 Research
 Open Access
Optimal surveillance network design: a value of information model
 Matteo Convertino^{1, 2, 3, 4}Email author,
 Yang Liu^{1} and
 Haejin Hwang^{1}
https://doi.org/10.1186/s4029401400068
© Convertino et al.; licensee Springer. 2014
 Received: 3 July 2014
 Accepted: 23 October 2014
 Published: 27 November 2014
The Erratum to this article has been published in Complex Adaptive Systems Modeling 2016 4:9
Abstract
Purpose
Infectious diseases are the second leading cause of deaths worldwide, accounting for 15 million deaths – that is more than 25% of all deaths – each year. Food plays a crucial role, contributing to 1.5 million deaths, most of which are children, through foodborne diarrheal disease alone. Thus, the ability to timely detect outbreak pathways via highefficiency surveillance system is essential to the physical and social well being of populations. For this purpose, a traceability model inspired by wavepattern recognition models to detect “zeropatient” areas based on outbreak spread is proposed.
Methods
Model effectiveness is assessed for data from the 2010 Cholera epidemic in Cameroon, the 2012 foodborne Salmonella epidemic in USA, and the 20042007 H5N1 avian influenza pandemic. Previous models are complemented by the introduction of an optimal selection algorithm of surveillance networks based on the Value of Information (VoI) of reporting nodes that are subnetworks of mobility networks in which people, food, and species move. The surveillance network is considered the response variable to be determined in maximizing the accuracy of outbreak source detections while minimizing detection error. Surveillance network topologies are selected by considering their integrated network resilience expressing the rewiring probability that is related to the ability to report outbreak information even in case of network destruction or missing information.
Results
Independently of the outbreak epidemiology, the maximization of the VoI leads to a minimum increase in accuracy of 40% compared to the random surveillance model. Such accuracy is accompanied by an average reduction of 25% in required surveillance nodes with respect to random surveillance. Accuracy in systems diagnosis increases when system syndromic signs are the most informative in a way they reveal linkages between outbreak patterns and network transmission processes.
Conclusions
The model developed is extremely useful for the optimization of surveillance networks to drastically reduce the burden of foodborne and other infectious diseases. The model can be the framework of a cybertechnology that governments and industries can utilize in a realtime manner to avoid catastrophic and costly health and economic outcomes. Further applications are envisioned for chronic diseases, socially communicable diseases, biodefense and other detection related problems at different scales.
Keywords
 Value of information
 Surveillance network
 Transmission network
 Network resilience
 Topology
 Uncertainty
 Biosafety
 Biodefense
 Outbreak sources
Background
Surveillance and uncertainty
Increased movements of people, and expansion of international trade in food and other commodities, enhanced by social and environmental changes linked to urbanization, are all manifestations of the rapidlychanging nature of the world we live in. Such changes contribute to the rapid adaptation and movement of microorganisms, which has facilitated the return of old communicable diseases, the emergence of new ones, and the evolution of antimicrobial resistance. Food, is in many aspect the “connectome” of most issues related to infectious diseases and for this motivation experts are now talking about “emerging foodborne infectious diseases” whose complex dynamics embraces a larger spectrum of than the one traditionally considered for infectious diseases. The majority of the pathogens causing the significant foodborne disease burden  estimated as 1.9 million people annually at the global level  are now considered to be zoonotic (Schlundt et al. 2004). For this motivation a onehealth approach is now embraced (NAS 2012). To put it more simply, food is in many cases involved in many infectious disease propagations as the vehicle of pathogen spread in the transmission chain (e.g., environmentanimalfoodhumans) leading to infections (e.g. for cholera and tuberculosis) beyond the classical foodborne illnesses where there is no humanhuman transmission. As for foodborne diseases, in USA result in 37.2 million illnesses, 228,744 hospitalizations, and 2,612 deaths each year (Batz et al. 2011; Hanson et al. 2012; NAS 2012; Scallan et al. 2011a, 2011b). Foodborne illnesses can be caused by a variety of microbial pathogens, chemicals, and parasites that contaminate food at different points in the food production and preparation process. Although most of these diarrheal deaths occur in poor countries, foodborne diseases are neither limited to developing countries nor to children. In general, infectious diseases other than foodborne are a huge problem also for creating susceptibility to chronic diseases. Because of the necessity to decrease such burden, surveillance is the primary public health strategy to emphasize, but a set of questions arise spontaneously.
