Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA.
Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA.
Pathog Glob Health. 2024 Feb;118(1):65-79. doi: 10.1080/20477724.2023.2201984. Epub 2023 Apr 19.
To study the SARS-CoV-2 transmission potential in Rhode Island (RI) and its association with policy changes and mobility changes, the time-varying reproduction number, R, was estimated. The daily incident case counts (16 March 2020, through 30 November 2021) were bootstrapped within a 15-day sliding window and multiplied by Poisson-distributed multipliers (λ = 4, sensitivity analysis: 11) to generate 1000 estimated infection counts, to which EpiEstim was applied to generate R time series. The median R percentage change when policies changed was estimated. The time lag correlations were assessed between the 7-day moving average of the relative changes in Google mobility data in the first 90 days, and R and estimated infection count, respectively. There were three major pandemic waves in RI in 2020-2021: spring 2020, winter 2020-2021 and fall-winter 2021. The median R fluctuated within the range of 0.5-2 from April 2020 to November 2021. Mask mandate (18 April 2020) was associated with a decrease in R (-25.99%, 95% CrI: -37.42%, -14.30%). Termination of mask mandates on 6 July 2021 was associated with an increase in R (36.74%, 95% CrI: 27.20%, 49.13%). Positive correlations were found between changes in grocery and pharmacy, R retail and recreation, transit, and workplace visits, for both R and estimated infection count, respectively. Negative correlations were found between changes in residential area visits for both Rt and estimated infection count, respectively. Public health policies enacted in RI were associated with changes in the pandemic trajectory. This ecological study provides further evidence of how non-pharmaceutical interventions and vaccination slowed COVID-19 transmission in RI.
为了研究罗德岛（RI）的 SARS-CoV-2 传播潜力及其与政策变化和流动性变化的关系，我们估计了时变繁殖数 R。通过 15 天的滑动窗口对每日新增病例数（2020 年 3 月 16 日至 2021 年 11 月 30 日）进行了自举，并乘以泊松分布乘数（λ=4，敏感性分析：11），生成了 1000 次估计的感染数，然后将 EpiEstim 应用于生成 R 时间序列。当政策发生变化时，估计了政策变化时 R 的百分比变化中位数。评估了谷歌流动性数据在最初 90 天内的相对变化的 7 天移动平均值与 R 和估计的感染数之间的时间滞后相关性。2020-2021 年，罗德岛发生了三次大流行浪潮：2020 年春季、2020-2021 年冬季和 2021 年秋季-冬季。2020 年 4 月至 2021 年 11 月，R 的中位数在 0.5-2 范围内波动。口罩强制令（2020 年 4 月 18 日）与 R 的减少相关（-25.99%，95%CrI：-37.42%，-14.30%）。2021 年 7 月 6 日终止口罩强制令与 R 的增加相关（36.74%，95%CrI：27.20%，49.13%）。杂货和药店、R 零售和娱乐、交通和工作场所访问的变化与 R 和估计的感染数之间均存在正相关关系。住宅区域访问的变化与 Rt 和估计的感染数之间均存在负相关关系。罗德岛实施的公共卫生政策与大流行轨迹的变化有关。这项生态研究提供了更多证据，表明非药物干预措施和疫苗如何减缓了罗德岛的 COVID-19 传播。
