Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches

• DOI: 10.1038/s41467-018-08082-0
• Publication Date: 2019-01-08T07:21:00Z
• Authors: Fred S. Lu; Mohammad W. Hattab; Cesar Leonardo Cl...; Matthew Biggerstaff; Mauricio Santillana
• Keywords: Data Analysis; Internet; 0303 health sciences; Databases, Factual; Science; Q; Article; United States; 3. Good health; Search Engine; 03 medical and health sciences; Epidemiological Monitoring; Influenza, Human; Electronic Health Records; Humans

AbstractIn the presence of health threats, precision public health approaches aim to provide targeted, timely, and population-specific interventions. Accurate surveillance methodologies that can estimate infectious disease activity ahead of official healthcare-based reports, at relevant spatial resolutions, are important for achieving this goal. Here we introduce a methodological framework which dynamically combines two distinct influenza tracking techniques, using an ensemble machine learning approach, to achieve improved state-level influenza activity estimates in the United States. The two predictive techniques behind the ensemble utilize (1) a self-correcting statistical method combining influenza-related Google search frequencies, information from electronic health records, and historical flu trends within each state, and (2) a network-based approach leveraging spatio-temporal synchronicities observed in historical influenza activity across states. The ensemble considerably outperforms each component method in addition to previously proposed state-specific methods for influenza tracking, with higher correlations and lower prediction errors.