What is the optimal set of surveillance nodes that best inform about the spread of an epidemic accurately? Is it possible to gauge the shape of transmission networks based on epidemic outbreaks? The accuracy in such estimations is a function of the source of information reliability. This means that a node, or a set of nodes performing surveillance, can report or not report whether a case of an infectious disease case is observed. Such information tremendously affects the estimation of outbreak path and sources. Thus, the functioning of surveillance networks is crucial for early detection and rapid response of epidemics which is possibly the only way to respond to emerging/reemerging infectious diseases for which we are unprepared (Stachenko 2008). The function of surveillance networks is the ability to observe epidemic information (outbreak occurrence) moving along the mobility network. Therefore, the design of surveillance networks via the maximization of reporting accuracy (i.e., the accuracy in surveillance function) is a key process for saving lives of human and animal populations (Helbing et al. 2014). Economical and environmental outcomes are also associated to such optimal design: for instance industries and government costs related to commodity recalls and health care, and footprints on the environment related to pathogen and/or chemical spills can be highly reduced. To face such interconnected problems systemic risk approaches are welcome in order to design globally resilient systems (Haldane and May 2011; Helbing 2013; Helbing et al. 2014; Park et al. 2012).
The consideration of deep uncertainty in reported outbreaks is a very rarely addressed topic in epidemiology. Recently large attention has been placed on detecting outbreak sources (Convertino and Hedberg 2014a; Convertino and Hedberg 2014b; Pinto et al. 2012). However, in these studies uncertainty was neglected in reported outbreaks. Convertino and Hedberg (Convertino M, Hedberg C: Multisite outbreak detection: how many foodborne outbreak are attributable to farmerõs markets?, submitted) considered large variability in food trade network factors to capture the deep uncertainty related to attribution of foodborne outbreaks to different local food establishments (versus food imported globally), with particular focus on farmers’ markets. While this approach considered deep uncertainty, reported outbreaks were considered as “perfect information”. Other studies, such as (Pinto et al. 2012) considered random and nonrandom placement of surveillance observers which partially captures reporting uncertainty; nonetheless these studies did not formulate any optimization algorithm maximizing detection accuracy even in presence of uncertainty.
Moreover, outbreak uncertainty is certainly biased, thus (Pinto et al. 2012)’s and others’ approaches, based on regular schemes of surveillance underestimated such bias related to the anisotropic spreading of epidemics. In addition to such considerations, (Pinto et al. 2012)’s approach considered the problem of surveillance and outbreak source detection as coupled problems. In this study and in (Convertino and Hedberg 2014a) it is shown that such problem can be mathematically decoupled and the optimization of surveillance implies the optimal detection of outbreak sources but not vice versa. Pinto et al. (2012)’s model, analogous to the method used by telecommunication towers to pinpoint cell phone users, focused on arrival times of outbreaks  thought as information arrivals that are correctly reproduced by the correct definition of outbreak sources. Here, we propose a simpler model, based on the original idea of (Brockmann and Helbing 2013) and subsequently developed by (Convertino and Hedberg 2014a; Convertino and Hedberg 2014b; (Convertino M, Hedberg C: Multisite outbreak detection: how many foodborne outbreak are attributable to farmerõs markets?, submitted)) and (Manitz et al. 2014), that considers the minimization of the error in the distance from real outbreak multisources with the most informative surveillance networks. All explored surveillance networks correspond to different reported outbreak patterns. Other approaches for designing surveillance have been developed in the past; for instance (Bajardi et al. 2012) developed a model for robust outbreak cluster detection based on network features. While such model has an optimization component, deep uncertainty in outbreak reporting (that is uncertainty related to report cases at a selected site and time) is not considered and the detection of clusters do not detect outbreak sources. The optimal surveillance network maximizes outbreak information, or equivalently minimizes the uncertainty related to the average distance between real and predicted outbreak sources.
