Optimizing Provider Recruitment for Influenza Surveillance Networks




Scarpino, Samuel V.
Dimitrov, Nedialko B.
Meyers, Lauren Ancel

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Public Library of Science


The increasingly complex and rapid transmission dynamics of many infectious diseases necessitates the use of new, more advanced methods for surveillance, early detection, and decision-making. Here, we demonstrate that a new method for optimizing surveillance networks can improve the quality of epidemiological information produced by typical provider-based networks. Using past surveillance and Internet search data, it determines the precise locations where providers should be enrolled. When applied to redesigning the provider-based, influenza-like-illness surveillance network (ILINet) for the state of Texas, the method identifies networks that are expected to significantly outperform the existing network with far fewer providers. This optimized network avoids informational redundancies and is thereby more effective than networks designed by conventional methods and a recently published algorithm based on maximizing population coverage. We show further that Google Flu Trends data, when incorporated into a network as a virtual provider, can enhance but not replace traditional surveillance methods.


Samuel V. Scarpino is with UT Austin, Nedialko B. Dimitrov is with the Naval Postgraduate School, Lauren Ancel Meyers is with UT Austin and the Santa Fe Institute.

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Scarpino SV, Dimitrov NB, Meyers LA (2012) Optimizing Provider Recruitment for Influenza Surveillance Networks. PLoS Comput Biol 8(4): e1002472. doi:10.1371/journal.pcbi.1002472