Decision-makers and the general public rely on models to simulate the SARS-CoV-2 virus spread and predict the number of infections and fatalities. Model predictions are critical to rapidly develop policy interventions that mitigate COVID-19, to anticipate impacts on health care resources, and to strategize how best to impose and lift public health guidelines. Existing COVID-19 models produce radically different predictions, thus creating confusion and mistrust over their use. Therefore, there is an urgent need to compare between the wide-range of existing COVID-19 models and their predictions. The goal of this project is to find a consensus among various model predictions and to make the different model assumptions and uncertainty transparent. An interactive web-based dashboard will serve as an open and accessible tool to inform the public, fellow researchers, and decision-makers where existing models agree and disagree on predictions. The predictions that are agreed upon by existing models can then be used with greater confidence and trust as the basis for effective decision-making to save lives and resources. Apart from the practical importance for the implementation of effective pandemic control measures and public health strategies, other broader impacts are professional development opportunities for early career researchers and training opportunities for a post-doctoral scholar.
To find a consensus among various model predictions, this project will develop a novel ensemble prediction approach that (1) aligns different COVID-19 simulation models and (2) uses a time series clustering technique to unify model predictions. In the model alignment stage, a range of open-source and publicly available COVID-19 simulation models will be selected, aligned based on their parameters, and run as needed. A broad set of predictions will be obtained from the model results, where each prediction represents a possible world with a corresponding number of new cases, fatalities, and other quantities of interest. The time series clustering stage will project possible worlds into a feature space and apply clustering algorithms to find similar possible worlds. For each cluster, a representative possible world will be selected, enriched with measures of uncertainty, and visualized using the dashboard. This project advances the theoretical knowledge base for model alignment approaches and representative uncertain clustering for simulation model predictions. The unification of model predictions into a scientific consensus can be used to inform decision-makers better so that they can develop life-saving interventions.
This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
