Interacting regional policies in containing a disease

The spread of COVID-19 in London: Network effects and optimal lockdowns

We generalise a stochastic version of the workhorse SIR (Susceptible-Infectious-Removed) epidemiological model to account for spatial dynamics generated by network interactions. Using the London metropolitan area as a salient case study, we show that commuter network externalities account for about 42% of the propagation of COVID-19. We find that the UK lockdown measure reduced total propagation by 44%, with more than one third of the effect coming from the reduction in network externalities. Counterfactual analyses suggest that: (i) the lockdown was somehow late, but further delay would have had more extreme consequences; (ii) a targeted lockdown of a small number of highly connected geographic regions would have been equally effective, arguably with significantly lower economic costs; (iii) targeted lockdowns based on threshold number of cases are not effective, since they fail to account for network externalities.

JUE Insight: Urban flight seeded the COVID-19 pandemic across the United States

We document large-scale urban flight in the United States during the COVID-19 pandemic. Regions that saw migrant influx experienced greater subsequent new COVID-19 cases, linking urban flight (as a disease vector) and coronavirus spread in destination areas. Urban residents fled to socially connected areas, consistent with the theory that individuals sheltered with friends and family, or in second homes. Populations that fled were disproportionately younger, whiter, and wealthier. The association between migration and subsequent new cases persists when instrumenting for migration with social networks.

Integrated graph measures reveal survival likelihood for buildings in wildfire events

Wildfire events have resulted in unprecedented social and economic losses worldwide in the last few years. Most studies on reducing wildfire risk to communities focused on modeling wildfire behavior in the wildland to aid in developing fuel reduction and fire suppression strategies. However, minimizing losses in communities and managing risk requires a holistic approach to understanding wildfire behavior that fully integrates the wildland’s characteristics and the built environment’s features. This complete integration is particularly critical for intermixed communities where the wildland and the built environment coalesce. Community-level wildfire behavior that captures the interaction between the wildland and the built environment, which is necessary for predicting structural damage, has not received sufficient attention. Predicting damage to the built environment is essential in understanding and developing fire mitigation strategies to make communities more resilient to wildfire events. In this study, we use integrated concepts from graph theory to establish a relative vulnerability metric capable of quantifying the survival likelihood of individual buildings within a wildfire-affected region. We test the framework by emulating the damage observed in the historic 2018 Camp Fire and the 2020 Glass Fire. We propose two formulations based on graph centralities to evaluate the vulnerability of buildings relative to each other. We then utilize the relative vulnerability values to determine the damage state of individual buildings. Based on a one-to-one comparison of the calculated and observed damages, the maximum predicted building survival accuracy for the two formulations ranged from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$58 - 64 \%$$\end{document}58-64% for the historical wildfires tested. From the results, we observe that the modified random walk formulation can better identify nodes that lie at the extremes on the vulnerability scale. In contrast, the modified degree formulation provides better predictions for nodes with mid-range vulnerability values.

Interdependent program evaluation: Geographic and social spillovers in COVID-19 closures and reopenings in the United States

In an interconnected world, understanding policy spillovers is essential. We propose a program evaluation framework to measure policy spillover effects and apply that framework to study the governmental responses to COVID-19 in the United States. Our analysis suggests the presence of social spillovers. We estimate that while state closures directly reduced mobility by 3 to 4%, all other states locking down further decreased mobility in the focal state by 8 to 14%. Similarly, while reopening directly increased mobility by 2 to 3%, all other states' reopening increased mobility in the focal state by 12 to 21%. Our analysis also suggests geographic spillovers: Travel from locked down origins to open destinations increased by 12 to 29%. In contrast, travel from reopened origins to locked down destinations decreased by 6 to 7% for nearby counties and by 14 to 18% for distant counties. Despite its limitations, we believe that our approach takes the first steps toward creating a framework for interdependent program evaluation across policy domains.