Purpose: To examine the time-varying reproduction number, Rt, for COVID-19 in Arkansas and Kentucky and investigate the impact of policies and preventative measures on the variability in Rt. Methods: Arkansas and Kentucky county-level COVID-19 cumulative case count data (March 6-November 7, 2020) were obtained. Rt was estimated using the R package 'EpiEstim', by county, region (Delta, non-Delta, Appalachian, non-Appalachian), and policy measures. Results: The Rt was initially high, falling below 1 in May or June depending on the region, before stabilizing around 1 in the later months. The median Rt for Arkansas and Kentucky at the end of the study were 1.15 (95% credible interval [CrI], 1.13, 1.18) and 1.10 (95% CrI, 1.08, 1.12), respectively, and remained above 1 for the non-Appalachian region. Rt decreased when facial coverings were mandated, changing by -10.64% (95% CrI, -10.60%, -10.70%) in Arkansas and -5.93% (95% CrI, -4.31%, -7.65%) in Kentucky. The trends in Rt estimates were mostly associated with the implementation and relaxation of social distancing measures. Conclusions: Arkansas and Kentucky maintained a median Rt above 1 during the entire study period. Changes in Rt estimates allows quantitative estimates of potential impact of policies such as facemask mandate.

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, Rt, 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 Rt estimates are more sensitive to fluctuation in incident cases than the GGM estimates. The median EpiEstim Rt estimate (1-week window) in Georgia dropped from between 2 and 4 in mid-March, to <2 in late March and early April. The median Rt dropped <1 for the first time on April 15. Rt fluctuates around 1 from mid-April to June 14. The Rt 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 Rt as an overall trend over a 3-month period, while EpiEstim is sensitive to daily variation in incidence.

Introduction: We aimed to examine how public health policies influenced the dynamics of COVID-19 time-varying reproductive number (R t ) in South Carolina from February 26, 2020 to January 1, 2021. Methods: COVID-19 case series (March 6, 2020 - January 10, 2021) were shifted by 9 days to approximate the infection date. We analyzed the effects of state and county policies on R t using EpiEstim. We performed linear regression to evaluate if per-capita cumulative case count varies across counties with different population size. Results: R t shifted from 2-3 in March to <1 during April and May. R t rose over the summer and stayed between 1.4 and 0.7. The introduction of statewide mask mandates was associated with a decline in R t (-15.3%; 95% CrI, -13.6%, -16.8%), and school re-opening, an increase by 12.3% (95% CrI, 10.1%, 14.4%). Less densely populated counties had higher attack rate (p<0.0001). Conclusion: The R t dynamics over time indicated that public health interventions substantially slowed COVID-19 transmission in South Carolina, while their relaxation may have promoted further transmission. Policies encouraging people to stay home, such as closing non-essential businesses, were associated with R t reduction, while policies that encouraged more movement, such as re-opening schools, were associated with R t increase.

Purpose To examine the time-varying reproduction number, R t , for COVID-19 in Arkansas and Kentucky and investigate the impact of policies and preventative measures on the variability in R t . Methods Arkansas and Kentucky county-level COVID-19 cumulative case count data (March 6-November 7, 2020) were obtained. R t was estimated using the R package ‘EpiEstim’, by county, region (Delta, non-Delta, Appalachian, non-Appalachian), and policy measures. Results The R t was initially high, falling below 1 in May or June depending on the region, before stabilizing around 1 in the later months. The median R t for Arkansas and Kentucky at the end of the study were 1.15 (95% credible interval [CrI], 1.13, 1.18) and 1.10 (95% CrI, 1.08, 1.12), respectively, and remained above 1 for the non-Appalachian region. R t decreased when facial coverings were mandated, changing by -10.64% (95% CrI, -10.60%, -10.70%) in Arkansas and -5.93% (95% CrI, -4.31%, -7.65%) in Kentucky. The trends in R t estimates were mostly associated with the implementation and relaxation of social distancing measures. Conclusions Arkansas and Kentucky maintained a median R t above 1 during the entire study period. Changes in R t estimates allows quantitative estimates of potential impact of policies such as facemask mandate.

Introduction: We aimed to examine how public health policies influenced the dynamics of COVID-19 time-varying reproductive number (Rt) in South Carolina from February 26, 2020 to January 1, 2021. Methods: COVID-19 case series (March 6, 2020 - January 10, 2021) were shifted by 9 days to approximate the infection date. We analyzed the effects of state and county policies on Rt using EpiEstim. We performed linear regression to evaluate if per-capita cumulative case count varies across counties with different population size. Results: Rt shifted from 2-3 in March to <1 during April and May. Rt rose over the summer and stayed between 1.4 and 0.7. The introduction of statewide mask mandates was associated with a decline in Rt (-15.3%; 95% CrI, -13.6%, -16.8%), and school re-opening, an increase by 12.3% (95% CrI, 10.1%, 14.4%). Less densely populated counties had higher attack rate (p<0.0001). Conclusion: The Rt dynamics over time indicated that public health interventions substantially slowed COVID-19 transmission in South Carolina, while their relaxation may have promoted further transmission. Policies encouraging people to stay home, such as closing non-essential businesses, were associated with Rt reduction, while policies that encouraged more movement, such as re-opening schools, were associated with Rt increase.