As a side note we want to emphasize that outbreak sources are oftentimes confused with “onset areas” which are areas where first cases occur. Those areas are not necessarily where the first contamination of the environment and/or human infection occur considering environmental and social determinants that contribute to the spread of the pathogen in space. However, considering the typical short range of dispersal of most pathogens (Mundt et al. 2009), onset areas tend to coincide with outbreak sources. In some cases one of the onsets is the outbreak source and all other onsets are generated by epidemic propagation. For other outbreaks in which vehicle or vector (e.g, wild birds, food, and people via airplanes) of pathogens travel for long distances the proposed model works well because of the large resolution at which outbreaks are investigated and because of the uniqueness of the outbreak source. We argue that the algorithm of (Pinto et al. 2012) is more likely to fail in detecting real outbreak sources because it does not consider uncertainty in reported outbreaks, thus the implemented matching function of the observed delay in reported information detects onset areas. Certainly, the definition of outbreak sources is always dependent on the spatial and temporal scale of the system considered and care has to be placed when planning control strategies based on supposed outbreak sources.
Proposed approach

Global Sensitivity and Uncertainty Analyses (GSUA) (Convertino et al. 2014a; (Convertino M, S L, M A, Morris S: Importance, Interaction, and Scaledependence 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).

Value of Information (VoI) portfolio model (Convertino and Valverde 2013; Convertino et al. 2014c, 2014d; TrainorGuitton 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.

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
Casestudy outbreaks
Mobility and surveillance networks
Mobility networks are the backbone of both transmission and surveillance networks that are both subnetworks of the mobility network. Mobility networks can be related to the mobility of vehicles, vectors, or pathogens themselves. For salmonella in tuna and H5N1 in humans we consider the food and human mobility network that are the primary networks responsible for the transmission of the pathogen. For cholera, previous studies such as (Pinto et al. 2012), have considered only the river network; however, considering the higher importance of human mobility in the transmission of cholera infections here we take into account human mobility and waterflow networks, simultaneously. However, only one network is enough for detecting outbreak sources. Surveillance networks can be though to coincide with human mobility networks rather than with environmental networks unless sensors are placed along those.
Human and food mobility network fluxes are estimated using the radiation model of (Simini et al. 2012) (Additional file 1) that is based on the population distribution. The population dataset is from the Web sites of the Gridded Population of the World and the Global UrbanRural Mapping projects (CIESIN 2014; GRUMP 2014), which are run by the Socioeconomic Data and Application Center (SEDAC) of Columbia University. According to this dataset, the surface of the world is divided into a grid of cells that can have different resolution levels. Each of these cells has been assigned an estimated population value. Out of the possible resolutions, we have opted for cells of 15 × 15 minutes of arc to constitute the basis of our model. This corresponds to an area of each cell approximately equivalent to an area of 25 × 25 km ^{2} along the Equator. The dataset comprises 823,680 cells, of which 250,206 are populated. Since the coordinates of each cell and those of the airports in the World Airport Network are known, the distance between the cells and the airports can be calculated.
For the H5N1 we consider only the estimated mobility for the World Airport Network (WAN) (Colizza et al. 2006; den Broeck et al. 2011). WAN is composed of 3362 commercial airports indexed by the International Air Transport Association (IATA) that are located in 220 different countries. The database contains the number of available seats per year for each direct connection between two of these airports. The coverage of the dataset is estimated to be 99% of the global commercial traffic. The WAN can be seen as a weighted graph comprising 16,846 edges whose weight, represents the passenger flow between airports. The network shows a high degree of heterogeneity both in the number of destinations per airport and in the number of passengers per connection.
The International AgroFood Trade Network (IFTN) (Convertino and Liang 2014; ErcseyRavasz et al. 2012) for the USA is built using data of (ComTrade 2014) for the worldwide linkages among countries, and data from the United States Department of Agriculture, Foreign Agricultural Service’s Global Agricultural Trade System (GATS) (FAS 2014; GATS 2014b) for the trade network of imported food commodities in USA. Additional data is used from the Agricultural Marketing Service (AMS 2014). The use of both datasets for food allows one to cross check erroneous data and to complement missing data missing. Data of imported and produced food that is locally consumed is obtained from FAOSTAT food balance sheets (FAOSTAT 2014). Such information is useful for calibrating the model factor that determines how many individuals consume the contaminated food.
Methods
 1.
Generation of potential surveillance networks within network topologies;
 2.
Modelbased detection of onset/outbreak sources with uncertainty assigned to the reported outbreaks for all feasible surveillance networks from (1); the determination of outbreak sources is performed by minimizing the uncertainty in the effective distance estimations; and,
 3.
Determination of the most informative surveillance network by maximizing the VoI considering all combinations of feasible surveillance networks corresponding to potential reporting patterns.
