Conclusions: GGM estimated <jats:italic>R<jats:sub>t</jats:sub></jats:italic> as an overall trend over a 3-month period, while EpiEstim is sensitive to daily variation in incidence.</jats:p>

Spatially refined time-varying reproduction numbers of SARS-CoV-2 in Arkansas and Kentucky and their relationship to population size and public health policy, March – November 2020

“…Several studies have examined the R t estimates with respect to policy and interventions and used R t estimates as predictive models and quantitative measures of epidemic growth or decline. [31] , [32] , [33] Here, the R t trajectories of Arkansas and Kentucky differed among rural and urban areas, increasing or decreasing, depending on the implementation of preventative and relaxation measures. The R t will be useful as the pandemic progresses to inform policymakers and public health professions of the direction of potential outbreaks, assisting in preventing health care surges and implementing more preventative measures and policies.…”

Spatially refined time-varying reproduction numbers of SARS-CoV-2 in Arkansas and Kentucky and their relationship to population size and public health policy, March – November, 2020

“…Rt estimates as predictive models and quantitative measures of epidemic growth or decline. [30][31][32] Here, the Rt trajectories of Arkansas and Kentucky differed among rural and urban areas, increasing or decreasing, depending on the implementation of preventative and relaxation measures. The Rt will be useful as the pandemic progresses to inform policymakers and public health professions of the direction of potential outbreaks, assisting in preventing health care surges and implementing more preventative measures and policies.…”

SARS-CoV-2 transmission potential and rural-urban disease burden disparities across Alabama, Louisiana, and Mississippi, March 2020 — May 2021

“…28 The time series was shifted by nine days to approximate the onset of infection by assuming a mean incubation period of six days and a median testing delay of three days. 25,[29][30][31] Two sets of time window arrangements were used. First, the 7-day sliding window was used to minimize the fluctuations observed with smaller time steps by taking the average of Rt estimates over a week.…”

“…28 The time series was shifted by nine days to approximate the onset of infection by assuming a mean incubation period of six days and a median testing delay of three days. 25,29-31…”