Scenario Modeling Hub


Even the best models of emerging infections struggle to give accurate forecasts at time scales greater than 3-4 weeks due to unpredictable drivers such as a changing policy environment, behavior change, the development of new control measures, and stochastic events. However, policy decisions and planning in reaction to infectious diseases often require projections on the time frame of months. The goal of long-term scenario projections is to compare outbreak trajectories under different assumptions governing key features of interventions, pathogens, and populations that drive disease dynamics. This is in contrast to forecasts, which offer unconditional estimates of what “will” happen.

Pathogen-Specific Projection

The Scenario Modeling Hub (SMH) pathogen-specific projections provide real-time modeling evidence aiming to support ongoing public health needs. SMH currently produces projections for COVID-19, seasonal influenza, and Respiratory Syncytial Virus (RSV), each addressing different public health questions and uncertainties. For details, please visit the pathogen-specific websites, linked below.

COVID-19 Scenario Modeling Hub

The COVID-19 Scenario Modeling Hub aims to examine the impact of changes in behavior and control, new variants, and vaccination over a 3-month to 2-year time period, depending on the round.

Flu Scenario Modeling Hub

The Flu Scenario Modeling Hub aims to anticipate the impact of changes in vaccination coverage and effectiveness, prior population immunity, and dominant subtypes over the course of each influenza season.

RSV Scenario Modeling Hub

The RSV Scenario Modeling Hub aims to project the impacts of new vaccines and monoclonal antibodies over the course of each RSV season.

Research Rounds

The ultimate goal of SMH Research Rounds is to improve our ability to predict and respond to ongoing and emerging infectious disease threats going forward. The rounds focus on augmenting modeling expertise to handle important uncertainties that define infectious disease dynamics, such as the role of structural inequities in driving disease disparities along sociodemographic lines. These rounds feature both retrospective and prospective modeling challenges that take non-standard approaches to address key gaps in the field of infectious disease modeling.

COVID-19 Research Scenario Modeling Hub

The COVID-19 Research Scenario Modeling Hub intended to encourage modeling to address specific COVID-19 research questions. Specific focus areas in the pipeline include revisiting whether we could have projected the disparities observed during early stages of the pandemic and whether better modeling may have been able to inform action to reduce these disparities. Additional research topics may follow.