Collaborative Research: RAPID: Statistical Tools to Quantify and Mitigate the Spread of COVID-19

点击翻译按钮获取中文摘要

This project will develop rigorous statistical models and inference tools to better understand the spread of COVID-19 as well as the type of the interventions that are most likely to work to prevent further outbreak. The work will result in concrete approaches for quantifying uncertainty of possible outcomes and provide reliable tools to assist decision makers in designing mitigation strategies for the COVID-19 pandemic. In particular, a new statistical inference tool will be developed to estimate the key epidemic parameters that describe the nature of the COVID-19 spread in an interpretable manner. These parameters are critical for providing reliable forecasts through real-time simulation of potential outbreaks. The new models will analyze the effects of changing social contact structures on the spread of COVID-19, and account for missing or partial data in a systematic way. The results of this project will contribute to the scientific and mathematical approaches needed to provide effective and quick understanding of future epidemics or relevant phenomena.Existing epidemic models largely rely on deterministic systems that are unable to quantify the uncertainty critical for decision making. In addition, data analysis and predictive modeling for the COVID-19 pandemic require statistical tools for fitting epidemic models with only partially available data. However, most stochastic models face challenges in the face of missing data. To address this gap, the project team will develop new stochastic compartmental models that extend classical approaches to explicitly account for changes to the social contact network underlying the disease spread. These new developments will be accompanied by likelihood-based methods to infer key epidemic parameters that offer mechanistic interpretations with uncertainty quantification. In particular, missing data such as infection times at the individual level will be accounted for via novel data augmentation techniques. The results of this project will provide guidance both to the scientific community on how to evaluate the COVID-19 pandemic, and to the general public on which mitigation strategies would be most effective.This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplemental funds allocated to MPS.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.