Johns Hopkins COVID-19 Scenario Modeling Pipeline

The goal of this proposal is to support the continued generation of real-time short and long-term forecasts for the COVID-19 epidemic in the US using the COVID-19 Scenario Modeling Pipeline developed by the Johns Hopkins Infectious Disease Dynamics group. This experienced team of infectious disease modelers has been developing a flexible open-source software package built on mathematical models of SARS-CoV-2 transmission and clinical progression since April 2020. The model combines spatially-resolved data on demographics, mobility, vaccine coverage, and variant prevalence, and is calibrated to past cases and deaths. The team produces regular projections of disease burden and healthcare utilization for agencies including the US CDC, the California and Maryland departments of public health, and multiple international governments. Projections are shared publicly and included in ensemble forecasts as a part of the COVID-19 Forecast Hub and COVID-19 Scenario Modeling Hub that they have helped develop. The high spatial and temporal resolution of the model combined with the need for repeated stochastic simulation for model inference and uncertainty analysis demand the use of multiple parallel high-memory large-processor cores for weekly computation. As new variants, heterogeneous and imperfect vaccination coverage, relaxations of control policies, and possible waning immunity fuel continual circulation of the virus and strain healthcare systems, there remains a huge demand for informed model predictions for COVID-19 through 2022. Support of this project by the COVID-19 High Performance Computing Consortium will allow our team to continue to provide policy makers with real-time short and long-term predictions for the COVID-19 epidemic, using the best epidemiological evidence to date, and taking into account uncertainty in model parameters, case reporting, intervention efficacy, and future behavioral and policy responses.