We present a dynamic model that produces day-to-day changes in key variables due to the COVID-19 contagion: currently infected people, accumulated infected people, recovered people, deaths, and infected people who require hospitalization. The model is calibrated to the COVID-19 outbreak in Spain so that it replicates the death toll and the phases on the daily deaths curve. Then, we study the effects of the isolation enforcement following the declaration of the State of Alarm (March 14th, 2020). The simulations indicate that both the timing and the intensity of the isolation enforcement are crucial for the COVID-19 spread. Since the infection curve was already very steep at the time of the State of Alarm declaration, a 4-day earlier intervention for social distancing would have reduced the number of COVID-19 infected people by 67%. The model also informs that the isolation enforcement does not delay the peak day of the epidemic but slows down its end. Finally, we find that when social distancing relaxes the evolution of the COVID-19 in Spain will be very sensitive to both the contagion probability (which it is expected to go down due to preventive actions) and the number of interpersonal encounters (which it is expected to go up due to the reopening of economic and social activities). We report a threshold level for the contagion pace to avoid a second COVID-19 outbreak in Spain.

Additional Metadata
Keywords COVID-19 pandemic, calibrated model simulations, isolation enforcement, policy intervention design
Publisher Carleton University
Series Carleton Economics Working Papers (CEWP)
Casares, Miguel, & Khan, H.U. (2020). A Dynamic Model of COVID-19: Contagion and Implications of Isolation Enforcement. Carleton Economics Working Papers (CEWP). Carleton University.