#Will the us go on total lockdown full#
In contrast, the late-lockdown counties did not achieve full lockdown until approximately day 25. Since we normalized the trajectories by defining day-0 as the day on which a county reported first 5 cumulative cases and “early” versus “late” lockdown was dichotomized based on implementation of a lockdown approximately 7 days prior to day 0, all “early-lockdown” counties were by definition locked-down at day 0. The upper panel shows the percentages of counties which implemented lockdown at each day, grouped by early (blue) or late (red) lockdown. Furthermore, an early lockdown was associated with a slower increase in the rate of COVID-19 counts for the first 50 days of the pandemic. An early lockdown (before the inflection point) was associated with a lower case count than the national average across the entirety of the follow-up period whereas the opposite was true for late lockdowns (defined as occurring after the inflection point). The shaded area represents the confidence intervals constructed using the interquartile range (i.e., 25–75% quartiles). In the lower panel, the blue/red curve represents the average COVID-19 case count trajectories of counties that implemented a lockdown before/after the inflection point. Here, we fitted all elastic net models using R Package ‘glmnet’ [įig. 1 shows the average trajectory of COVID-19 daily cumulative counts across all US counties, which is denoted by the function μ ( ). Next, we applied an elastic net model to each of these random subsets to generate 1000 sets of estimated coefficients, and then built a 95% confidence interval using these coefficients. Specifically, we sampled the counties for replication 1000 times.
To capture the uncertainty of the risk estimates, we generated 95% confidence intervals for each coefficient using a re-sample (bootstrap) approach. Second, the elastic net penalty addresses the issue of multi-collinearity among predictors, which makes models more reliable than multiple regressions. However, elastic net does not provide confidence intervals for coefficients. First, elastic net can automatically select important predictors in a linear model (2) by automatically assigning a zero coefficient to unimportant predictors via a penalty on absolute values of coefficients. The statistical technical details of the segmented model are provided in supplementary document under section “ Modeling lockdown effect using segmented regression.” 2.2.3 Modeling joint effects of all risk factors simultaneously using supervised machine learningĬompared with multiple linear regression, elastic net incorporates various penalties on coefficients and provides better prediction models. The inflection point for the lockdown variable was ascertained via the significant change in the slopes before and after the inflection point. Thus, we derived three new variables from the timing of lockdown: a binary indicator of lockdown implementation, a slope before inflection point (denotes the effect of the lockdown timing when implemented before the inflection point), and a slope after inflection point (denotes the effect of the lockdown timing when implemented after the inflection point), and used segmented regression to model this relationship. The Lancet Regional Health – Western Pacificįig. 3 shows that the observed relationship between the first FPC scores and the timing of lockdowns was non-linear: its appearance was that of a “hockey stick” with an inflection point indicating a significant change in its slope.The Lancet Regional Health – Southeast Asia.The Lancet Gastroenterology & Hepatology.