The Impact of Early or Late Lockdowns on the Spread of COVID-19 in US Counties

Background: COVID-19 is a highly transmissible infectious disease that has infected over 122 million individuals worldwide. To combat this pandemic, governments around the world have imposed lockdowns. However, the impact of these lockdowns on the rates of COVID-19 transmission in communities is not well-known. Here, we used COVID-19 case counts from 3,000+ counties in the United States (US) to determine the relationship between lockdown as well as other county factors and the rate of COVID-19 spread in these communities. Methods: We merged county-specific COVID-19 case counts with US census data and the date of lockdown for each of the counties. We then applied a Functional Principal Component (FPC) analysis on this dataset to generate scores that described the trajectory of COVID-19 spread across the counties. We used machine learning methods to identify important factors in the county including the date of lockdown that significantly influenced the FPC scores. Findings: We found that the first FPC score accounted for up to 92.81% of the variations in the absolute rates and the topology of COVID-19 spread over time at a county level. The relation between incidence of COVID-19 and time at a county level demonstrated a hockey-stick appearance with an inflection point approximately 7 days prior to the county reporting at least 5 new cases of COVID-19; beyond this inflection point, there was an exponential increase in incidence. Among the risk factors, lockdown and total population were the two most significant features of the county that influenced the rate of COVID-19 infection, while the median family income, median age and within-county move also substantially affect COVID spread. Interpretation: Lockdowns are an effective way of controlling the COVID-19 spread in communities. However, significant delays in lockdown cause a dramatic increase in the case counts. Thus, the timing of the lockdown relative to the case count is an important consideration in controlling the pandemic in communities. Funding Statement: Dr. Xuekui Zhang is funded by Canada Research Chairs. Grant Number: 950-231363 and Natural Sciences and Engineering Research Council of Canada. Grant Number: RGPIN-2017-04722. 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. Declaration of Interests: Don Sin: Professor Sin reports grants from Merck, personal fees from Sanofi-Aventis, personal fees from Regeneron, grants and personal fees from Boehringer Ingelheim, grants and personal fees from AstraZeneca, personal fees from Novartis, outside the submitted work. Other coauthors have nothing to declare.