tetano
Editor, Senior Moderator
Nat Commun. 2019 Jan 11;10(1):147. doi: 10.1038/s41467-018-08082-0.
[h=1]Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches.[/h] Lu FS[SUP]1[/SUP], Hattab MW[SUP]2[/SUP], Clemente CL[SUP]3[/SUP], Biggerstaff M[SUP]4[/SUP], Santillana M[SUP]5,[/SUP][SUP]6[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] In 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.
PMID: 30635558 DOI: 10.1038/s41467-018-08082-0
[h=1]Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches.[/h] Lu FS[SUP]1[/SUP], Hattab MW[SUP]2[/SUP], Clemente CL[SUP]3[/SUP], Biggerstaff M[SUP]4[/SUP], Santillana M[SUP]5,[/SUP][SUP]6[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] In 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.
PMID: 30635558 DOI: 10.1038/s41467-018-08082-0