The distribution of <i>R</i>₀ values found by calibration (Red lines represent the range in the literature).

<p>The distribution of <i>R</i>₀ values found by calibration (Red lines represent the range in the literature).</p

Spearman correlation coefficients during AUTUMN outbreak.

<p>Spearman correlation coefficients during AUTUMN outbreak.</p

GAM predictions—Density vs. Deviation from the mean Peak Load (Proportion of Infectious) on the left and mean Peak Load (Actual) on the right.

<p>GAM predictions—Density vs. Deviation from the mean Peak Load (Proportion of Infectious) on the left and mean Peak Load (Actual) on the right.</p

Sample convergence of simulation iterations to POC-Data.

<p>Sample convergence of simulation iterations to POC-Data.</p

Trajectory of proportion of infectious—Simulation vs. Knox-Data.

<p>Trajectory of proportion of infectious—Simulation vs. Knox-Data.</p

Comparisons of calibrated <i>pt</i> values against distinct datasets.

<p>Comparisons of calibrated <i>pt</i> values against distinct datasets.</p

The distribution of diffusion speed (on the left) and peak load (on the right) for all zip-codes.

<p>The distribution of diffusion speed (on the left) and peak load (on the right) for all zip-codes.</p

Disease Prediction Models and Operational Readiness

<div><p>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 <a href="http://BioCat.pnnl.gov" target="_blank">http://BioCat.pnnl.gov</a>. 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.</p></div

Grouping of Citations by Verification and Validation (V&V) Methods.

<p>If a model used multiple methods for its verification or validation, it was categorized in each respective group.</p

Citations categorized by model type.

<p>The categories are not mutually exclusive.</p><p>* The authors acknowledge others significant work in event-based biosurveillance, such as the G-7 Global Health Security Action Group <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091989#pone.0091989-Hartley3" target="_blank">[110]</a>, which is not cited in this table because of the selection criteria.</p