Containment strategies after the first wave of COVID-19 using mobility data

In their response to the COVID outbreak, governments face the dilemma to
balance public health and economy. Mobility plays a central role in this dilemma
because the movement of people enables both the economic activity and spreading
of the virus. In our work, we quantify the trade-off between mobility and infection
spreading, and propose strategies for restricting mobility in such a way that the
restrictions are minimal while the spreading of infection can be effectively
prevented. Mobility is quantified using aggregated origin-destination data in the
Netherlands before the outbreak (normal economic activity), and the spreading of
infection is predicted using compartmental epidemic models that include
geography of contacts. Using Network Science methodology, we obtain effective
policies of mobility restrictions and compare their results in two scenarios: when
the infection has already spread throughout the country, and when many people are
infected in one location – a so-called superspreader event.