  We propose an SEIR-type meta-population model to simulate and monitor the
Covid-19 epidemic evolution. The basic model consists of seven compartments,
namely susceptible (S), exposed (E), three infective classes, recovered (R),
and deceased (D). We define these compartments for n age and gender groups in m
different spatial locations. So, the resulting model has, for each age group,
gender, and place, all epidemiological classes. The mixing between them is
accomplished by means of time-dependent infection rate matrices. The model is
calibrated with the curve of daily new infections in New York City and its
boroughs, including census data, and the proportions of infections,
hospitalizations, and deaths for each age range. We end up with a model that
matches the reported curves and predicts accurately infection information for
different places and age classes.
