  We study and predict the evolution of Covid-19 in six US states from the
period May 1 through August 31 using a discrete compartment-based model and
prescribe active intervention policies, like lockdowns, on the basis of
minimizing a loss function, within the broad framework of partially observed
Markov decision processes. For each state, Covid-19 data for 40 days (starting
from May 1 for two northern states and June 1 for four southern states) are
analyzed to estimate the transition probabilities between compartments and
other parameters associated with the evolution of the epidemic. These
quantities are then used to predict the course of the epidemic in the given
state for the next 50 days (test period) under various policy allocations,
leading to different values of the loss function over the training horizon. The
optimal policy allocation is the one corresponding to the smallest loss. Our
analysis shows that none of the six states need lockdowns over the test period,
though the no lockdown prescription is to be interpreted with caution:
responsible mask use and social distancing of course need to be continued. The
caveats involved in modeling epidemic propagation of this sort are discussed at
length. A sketch of a non-Markovian formulation of Covid-19 propagation (and
more general epidemic propagation) is presented as an attractive avenue for
future research in this area.
