Abstract
Influenza is a common respiratory infection that causes considerable morbidity and mortality worldwide each year. In recent years, along with the improvement in computational resources, there have been a number of important developments in the science of influenza surveillance and forecasting. Influenza surveillance systems have been improved by synthesizing multiple sources of information. Influenza forecasting has developed into an active field, with annual challenges in the United States that have stimulated improved methodologies. Work continues on the optimal approaches to assimilating surveillance data and information on relevant driving factors to improve estimates of the current situation (nowcasting) and to forecast future dynamics.
Highlights
Seasonal influenza epidemics mainly caused by influenza A and B viruses result in ∼3–5 million cases of severe illness and 290,000–650,000 deaths worldwide each year [87]
Data on influenza surveillance and knowledge on influenza transmission dynamics can be used to construct models for predicting future influenza activity or for creating counterfactual scenarios about what might have happened in alternative circumstances, for example, with or without the implementation of particular public health measures
A projection is a realization or comparison of what would happen under certain assumptions and hypotheses, whereas a forecast is a quantitative estimate of what will happen in the future
Summary
Seasonal influenza epidemics mainly caused by influenza A and B viruses result in ∼3–5 million cases of severe illness and 290,000–650,000 deaths worldwide each year [87]. Improvements in computational skill allow advanced modeling techniques for prediction and forecasting by incorporating multiple streams of data and complex underlying dynamics. These improvements present new opportunities for understanding the current situation, referred to as nowcasting, and for forecasting what might happen in future weeks, for example, determining when the influenza season will reach a peak. We attempt to address, at each step, the potential uses, challenges, and possible research gaps that need to be pursued in future courses of tracking, predicting, and forecasting influenza virus circulation in the community
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