title: How many suffice? A computational framework for sizing sentinel surveillance networks.
abstract: BACKGROUND
  Data from surveillance networks help epidemiologists and public health officials detect emerging diseases, conduct outbreak investigations, manage epidemics, and better understand the mechanics of a particular disease. Surveillance networks are used to determine outbreak intensity (i.e., disease burden) and outbreak timing (i.e., the start, peak, and end of the epidemic), as well as outbreak location. Networks can be tuned to preferentially perform these tasks. Given that resources are limited, careful site selection can save costs while minimizing performance loss.
  
  
  METHODS
  We study three different site placement algorithms: two algorithms based on the maximal coverage model and one based on the K-median model. The maximal coverage model chooses sites that maximize the total number of people within a specified distance of a site. The K-median model minimizes the sum of the distances from each individual to the individual's nearest site. Using a ground truth dataset consisting of two million de-identified Medicaid billing records representing eight complete influenza seasons and an evaluation function based on the Huff spatial interaction model, we empirically compare networks against the existing Iowa Department of Public Health influenza-like illness network by simulating the spread of influenza across the state of Iowa.
  
  
  RESULTS
  We show that it is possible to design a network that achieves outbreak intensity performance identical to the status quo network using two fewer sites. We also show that if outbreak timing detection is of primary interest, it is actually possible to create a network that matches the existing network's performance using 59% fewer sites.
  
  
  CONCLUSIONS
  By simulating the spread of influenza across the state of Iowa, we show that our methods are capable of designing networks that perform better than the status quo in terms of both outbreak intensity and timing. Additionally, our results suggest that network size may only play a minimal role in outbreak timing detection. Finally, we show that it may be possible to reduce the size of a surveillance system without affecting the quality of surveillance information produced.
authors: Department of Computer Science, University of Iowa, Iowa City, Iowa, USA. geoffrey-fairchild@uiowa.edu.
authors: Geoffrey
authors: G
authors: Fairchild
authors: Philip M
authors: PM
authors: Polgreen
authors: Eric
authors: E
authors: Foster
authors: Gerard
authors: G
authors: Rushton
authors: Alberto M
authors: AM
authors: Segre
mesh_terms: D004196:Disease Outbreaks; D006801:Humans; D007251:Influenza, Human / Q000175:diagnosis / Q000453:epidemiology*; D020407:Internet; D011634:Public Health / Q000379:methods; D018571:Sentinel Surveillance; D014481:United States / Q000453:epidemiology