Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States
Roland Pongou, Guy Tchuente, Jean-Baptiste Tondji

TL;DR
This paper models optimal intervention strategies during a pandemic using network theory, calibrates it with U.S. nursing home data, and finds that more tolerant policies increase deaths but boost economic growth, with political factors influencing policy choices.
Contribution
It introduces a novel economic network model for pandemic intervention targeting, calibrated with real U.S. nursing home data, to analyze policy tradeoffs and network effects.
Findings
Laissez-faire policies increase nursing home deaths and GDP growth.
Higher network centrality correlates with increased lockdown probability.
States with Republican governors tolerate higher infection levels but have higher death counts.
Abstract
This study develops an economic model for a social planner who prioritizes health over short-term wealth accumulation during a pandemic. Agents are connected through a weighted undirected network of contacts, and the planner's objective is to determine the policy that contains the spread of infection below a tolerable incidence level, and that maximizes the present discounted value of real income, in that order of priority. The optimal unique policy depends both on the configuration of the contact network and the tolerable infection incidence. Comparative statics analyses are conducted: (i) they reveal the tradeoff between the economic cost of the pandemic and the infection incidence allowed; and (ii) they suggest a correlation between different measures of network centrality and individual lockdown probability with the correlation increasing with the tolerable infection incidence…
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Taxonomy
TopicsHealth disparities and outcomes · COVID-19 epidemiological studies · Urban, Neighborhood, and Segregation Studies
