During an epidemic, some of the most critical questions for healthcare decision-makers are the hardest ones to answer: When will the epidemic peak, how many people will need treatment at once and how long will that peak level of demand for care last? Timely answers can help hospital administrators, community leaders and clinics decide how to deploy staff and other resources most effectively. Unfortunately, many epidemiological forecasting models tend to struggle with accurately predicting cases and hospitalizations around peaks.
During an epidemic, some of the most critical questions for healthcare decision-makers are the hardest ones to answer: When will the epidemic peak, how many people will need treatment at once and how long will that peak level of demand for care last? Timely answers can help hospital administrators, community leaders and clinics decide how to deploy staff and other resources most effectively. Unfortunately, many epidemiological forecasting models tend to struggle with accurately predicting cases and hospitalizations around peaks.
A new approach described in the journal Proceedings of the National Academy of Sciences and led by University of Texas at Austin researchers, builds a critical piece of epidemiological understanding into forecasting models to address these longstanding issues.
