Two Burning Questions on COVID-19: Did shutting down the economy help? Can we (partially) reopen the economy without risking the second wave?

Abstract

As we reach the apex of the COVID-19 pandemic across the globe, the most pressing question facing us all is: can we, even partially, reopen the economy without risking the second wave? Towards answering this question, we first need to understand if shutting down the economy helped. And second if it did, is it possible to achieve similar gains in the war against the pandemic while partially opening up the economy? To do so, it is critical to understand the effects of the various interventions that can be put into place and their corresponding health and economic implications. Since many possible interventions exist, the key challenge facing policy makers is understanding the potential trade-offs between them, and choosing the particular set of interventions that works best for their circumstance -for example, what would be the effect on COVID-19 related deaths if the U.S. had decreased mobility by 20% instead of 60% versus if Italy had done the same?In this memo, we provide an overview of Synthetic Interventions [3] (a natural generalization of the widely applied Synthetic Control method [2]), which is a data-driven and statistically principled method to perform what-if scenario planning, i.e., for policy makers to understand the trade-offs between different interventions before having to actually enact them. In essence, the Synthetic Interventions method leverages information from different interventions that have already been enacted across the world and fits it to a policy maker's setting of interest -for instance, to estimate the effect of mobility-restricting interventions on the U.S., we use daily death data from countries that enforced severe mobility restrictions to create a "synthetic low mobility U.S." and predict the counterfactual trajectory of the U.S. if it had indeed applied a similar intervention.We use the Synthetic Interventions method towards answering the two questions laid out above via a case study of the effectiveness of different interventions in mitigating the COVID-19 pandemic at the international level. Encouragingly, we find that lifting severe mobility restrictions and only retaining moderate mobility restrictions (at retail and transit locations), seems to effectively flatten the curve. We hope that this will provide useful guidance for policy makers as they weigh the trade-offs between the safety of the population, strain on the healthcare system, and impact on the economy.

Minimizing post-shock forecasting error through aggregation of outside information

“…However, there is a tremendous amount of volatility in this form of COVID-19 data, and the fit of this prediction method may be improved with modeling structure or preprocessing of the donor pool. Agarwal et al [2020] proposed a model-free synthetic intervention method to predict unobserved potential outcomes after different interventions given a donor pool of observed outcomes with given interventions. They also provided useful guidelines for how to estimate the effects of potential interventions by giving recommendations for choosing the metric of interest, the intervention of interest, time horizons, and the donor pool.…”

“…They also provided useful guidelines for how to estimate the effects of potential interventions by giving recommendations for choosing the metric of interest, the intervention of interest, time horizons, and the donor pool. Although the methodology in Agarwal et al [2020] is quite general, there is no guarantee for theoretical properties in prediction without assuming any distributional structure.…”

“…The core idea of our methodology is to sensibly aggregate similar past realized shock effects which arose from other time series, and then incorporate the aggregated shock effect estimator into the present forecast. Our method of combining shock effects embraces ideas from conditional forecasting Kilian, 2014b, Kilian andLütkepohl, 2017], time series pooling using cross-sectional panel data [Ramaswamy et al, 1993, Pesaran et al, 1999, Hoogstrate et al, 2000, Baltagi, 2008, Koop and Korobilis, 2012, Liu et al, 2020, forecasting with judgement and models [Svensson, 2005, Monti, 2008, synthetic control methodology [Abadie et al, 2010, Agarwal et al, 2020, and expectation shocks [Croushore and Evans, 2006, Baumeister and Kilian, 2014a, Clements et al, 2019.…”

The Effectiveness of Mobility Restrictions on Controlling the Spread of COVID-19 in a Resistant Population

“…The second type of studies focused on policymaking, e.g., testing different mobility interventions to find optimal mobility reductions that balance the cost of viral spread with the economic cost associated with lockdowns, as well as implementing prediction models to advise policymakers. Several studies [ 13 , 14 , 15 , 16 ] sought to understand how the reduction in mobility affects the spread of COVID-19 cases across different POIs. In [ 13 ], researchers used Google mobility data and measured a correlation with the effective reproduction rate .…”

“…That study reveals that staying at home is effective at reducing , time spent at parks has little effect, while reducing mobility in other POIs has larger positive effects. In [ 15 , 16 ], researchers showed that mobility reduction of up to 40% in transit stations and retail and recreation venues decreased the number of cases and appeared to effectively “flatten the curve”. Furthermore, Refs.…”