JMIR Publications

ABSTRACT

Background:

While dynamic models are increasingly used by decision makers as a source of insight to guide interventions to control communicable disease outbreaks, such models have long suffered from a risk of rapid obsolescence due to a failure to keep updated with emerging epidemiological evidence. The application of statistical filtering algorithms to high-velocity data streams has recently demonstrated effectiveness in allowing such models to be automatically regrounded by each new incoming observation. The attractiveness of such techniques has been enhanced by the emergence of a new generation of geospatially specific, high-velocity data sources, including daily counts of relevant searches and social media posts. The information available in such electronic data sources complements that of traditional epidemiological data sources.

Objective:

This paper seeks to evaluate the degree to which the predictive accuracy of pandemic projection models regrounded via machine learning in daily clinical data can be enhanced by extending such methods leverage daily search counts.

Methods:

We combined a previously published influenza A (H1N1) pandemic projection model with the sequential Monte Carlo technique of particle filtering so as to reground the model on a daily basis using confirmed incident case counts and search volumes. The effectiveness of particle filtering was evaluated using a norm discrepancy metric via predictive- and dataset specific- cross-validation.

Results:

Results suggested that despite the data quality limitations of high-velocity search volume data, the predictive accuracy of dynamic models can be strongly elevated by inclusion of such data in filtering methods.

Conclusions:

The predictive accuracy of dynamic models can be notably enhanced by tapping a readily accessible, publically available high-velocity data source. This work highlights a low-cost, low-burden avenue for strengthening model-based outbreak intervention response planning using low-cost public electronic datasets.