Combining predictive models with future change scenarios can produce credible forecasts of COVID-19 futures
Authors: Newcomb Ken , Bilal Shakir , Michael Edwin 
Published: December 2021

The advent and distribution of vaccines against SARS-CoV-2 in late 2020 was thought to represent an effective means to control the ongoing COVID-19 pandemic. This optimistic expectation was dashed by the omicron waves that emerged over the winter of 2021/2020 even in countries that had managed to vaccinate a large fraction of their populations, raising questions about whether it is possible to use scientific knowledge along with predictive models to anticipate changes and design management measures for the pandemic. Here, we used an extended SEIR model for SARS-CoV-2 transmission sequentially calibrated to data on cases and interventions implemented in Florida until Sept. 24th2021, and coupled to scenarios of plausible changes in key drivers of viral transmission, to evaluate the capacity of such a tool for exploring the future of the pandemic in the state. We show that while the introduction of vaccinations could have led to the permanent, albeit drawn-out, ending of the pandemic if immunity acts over the long-term, additional futures marked by complicated repeat waves of infection become possible if this immunity wanes over time. We demonstrate that the most recent omicron wave could have been predicted by this hybrid system, but only if timely information on the timing of variant emergence and its epidemiological features were made available. Simulations for the introduction of a new variant exhibiting higher transmissibility than omicron indicated that while this will result in repeat waves, forecasted peaks are unlikely to reach that observed for the omicron wave owing to levels of immunity established over time in the population. These results highlight that while limitations of models calibrated to past data for precisely forecasting the futures of epidemics must be recognized, insightful predictions of pandemic futures are still possible if uncertainties about changes in key drivers are captured appropriately through plausible scenarios.

Author Summary

We present an extended SEIR model of the ongoing SARS-CoV-2 pandemic in the State of Florida coupled to scenarios of future changes in key transmission drivers as a means to explore the future paths that might be followed by the current pandemic. The model explicitly accounts for vaccination (including refusal within the population), waning of natural and vaccine-induced immunity, and varying levels of protection due to social measures. Model parameters are tuned to reproduce daily reported cases and deaths, and the updated model is used to forecast pandemic paths given plausible future scenarios, including the emergence of new variants and changing vaccination uptake and social behavior. While our models predict a rapid decay of cases if immunity acts in the long-term, the futures become more complex if immunity is not permanent. Even though new variants and loss of immunity may contribute to repeat waves of infection, the size of future waves is likely to be small, due to the high levels of immunity conferred by both the vaccines and natural infection. These results show that coupling predictive models with plausible scenarios of change in key drivers can provide an important tool for forecasting the future states of the pandemic.