Effective distance and traceback model
The model is based on the optimal inference of epidemiological factors that better explain the spatiotemporal occurrence of foodborne outbreaks for any potential outbreak location to which different food trade paths correspond. In this study, differently from (Convertino and Hedberg 2014a), Manitz et al. (2014), and (Brockmann and Helbing 2013), deep uncertainty in reported outbreaks is introduced. The possible outbreak sources are determined by perturbing the reported outbreaks considering different surveillance networks. All feasible outbreak sources are tested considering their likelihood to satisfy the relationship between outbreak arrival times and velocity at any time step of the epidemic and for all infected communities simultaneously.
We define the effective distance (Brockmann and Helbing 2013) of directly connected nodes as a function of the most probable food trajectories defined by the radiation model (Section S2.1 in Additional file 1) that can be derived from the connectivity matrix P that specifies which nodes are connected; specifically d _{ mn }=1−logP _{ mn } where for any couple m−n there are multiple distances (Brockmann and Helbing 2013). For any candidate outbreak source we use the definition of “shortest path” effective distance D _{ mn } (Brockmann and Helbing 2013) from an arbitrary reference node n to another node m in the network (not necessarily directed connected) as the length of the shortest path from n to m, D _{ mn }=min_{ Γ } λ(Γ), where λ(Γ) is the directed length of an ordered path Γ=n _{1},…,n _{ L } as the sum of effective lengths along the path Brockmann and Helbing (2013). The average shortest path tree is calculated among all effective distances that are associated to potential candidate outbreak sources. Thus, both average effective distance (d _{ mn }) and average shortest path tree (that is the shortest path among many effective distances) responsible of outbreak spreading (D _{ mn }) depend only on the static mobility matrix weighted by the food fluxes. There is a family of effective distances and there one of shortest path distances between community m and n. Theoretically, there can be more than one shortest path distance but the dimension of the ensemble of shortest path distances is always smaller (or equal) than the ensemble of effective distances.
This equation states that effective outbreak distances D _{ e } can be computed with high fidelity based on outbreak arrival times on and effective spreading speed v _{ e }, and that each factor depends on different factors of the dynamical system considered (Brockmann and Helbing 2013). Note that here we indicate D _{ e } as D _{ mn } previously defined. The epidemiological factors associated with the classical SIR model determine the effective speed, whereas effective distance depends only on the topological features of the static underlying network, i.e., the matrix P. Because, T _{ a } and v _{ e } are know and estimated from data (that can be generated in realtime), respectively, it is easy to estimate D _{ e } of an outbreak considering also the uncertainty in epidemiological dynamics and food trade network. Thus, Eq. 1 can be used as a test of hypotheses where hypotheses are about all candidate outbreak sources responsible for the observed outbreak patterns. The most likely geographical distance can be assessed after determining the effective distance and considering all potential food trade paths. The SIR dynamics (Section “Epidemiological dynamics and information spreading: predictive metacommunity model”) is calibrated on the range of the outbreak velocity and arrival times, and is simulated by maximizing the prediction accuracy of the observed outbreak patterns in terms of cumulative outbreaks. Such accuracy is maximized when the uncertainty about the effective distance from the real outbreak source is minimized, that determines the maximization of the concentricity of outbreak spreading waves (Section “Outbreak detection: linking patterns and processes”). The concentricity is maximized only when the accuracy in the reported outbreaks is maximized that is equivalent to the maximum of the value of information (Section “Value of information and Pareto optimization”) considering any uncertainty source in outbreak patterns, supply chain and disease dynamics (Section “Global sensitivity and uncertainty analyses”).
Epidemiological dynamics and information spreading: predictive metacommunity model
where D=l ^{2} γ is the diffusion coefficient that is related to a classic form of a Fisher equation which has been deployed in mathematical epidemiology for describing wave spreading in reactiondiffusion system such as for epidemics (Belik et al. 2011; Campos and Mendez 2005). Note that \(s_{n}=s_{n}(\hat {s}(O),t)\) and \(j_{n}=j_{n}(\hat {s}(O),t)\) where \((\hat {s}(O),t)\) is the surveillance network in Equation 4.
