  This work aims to assess the risks of Covid-19 disease spread in diverse
daily-life situations (referred to as scenarios) involving crowds of maskless
pedestrians, mostly outdoors. More concretely, we develop a method to infer the
global number of new infections from patchyobservations of pedestrians. The
method relies on ad hoc spatially resolved models for disease transmissionvia
virus-laden respiratory droplets, which are fit to existing exposure studies
about Covid-19. The approach is applied to the detailed field data about
pedestrian trajectories and orientations that we acquired during the pandemic.
This allows us to rank the investigated scenarios by the infection risks that
they present; importantly, the obtained hierarchy of risks is conserved across
all our transmission models (except the most pessimistic ones): Street
caf{\'e}s present the largest average rate of new infections caused by an
attendant, followed by busy outdoor markets, and then metro and train stations,
whereas the risks incurred while walking on fairly busy streets (average
density around 0.1 person/m${}^2$) are comparatively quite low. While none of
our ad hoc models can claim accuracy, their converging predictions lend
credence to these findings.} In scenarios with a moving crowd, we find that
density is the main factor influencing the estimated infection rate. Finally,
our study explores the efficiency of street and venue redesigns in mitigating
the viral spread: While the benefits of enforcing one-way foot traffic in
(wide) walkways are unclear, changing the geometry of queues substantially
affects disease transmission risks.
