Abstract
Seasonal influenza causes millions of illnesses and tens of thousands of deaths per year in the USA alone. While the morbidity and mortality associated with influenza is substantial each year, the timing and magnitude of epidemics are highly variable which complicates efforts to anticipate demands on the healthcare system. Better methods to forecast influenza activity would help policymakers anticipate such stressors. The US Centers for Disease Control and Prevention (CDC) has recognized the importance of improving influenza forecasting and hosts an annual challenge for predicting influenza-like illness (ILI) activity in the USA. The CDC data serve as the reference for ILI in the USA, but this information is aggregated by epidemiological week and reported after a one-week delay (and may be subject to correction even after this reporting lag). Therefore, there has been substantial interest in whether real-time Internet search data, such as Google, Twitter or Wikipedia could be used to improve influenza forecasting. In this study, we combine a previously developed calibration and prediction framework with an established humidity-based transmission dynamic model to forecast influenza. We then compare predictions based on only CDC ILI data with predictions that leverage the earlier availability and finer temporal resolution of Wikipedia search data. We find that both the earlier availability and the finer temporal resolution are important for increasing forecasting performance. Using daily Wikipedia search data leads to a marked improvement in prediction performance compared to weekly data especially for a three- to four-week forecasting horizon.
Highlights
Seasonal influenza remains an important infectious cause of morbidity and mortality [1,2]
We assume that Centers for Disease Control and Prevention (CDC) influenza-like illness (ILI) data serves as a gold standard for influenza activity and we evaluate all predictions relative to the CDC ILI data
While this oneweek lag in reporting of CDC data accurately reflects the reporting delay, we note that the CDC ILI data are sometimes revised at a later date [14]
Summary
Seasonal influenza remains an important infectious cause of morbidity and mortality [1,2]. The US Centers for Disease Control and Prevention (CDC) recognize the importance of improving methods for assessing short-term predictions of influenza activity by hosting an annual influenza forecasting challenge [4]. Several data sources have been used to monitor the influenza activity in the USA and as calibration data for forecasting models. Data include both direct measures of influenza such as the official CDC influenza-like illness (ILI) data [5] and indirect measures such as Internet search queries. The rapid availability, low cost of acquisition and potential to access Internet search query data at relatively high spatial weekly %ILI resolution make these data sources attractive options for many teams participating in the annual CDC’s influenza prediction challenge [17]
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