For sufficiently localized initial conditions this systems exhibits traveling waves with speed \(c=2\sqrt {\alpha D(1\beta /\alpha)} \sim \sqrt {\gamma }\) (Belik et al. 2011). Such velocity can consider bias transport as in (Bertuzzo et al. 2007) in case the bias is known and elevated in the transport process. The telegraph model introduced by (Holmes et al. 1993) and simulated by (Bertuzzo et al. 2007) for cholera possesses both diffusion and wave motions. The Fisher equation of diffusion works well when using the effective distance, while the telegraph model equation works well when using the geographical distance of the transmission network. GSUA is used here to consider uncertainty in the traceback model and to assess the relative importance and interactions of factors  epidemiological and mobility network factors (Section “Epidemiological dynamics and information spreading: predictive metacommunity model” and Additional file 1: Section S2.1, respectively)  leading to outbreaks. Interactions of causal factors are typically neglected as well as the determination of the causality of such factors. The traceback model is run multiple times according to the Sobol scheme (Additional file 1: Section S1.2) (Figure 2) sampling all model factors along their probability distributions (Additional file 1: Figure S4) estimated from data or assumed as uniform according to a maximum entropy principle (Convertino et al. 2014a).
Outbreak detection: linking patterns and processes
The simplest method to determine which food and which outbreak source are the most likely to determine the observed outbreak patterns is to test which feasible candidate source has the lowest variability in terms of mean and variance assessed from Eq. 1. This is essentially a portfolio problem (Convertino and Valverde 2013) in which a large set of outbreak source alternatives exist and the whole set is evaluated considering average risk and variance, where in this case the “risk” is related to the inability to predict the correct outbreak patterns in space and time. Thus, for each potential candidate outbreak location the model computes the effective distance to the subset of nodes in which an outbreak is observed. This is done for any time step of the epidemic. On the basis of this set of effective distances (denoted by different outbreak sources), we compute the mean μ(D _{ e }) and standard deviation σ(D _{ e }) of the effective distance. As shown by (Brockmann and Helbing 2013), concentricity of epidemic waves increases with a combined minimization of mean and standard deviation of the estimated effective distance. In other words, such approach minimizes the deviation from the expected relationship arrival timesdistance, or velocitydistance, equivalently. Here we perform a minimization of the sum \(\sqrt {\mu (D_{e})^{2} + \sigma (D_{e})^{2}}\) as in a portfolio model where the objective function is the Euclidian distance built with the variables to minimize (Convertino and Valverde 2013). The correct outbreak source is the one that satisfies the minimum of the aforementioned sum for the whole epidemic duration. It is possible to visualize the dynamics of epidemic spread and concentricity of outbreak waves by plotting outbreak sources at the center of a network where all other nodes are placed around the central node at a radial distance equal to the effective distance. Correct outbreak sources determine cohesively evolving outbreak waves from the center to the nodes at the boundary from the beginning to the end of the epidemic.
Value of information and Pareto optimization
where C(t) and \(d(\hat {s}(O),t)\) is evaluated for all possible networks n (among all topologies (Additional file 1: Figure S1)), communities i, and time steps t; M is the total number of considered networks. ∝ stands for “proportional to”. Hence, the VoI is the difference of \(d(\hat {s}(O))\) estimated for different networks and all communities of the system. The VoI (Eq. 4) can be theoretically defined by the difference between two estimates of \(d(\hat {s}(O))\) for different surveillance networks (Eq. 4) (i.e. a different set of observer nodes). VoI is always taken equal or greater than zero and a rational decision maker always chooses more valuable information. Note that here we do not consider any cost function in the VoI because no information is available; however, that information is easily included as function detracted to the predictive benefits in Eq. 4. In presence of a physicalbased model reproducing outbreaks in space and time the VoI can also be measured as accuracy in predicting prevalence patterns. In fact, errors in detecting outbreak sources translate into errors in initial conditions of the model that reduce model predictive accuracy. Such predictions generated by models that take in input surveillance data are used for public health decision making and system design.
