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Review
. 2014 Mar 19;9(3):e91989.
doi: 10.1371/journal.pone.0091989. eCollection 2014.

Disease prediction models and operational readiness

Courtney D Corley  1 , Laura L Pullum  2 , David M Hartley  3 , Corey Benedum  1 , Christine Noonan  1 , Peter M Rabinowitz  4 , Mary J Lancaster  1
Affiliations
Review

Disease prediction models and operational readiness

Courtney D Corley et al. PLoS One. .

Abstract

The objective of this manuscript is to present a systematic review of biosurveillance models that operate on select agents and can forecast the occurrence of a disease event. We define a disease event to be a biological event with focus on the One Health paradigm. These events are characterized by evidence of infection and or disease condition. We reviewed models that attempted to predict a disease event, not merely its transmission dynamics and we considered models involving pathogens of concern as determined by the US National Select Agent Registry (as of June 2011). We searched commercial and government databases and harvested Google search results for eligible models, using terms and phrases provided by public health analysts relating to biosurveillance, remote sensing, risk assessments, spatial epidemiology, and ecological niche modeling. After removal of duplications and extraneous material, a core collection of 6,524 items was established, and these publications along with their abstracts are presented in a semantic wiki at http://BioCat.pnnl.gov. As a result, we systematically reviewed 44 papers, and the results are presented in this analysis. We identified 44 models, classified as one or more of the following: event prediction (4), spatial (26), ecological niche (28), diagnostic or clinical (6), spread or response (9), and reviews (3). The model parameters (e.g., etiology, climatic, spatial, cultural) and data sources (e.g., remote sensing, non-governmental organizations, expert opinion, epidemiological) were recorded and reviewed. A component of this review is the identification of verification and validation (V&V) methods applied to each model, if any V&V method was reported. All models were classified as either having undergone Some Verification or Validation method, or No Verification or Validation. We close by outlining an initial set of operational readiness level guidelines for disease prediction models based upon established Technology Readiness Level definitions.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. The Percentage of Citations Placed in Each Variable Group by Transmission Mode (if a model contained variables from multiple groups, it was placed in each respective group).
See this image and copyright information in PMC

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Grants and funding

This study was supported through a contract to Pacific Northwest National Laboratory from the National Biosurveillance Integration Center, Office of Health Affairs, and the Science and Technology Directorate, Chemical and Biological Division, Threat Characterization and Attribution Branch, of the U.S. Department of Homeland Security (DHS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.