7,747 Matching Annotations
  1. Jul 2020
    1. We study optimal dynamic lockdowns against Covid-19 within a commuting network. Our framework integrates canonical spatial epidemiology and trade models, and is applied to cities with varying initial viral spread: Seoul, Daegu and NYC-Metro. Spatial lockdowns achieve substantially smaller income losses than uniform lockdowns, and are not easily approximated by simple centrality-based rules. In NYM and Daegu—with large initial shocks—the optimal lockdown restricts inflows to central districts before gradual relaxation, while in Seoul it imposes low temporal but large spatial variation. Actual commuting responses were too weak in central locations in Daegu and NYM, and too strong across Seoul.
    1. As the COVID-19 outbreak progresses, increasing numbers of researchers are examining how an array of factors either hurt or help the spread of the disease. Unfortunately, the majority of available data, primarily confirmed cases of COVID-19, are widely known to be biased indicators of the spread of the disease. In this paper we present a retrospective Bayesian model that is much simpler than epidemiological models of disease progression but is still able to identify the effect of covariates on the historical infection rate. The model is validated by comparing our estimation of the count of infected to projections from expert surveys and extant disease forecasts. To apply the model, we show that as of April 10th, there are approximately 2 million infected people in the United States, and these people are increasingly concentrated in states with less wealth, better air quality, fewer smokers, fewer people under the age of 18, less public health funding and more cardiovascular deaths. On the other hand, the percentage of foreign born residents and the proportion of people who voted for President Trump in 2016 are not clear predictors of COVID-19 trends.