Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States

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 level. Using unique data on the networks of nursing and long-term homes in the U.S., we calibrate our model at the state level and estimate the tolerable COVID-19 infection incidence level. We find that laissez-faire (more tolerance to the virus spread) pandemic policy is associated with an increased number of deaths in nursing homes and higher state GDP growth. In terms of the death count, laissez-faire is more harmful to nursing homes than more peripheral in the networks, those located in deprived counties, and those who work for a profit. We also find that U.S. states with a Republican governor have a higher level of tolerable incidence, but policies tend to converge with high death count.