Proximity to international airports and early transmission of COVID-19 in the United States-An epidemiological assessment of the geographic distribution of 490,000 cases

Abstract

Background: Identifying hotspots in a pandemic is essential for early containment. In the context of the rapid global dissemination of the Covid-19 pandemic, describing viral infection rates in relation to international air travel early during the pandemic can help inform future public health policy. The objective of this study is to determine whether proximity to an international airport predicted higher infection rates during the early phase of the Covid-19 pandemic in the United States (US).

Methods: In this cross-sectional study, the authors examined the incidence of Covid-19 in areas near US international airports in the first weeks after detection of Covid-19 in all 50 states, using publicly available county-level incidence of Covid-19 data. They performed a multiple regression to determine the relative effects of population density and air traffic in the Counties Containing Airports (CCA) and the number of Covid-19 cases, and determined the odds of Covid-19 in CCA compared to the rest of the state.

Results: Multiple regression analysis revealed that air traffic was significantly correlated with Covid-19 cases during the initial phase of pandemic while population density was not significantly correlated. Three weeks into the pandemic, the pooled odds of Covid-19 cases in CCA was 2.66 (95% CI [2.64, 2.68], p < 0.0001).

Conclusions: The counties in the US containing international airports represented initial hotspots for Covid-19 transmission. Early public health containment efforts focused on these areas may help mitigate disease transmission during future similar novel respiratory virus epidemics.

Keywords: Containment; Covid-19; Hotspots; Public health interventions.

Fig. 1

Scatter Plot Representing Correlation Between Air Traffic and Covid-19 Cases. Air Traffic and Covid-19 cases are significantly correlated with p-value of 0.004 per multivariable regression and correlation coefficient of 95.75.

Fig. 2

Scatter Plot Representing Correlation Between Population Density and Covid-19 Cases. Population Density and Covid-19 cases are not significantly correlated with p-value of 0.377 per multivariable regression and correlation coefficient of 0.10.

Fig. 3

Forest plot showing odds ratios of Covid-19 in Counties Containing Airports (CCA) Compared to the Rest of the State as of April 10, 2020. For each state, the odds ratio of Covid-19 in CCA compared to the rest of the state is shown by the diamond (◆) and the 95% confidence interval is depicted using the error bars. The overall odds ratio of Covid-19 in CCA compared to all other areas in the 23 states is shown by the large black diamond. The red line indicates an odds ratio of 1.0. Odds ratios not overlapping 1.0 are significant with a p-value<0.0001. These odds ratios are calculated based on April 10, 2020 Covid-19 cases. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4

Forest plot showing odds ratios of Covid-19 in Counties Containing Airports (CCA) Compared to the Rest of the State as of November 29, 2020. For each state, the odds ratio of Covid-19 in CCA compared to the rest of the state is shown by the diamond (◆) and the 95% confidence interval is depicted using the error bars. The overall odds ratio of Covid-19 in CCA compared to all other areas in the 23 states is shown by the large black diamond. The red line indicates an odds ratio of 1.0. These odds ratios are calculated based on November 29, 2020 Covid-19 cases. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)