A major output of the projection hub would be ensemble estimates of epidemic outcomes (e.g., cases, hospitalization and/or deaths), for different time points, intervention scenarios, and US jurisdictions. We only require that participating teams share point estimates and uncertainty bounds, along with a short model description and answers to a list of key questions about design. The COVID-19 Scenario Modeling Hub is be open to any team willing to provide projections at the right temporal and spatial scales, with minimal gatekeeping. Scenarios have been designed in consultation with academic modeling teams and government agencies (e.g., CDC). We have specified a set of scenarios and target outcomes to allow alignment of model projections for collective insights. We plan to fill this gap by building a public COVID-19 Scenario Hub to harmonize scenario projections in the United States. There is a need for generating long-term COVID-19 projections combining insights from different models and making them available to decision-makers, public health experts, and the general public. Single model projections are particularly problematic for emerging infections where there is much uncertainty about basic epidemiological parameters (such as the waning of immunity), the transmission process, future policies, the impact of interventions, and how the population may react to the outbreak and associated interventions. Some single models are available online (e.g., IHME, or Imperial College), but a decade of infectious disease forecasts has demonstrated that projections from a single model are particularly risky. The COVID-19 Forecasting Hub provides useful and accurate short-term forecasts, but there remains a lack of publicly available model projections at 3-6 month time scale.
Further, a comparison of the impact of interventions across 17 models has illustrated how any individual model can grossly underestimate uncertainty, while ensemble projections can offer robust projections of COVID-19 the course of the epidemic under different scenarios at a 6-month time scale. In the COVID-19 pandemic this approach has been exemplified by the COVID-19 Forecast Hub, which combines the results of over 30 models (see a report on the first wave of the pandemic). The need for long-term epidemic projections is particularly acute in a severe pandemic, such as COVID-19, that has a large impact on the economy for instance, economic and budget projections require estimates of outbreak trajectories in the 3-6 month time scale.įrom weather to infectious diseases, it has been shown that synergizing results from multiple models gives more reliable projections than any one model alone. As such, long-term projections can guide longer-term decision-making while short-term forecasts are more useful for situational awareness and guiding immediate response. The goal of long-term projections is to compare outbreak trajectories under different scenarios, as opposed to offering a specific, unconditional estimate of what “will” happen. However, policy decisions around the course of emerging infections often require projections in the time frame of months.
#NETLOGO ROUND DRIVERS#
Round 14: Scenario Descriptions and Model DetailsĮven 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. Last updated: 7-22-2022 for Round 15 Scenarios.
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