The impact of lockdown timing on COVID-19 transmission across US counties

Abstract

Background: Many countries have implemented lockdowns to reduce COVID-19 transmission. However, there is no consensus on the optimal timing of these lockdowns to control community spread of the disease. Here we evaluated the relationship between timing of lockdowns, along with other risk factors, and the growth trajectories of COVID-19 across 3,112 counties in the US.

Methods: We ascertained dates for lockdowns and implementation of various non-pharmaceutical interventions at a county level and merged these data with those of US census and county-specific COVID-19 daily cumulative case counts. We then applied a Functional Principal Component (FPC) analysis on this dataset to generate FPC scores, which were used as a surrogate variable to describe the trajectory of daily cumulative case counts for each county. We used machine learning methods to identify risk factors including the timing of lockdown that significantly influenced the FPC scores.

Findings: We found that the first eigen-function accounted for most (>92%) of the variations in the daily cumulative case counts. The impact of lockdown timing on the total daily case count of a county became significant beginning approximately 7 days prior to that county reporting at least 5 cumulative cases of COVID-19. Delays in lockdown implementation after this date led to a rapid acceleration of COVID-19 spread in the county over the first ~50 days from the date with at least 5 cumulative cases, and higher case counts across the entirety of the follow-up period. Other factors such as total population, median family income, Gini index, median age, and within-county mobility also had a substantial effect. When adjusted for all these factors, the timing of lockdowns was the most significant risk factor associated with the county-specific daily cumulative case counts.

Interpretation: Lockdowns are an effective way of controlling the spread of COVID-19 in communities. Significant delays in lockdown cause a dramatic increase in the cumulative case counts. Thus, the timing of the lockdown relative to the case count is an important consideration in controlling the pandemic in communities.

Funding: The study period is from June 2020 to July 2021. Dr. Xuekui Zhang is a Tier 2 Canada Research Chairs (Grant No. 950231363) and funded by Natural Sciences and Engineering Research Council of Canada (Grant No. RGPIN201704722). Dr. Li Xing is funded by Natural Sciences and Engineering Research Council of Canada (Grant Number: RGPIN 202103530). This research was enabled in part by support provided by WestGrid (www.westgrid.ca) and Compute Canada (www.computecanada.ca). The computing resource is provided by Compute Canada Resource Allocation Competitions #3495 (PI: Xuekui Zhang) and #1551 (PI: Li Xing). Dr. Don Sin is a Tier 1 Canada Research Chair in COPD and holds the de Lazzari Family Chair at the Heart Lung Innovation, Vancouver, Canada.

Keywords: Covid-19; Elastic net; Functional principal component analysis; Lockdown.

Fig. 1

The mean curve of COVID-19 cumulative case trajectories. In the upper panel, the red curve shows the (cumulative) percentages of “late-lockdown” counties which locked down during the follow-up period. Day zero is defined as the date on which a county reported at least 5 cumulative COVID-19 cases. Late-lockdown was defined as implementing lockdown after the inflection point (which occurred approximately 7 days prior to day 0). Blue line denotes “early-lockdown” counties. In the lower panel, dotted curve $\mu \left(\right)$ represents the national average of COVID-19 cases over time. The blue curve represents the average COVID-19 count trajectories of counties that implemented a lockdown before the inflection point, while the red curve represents the average trajectories of counties with lockdown after the inflection point. The shaded area represents confidence bound constructed using interquartile range (i.e., 25−75% quantiles) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

Fig. 2

Heat map of the United States (US) according to the first FPC scores of counties.

Fig. 3

Relationship between the first FPC score and the first lockdown date. The x-axis represents the number of days between the lockdown date and the date on which the county reported at least 5 COVID-19 cases. Positive values denote counties that instituted a lockdown after they reported at least 5 cumulative COVID-19 cases, while negative values denote counties that instituted a lockdown before they reported at least 5 cumulative COVID-19 cases. Each blue point represents data of a US county. The red hockey-stick shape line represents two fitted slopes of a segmented regression model. The vertical green line (at −7.8 days) indicates the inflection point on which the slope of the first FPC score significantly changes (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

Fig. 4

The adjusted relationship between standardized characteristics of counties and the first FPC scores, based on results of elastic net models. The effect of every variable is adjusted to other factors listed in the figure. A positive coefficient denotes variables that are positively related to the number of COVID cases. The dot indicates the mean coefficients, and the bar represents the 95% confidence interval. Blue color indicates the significant factors whose 95% confidence interval does not cover 0 (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).