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.

[Nature Communications, 20 November 2023]

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.