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: $$ the lockdown was somehow late, but further delay would have had more extreme consequences; $$ a targeted lockdown of a small number of highly connected geographic regions would have been equally effective, arguably with significantly lower economic costs; $$ targeted lockdowns based on threshold number of cases are not effective, since they fail to account for network externalities.