Findings
At the country-level, there appears to be no discernable relationship between percentage of population fully vaccinated and new COVID-19 cases in the last 7 days (Fig.
1). In fact, the trend line suggests a marginally positive association such that countries with higher percentage of population fully vaccinated have higher COVID-19 cases per 1 million people. Notably, Israel with over 60% of their population fully vaccinated had the highest COVID-19 cases per 1 million people in the last 7 days. The lack of a meaningful association between percentage population fully vaccinated and new COVID-19 cases is further exemplified, for instance, by comparison of Iceland and Portugal. Both countries have over 75% of their population fully vaccinated and have more COVID-19 cases per 1 million people than countries such as Vietnam and South Africa that have around 10% of their population fully vaccinated.
Of the top 5 counties that have the highest percentage of population fully vaccinated (99.9–84.3%), the US Centers for Disease Control and Prevention (CDC) identifies 4 of them as “High” Transmission counties. Chattahoochee (Georgia), McKinley (New Mexico), and Arecibo (Puerto Rico) counties have above 90% of their population fully vaccinated with all three being classified as “High” transmission. Conversely, of the 57 counties that have been classified as “low” transmission counties by the CDC, 26.3% (15) have percentage of population fully vaccinated below 20%.
Since full immunity from the vaccine is believed to take about 2 weeks after the second dose, we conducted sensitivity analyses by using a 1-month lag on the percentage population fully vaccinated for countries and US counties. The above findings of no discernable association between COVID-19 cases and levels of fully vaccinated was also observed when we considered a 1-month lag on the levels of fully vaccinated (Supplementary Figure 1, Supplementary Figure 2).
We should note that the COVID-19 case data is of confirmed cases, which is a function of both supply (e.g., variation in testing capacities or reporting practices) and demand-side (e.g., variation in people’s decision on when to get tested) factors.