We address the problem of finding the optimal lockdown and reopening policy during a pandemic like COVID-19, for a social planner who prioritizes health over short-term wealth accumulation. Agents are connected through a fuzzy 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. We show theoretically that the planner's problem has a unique solution. The optimal policy depends both on the configuration of the contact network and the tolerated infection incidence. Using simulations, we apply these theoretical findings to: (i) quantify the tradeoff between the economic cost of the pandemic and the infection incidence allowed by the social planner, and show how this tradeoff depends on network configuration; (ii) understand the correlation between different measures of network centrality and individual lockdown probability, and derive implications for the optimal design of surveys on social distancing behavior and network structure; and (iii) analyze how segregation induces differential health and economic dynamics in minority and majority populations, also illustrating the crucial role of patient zero in these dynamics.
Pongou, Roland and Tchuente, Guy and Tondji, Jean-Baptiste, An Economic Model of Health-vs-Wealth Prioritization during COVID-19: Optimal Lockdown, Network Centrality, and Segregation (September 22, 2020). Available at SSRN: https://ssrn.com/abstract=3692890 or http://dx.doi.org/10.2139/ssrn.3692890