The pandemic of SARS-CoV-2 in late 2019 and 2020 is a global pandemic with major devastating effects on the health of millions of people in the entire world, and unprecedented economic disturbances of most countries on the globe. Massive investments are currently made to fight this pandemic, mainly by a strategy of individual containment, which strives to isolate infected individuals from the non-infected part of the population. We study an algorithmic approach on the social distancing strategy, which identify, for any given moment of the pandemic, key people in large populations who if stopped from spreading the virus leave only separate small groups for the virus to spread in. The developed algorithms will enable decision makers to efficiently distribute sparse resources (masks, test kits) or optimize which businesses to close. Such an approach must also incorporate the dynamic situation that the SARS-CoV-2 pandemic exhibits.