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.
Highlights
A rich and diverse field of infectious disease modeling has emerged in the past 60 years and has advanced our understanding of population- and individual-level disease transmission dynamics, including risk factors, virulence, and spatio-temporal patterns of disease spread [1,2,3,4]
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
Verification and Validation Methods The V&V methods applied to each model, if any, were analyzed; see Table 6
Summary
A rich and diverse field of infectious disease modeling has emerged in the past 60 years and has advanced our understanding of population- and individual-level disease transmission dynamics, including risk factors, virulence, and spatio-temporal patterns of disease spread [1,2,3,4]. These modeling techniques span domains from biostatistical methods to massive agent-based, biophysical, ordinary differential equation (ODE), to ecological-niche models [5,6,7,8]. There are strong indicators that dynamics and emergence of vector-borne diseases are in flux because of, among other factors, changes in land use, human behavior, and climate [20,21,22]
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