COVID-19 epidemic doubling time by Chinese province was increasing from January 20 through February 9, 2020. The harmonic mean of the arithmetic mean doubling time estimates ranged from 1.4 (Hunan, 95% CI, 1.2-2.0) to 3.1 (Xinjiang, 95% CI, 2.1-4.8), with an estimate of 2.5 days (95% CI, 2.4-2.6) for Hubei.

To describe the geographical heterogeneity of COVID-19 across prefectures in mainland China, we estimated doubling times from daily time series of the cumulative case count between 24 January and 24 February 2020. We analyzed the prefecture-level COVID-19 case burden using linear regression models and used the local Moran’s I to test for spatial autocorrelation and clustering. Four hundred prefectures (~98% population) had at least one COVID-19 case and 39 prefectures had zero cases by 24 February 2020. Excluding Wuhan and those prefectures where there was only one case or none, 76 (17.3% of 439) prefectures had an arithmetic mean of the epidemic doubling time <2 d. Low-population prefectures had a higher per capita cumulative incidence than high-population prefectures during the study period. An increase in population size was associated with a very small reduction in the mean doubling time (−0.012, 95% CI, −0.017, −0.006) where the cumulative case count doubled ≥3 times. Spatial analysis revealed high case count clusters in Hubei and Heilongjiang and fast epidemic growth in several metropolitan areas by mid-February 2020. Prefectures in Hubei and neighboring provinces and several metropolitan areas in coastal and northeastern China experienced rapid growth with cumulative case count doubling multiple times with a small mean doubling time.

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.