  Since the beginning of the epidemic, daily reports of CoViD-19 cases,
hospitalizations, and deaths from around the world have been publicly
available. This paper describes methods to characterize broad features of the
spread of the disease, with relatively long periods of constant transmission
rates, using a new population modeling framework based on discrete-time
difference equations. Comparative parameters are chosen for their weak
dependence on model assumptions. Approaches for their point and interval
estimation, accounting for additional sources of variance in the case data, are
presented. These methods provide a basis to quantitatively assess the impact of
changes to social distancing policies using publicly available data. As
examples, data from Ontario and German states are analyzed using this
framework. German case data show a small increase in transmission rates
following the relaxation of lock-down rules on May 6, 2020. By combining case
and death data from Germany, the mean and standard deviation of the time from
infection to death are estimated.
