Benchmarking the Covid-19 pandemic across countries and states in the USA under heterogeneous testing.

Scientists and policymakers need to compare the incidence of Covid-19 across territories or periods with various levels of testing. Benchmarking based on the increase in total cases or case fatality rates is one way of comparing the evolution of the pandemic across countries or territories and could inform policy decisions about strategies to control coronavirus transmission. However, comparing cases and fatality rates across regions is challenging due to heterogeneity in testing and health systems. We show two complementary ways of benchmarking across territories and in time. First, we used multivariate regressions to estimate the test-elasticity of Covid-19 case incidence. Cases grow less than proportionally with testing when assessing weekly changes or looking across states in the USA. They tend to be proportional or even more than proportional when comparing the month-to-month evolution of an average country in the pandemic. Our results were robust to various model specifications. Second, we decomposed the growth in cases into test growth and positive test ratio growth to intuitively visualize the components of case growth. We hope these results can help support evidence-based decisions by public officials and help the public discussion when comparing across territories and in time.

The effect of COVID-19 on the economy: Evidence from an early adopter of localized lockdowns.

BACKGROUND: Governments worldwide have implemented large-scale non-pharmaceutical interventions, such as social distancing or school closures, to prevent and control the growth of the COVID-19 pandemic. These strategies, implemented with varying stringency, have imposed substantial social and economic costs to society. As some countries begin to reopen and ease mobility restrictions, lockdowns in smaller geographic areas are increasingly considered an attractive policy intervention to mitigate societal costs while controlling epidemic growth. Nevertheless, there is a lack of empirical evidence to support these decisions. METHODS: Drawing from a rich data set of localized lockdowns in Chile, we used econometric methods to measure the reduction in local economic activity from lockdowns when applied to smaller or larger geographical areas. We measured economic activity by tax collection at the municipality-level. RESULTS: Our results show that lockdowns were associated with a 10%-15% drop in local economic activity, which is twice the reduction in local economic activity suffered by municipalities that were not under lockdown. A three-to-four-month lockdown had a similar effect on economic activity than a year of the 2009 great recession. We found costs are proportional to the population under lockdown, without differences when lockdowns were measured at the municipality or city-wide levels. CONCLUSIONS: Our findings suggest that localized lockdowns have a large effect on local economic activity, but these effects are proportional to the population under lockdown. Our results suggest that epidemiological criteria should guide decisions about the optimal size of lockdown areas since the proportional impact of lockdowns on the economy seems to be unchanged by scale.