Optimal Travel Restrictions in Epidemics
30 Pages Posted: 10 Aug 2020
Date Written: August 1, 2020
Travel restrictions are often imposed to limit the spread of infectious diseases. As uniform restrictions can be inefficient and incur unnecessarily high costs, this paper examines the optimal design of restrictions that target specific travel routes. We show that the marginal effect of an origin - destination specific travel restriction on the total number of infections is the greatest if the origin is closely connected to areas where the epidemic is more serious, or infections in the destination can spill over to many other areas. We then propose a model with trade-offs between costs of infections and costs of travel restrictions, where decisions are made with or without coordination between local jurisdictions, and provide a computational feasible way to solve the optimization problem. We illustrate the model using the COVID-19 data in China. When travel restrictions target key routes, only around 5% of the possible routes need to be closed in order to have the same number of confirmed COVID-19 cases as actually observed by February 29, 2020. Uncoordinated travel restrictions ignore policy externalities and therefore are sub-optimal in comparison to coordinated restrictions. Our approach may be generalized to multiple countries to guide policies during epidemics ranging from ex ante route-specific travel restrictions to ex post health measures based on travel histories, and from the initial travel restrictions to the phased reopening.
Note: Funding: Chen was supported by the US PEPPER Center Scholar Award (P30AG021342) and NIH/NIA grants (R03AG048920; K01AG053408). Qiu and Shi received support from the 111 Project of China (Grant No.B18026). Shi was supported by the National Natural Science Foundation of China (Grant No.71803062) and the Ministry of Education of China (GrantNo.18YJC790138). Conflict of Interest: None.
Keywords: COVID-19, transmission, public health measures
JEL Classification: I18, R1, C21, C6
Suggested Citation: Suggested Citation