How many suffice? A computational framework for sizing sentinel surveillance networks

International Journal of Health Geographics

Alberto M Segre¹  Gerard Rushton²  Eric Foster³  Philip M Polgreen⁴  Geoffrey Fairchild¹ 

[1]Department of Computer Science, University of Iowa, Iowa City, Iowa, USA

[2]Department of Geography, University of Iowa, Iowa City, Iowa, USA

[3]Department of Biostatistics, University of Iowa, Iowa City, Iowa, USA

[4]Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USA

关键词: Simulation;    Medicaid;    Harmony search;    Huff model;    K-median model;    Maximal coverage model;    Disease surveillance;    Outbreak timing;    Outbreak intensity;    Influenza;   

Others  :  809854
DOI  :  10.1186/1476-072X-12-56

 received in 2013-10-02, accepted in 2013-12-06,  发布年份 2013

【 摘 要 】

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.

【 授权许可】

   
2013 Fairchild et al.; licensee BioMed Central Ltd.

【 图 表 】

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