Fundamental limitations on efficiently forecasting certain epidemic measures in network models.

ABSTRACT: The ongoing COVID-19 pandemic underscores the importance of developing reliable forecasts that would allow decision makers to devise appropriate response strategies. Despite much recent research on the topic, epidemic forecasting remains poorly understood. Researchers have attributed the difficulty of forecasting contagion dynamics to a multitude of factors, including complex behavioral responses, uncertainty in data, the stochastic nature of the underlying process, and the high sensitivity of the disease parameters to changes in the environment. We offer a rigorous explanation of the difficulty of short-term forecasting on networked populations using ideas from computational complexity. Specifically, we show that several forecasting problems (e.g., the probability that at least a given nu

SUBMITTER: Rosenkrantz DJ 

PROVIDER: S-EPMC8794801 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature