Background
Surveillance and uncertainty
Proposed approach
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Global Sensitivity and Uncertainty Analyses (GSUA) (Convertino et al. 2014a; (Convertino M, S L, M A, Morris S: Importance, Interaction, and Scale-dependence of Cholera Outbreak Drivers: Metamodeling Predictions, submitted); Saltelli et al. 2008) as a method for evaluating the systemic uncertainty in reported outbreaks for the determination of outbreak sources, importance and interaction of transmission network variables. GSUA fully attributes uncertainty to any input factor of the model via probability distributions, and such uncertainty is propagated to study how it affects the uncertainty of the output that is the effective distance from observed outbreak sources (Brockmann and Helbing 2013).×
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Value of Information (VoI) portfolio model (Convertino and Valverde 2013; Convertino et al. 2014c, 2014d; Trainor-Guitton et al. 2012) with Pareto optimization for the design of optimal surveillance networks by selecting observers in network topologies with the highest information for detecting outbreak sources.
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Network based (Brockmann and Helbing 2013; Convertino and Hedberg 2014a; Newman 2003) versus random node surveillance design, and effective distance based prediction of outbreak spread with source detection. The effective distance implementation (Brockmann and Helbing 2013) does require only the knowledge of outbreak occurrence versus the number of cases.
Materials
Case-study outbreaks
Mobility and surveillance networks
Methods
Effective distance and traceback model
Epidemiological dynamics and information spreading: predictive metacommunity model
Outbreak detection: linking patterns and processes
Value of information and Pareto optimization
Global sensitivity and uncertainty analyses
Results and discussion
Observers
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Outbreak (Network)
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Random
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High degree
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maxVoI
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Cholera Cameroon 2010 (Tree) | 20% | 5% | 3% |
Salmonella USA 2012 (Tree) | 37% | 13% | 5% |
H5N1 2007 (Graph) | 46% | 38% | 20% |
Conclusions
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The structural resilience of the system is independent of epidemic dynamics and can be identified by the Integrated Network Resilience that combines two key network topological factors. Because of the spatio-temporal anisotropy of outbreaks the small-world network is the surveillance network with the highest value of information, that implies the most accurate outbreak source detection and outbreak pattern prediction. Structural resilience is just one component of the system to respond to outbreaks. A portion of system resilience has to be built considering training of personnel dedicated to surveillance.
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The knowledge of mobility networks and the value of information of surveillance networks as subsets of of the former is determinant for early detection and response of outbreaks. The use of effective distances allow to avoid the need to use topological features - necessary in the design process - that increase the computational complexity and uncertainty related to the estimation of the most likely transmission networks.
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Optimal dynamic design and real time surveillance are tasks that can be achieved using the proposed model. The design and outbreak source detection can be performed with a portfolio model with Pareto optimization that selects the most important observer networks by maximizing the VoI, and detects the most likely outbreak sources by minimizing the uncertainty in the effective distance from the real sources. The optimal design implies accuracy detection but not vice versa. Dynamic surveillance network in which surveillance change configurations, for instance taking advantage of smart sensors placed along the mobility network, are at the frontier of technological development and can implement the maxVoI model.