Global sensitivity and uncertainty analyses
GSUA involves five steps: (1) the probability distribution functions (pdfs) for each input factor are selected (epidemiological, mobility, and network topology factors); (2) sample points are generated on the input factor distributions using the Sobol method; (3) multiple model execution using each of the sample points and a set of outputs is generated; (4) global sensitivity analysis is performed (i.e., assessment of factor interaction and importance); and, (5) the important input factors are selected. Probability distributions of mobility network factors (link length and clustering coefficient) are derived from data as in (Convertino M, S L, M A, Morris S: Importance, Interaction, and Scaledependence of Cholera Outbreak Drivers: Metamodeling Predictions, submitted). Epidemiological factors (α, and β) and contamination factors (σ an ε) are assumed to be normally distributed within a normalized range [0,1]. γ is related to the range of variability of the total food and human mobility flux. As for human mobility, more than 60 million people travel billions of miles on more than 2 million international flights each week. GSUA is extremely important in complex systems that are dominated by factor interactions rather than an additive contribution of each single factor. In fact, classical sensitivity analysis fails to account for factor interactions and does not offer a full probabilistic investigation of system states; yet, classical one factor sensitivity analysis provides misleading conclusions. GSUA is rarely performed and classical onefactor at a time sensitivity approaches are used to evaluated uncertainty. GSUA first assign uncertainty and that drives sensitivity analysis via variability of model output that accounts for nonlinearity among model input factors and those and model output. The Sobol method (Sobol 1993, 2001) is a variancebased method that performs a quantitative analysis of model sensitivity based on the principle of variance decomposition. According to this principle, the full variance of the model output is given by the sum of the variances of all input factors (Saltelli et al. 2008). The Sobol method has the capacity to quantify the influence of the full range of variation of each input factor as well as the interaction effects among the input factors on the model output (Saltelli et al. 2008). The Sobol method estimates sensitivity measures which summarize the models behavior. The most common measure of sensitivity is the firstorder sensitivity index, S _{ i }, that represents the main effect (direct contribution) of each input factor to the variance of the output. It is expressed as S _{ i }=V _{ i }/V where V _{ i } is the part of the variance due to the input factor X _{ i }, and V is the total variance of the model output. Another measure of sensitivity is the total factor sensitivity that includes the interactions. The total effect index, \(S_{T_{i}}\), that is the result of the variance decomposition, accounts for the total contribution to the output variation due to factor X _{ i }, i.e., its firstorder effect plus all the higher order effects due to interactions (Saltelli et al. 2008). Thus, the total effect index of factor X _{ i } can be expressed as \(S_{T_{i}} = S_{i} + S_{i,i+1} + S_{i,i+1,i+2} + S_{i,i+1,i+2,i+3} + S_{i,i+1,i+2,i+3+\ldots +N_{\textit {IF}}} = S_{i} + S_{I_{i}}\), where N _{ IF } is the number of input factors and \(S_{I_{i}}\) is the sum of all the interaction indexes. The Sobol pairwise interaction between factors can be calculated and we identify this with the notation \(S_{I_{\textit {ij}}}\) where i and j are the factors that are considered. As an example, in the case of three input factors \(S_{T_{1}}\) is the total sensitivity index of X _{1}, S _{1} is the main effect of X _{1}, \(S_{I_{12}}\) is the interaction effect between X _{1} and X _{2}, and \(S_{I_{123}}\) is the interaction effect between X _{1}, X _{2}, and X _{3}. Considering the previous expression, \(S_{T_{1}}  S_{1}\) provides a measure of how much X _{1} is involved in interactions with all other input factors (Saltelli et al. 2008). The sum of all S _{ i } is equal to one for additive models and less than one for nonadditive models. The difference 1−S _{ i } can be used as an indicator of the presence of interactions in the model and we indicate that as S _{ I }. The number of simulations required for the Sobol method for a twoindex analysis (i.e., for first order and total indices) is given as N=M(2k+2) where M is the sample size of each index (typically taken between 5001000) and k is the number of uncertain input factors after the Morris screening. In this study because k=7, and we take M= 600 we have 9,600 Sobol simulations. Since the Sobol method uses a pseudorandomized multivariate sampling procedure, it can be used as a basis for a global uncertainty evaluation by constructing the pdfs and cumulative distribution functions for each of the selected outputs.
Results and discussion
Surveillance node coverage for achieving an outbreak source detection higher than 90%
Observers  

Outbreak (Network)  Random  High degree  maxVoI 
Cholera Cameroon 2010 (Tree)  20%  5%  3% 
Salmonella USA 2012 (Tree)  37%  13%  5% 
H5N1 2007 (Graph)  46%  38%  20% 
The ability of the maxVoI model to detect outbreak sources with high accuracy and low surveillance nodes is related to determination of the most meaningful nodes (in terms of known information for describing outbreak patterns) via a portfolio model with Pareto optimization that explores all potential combinations of observers and corresponding outbreak information. The VoI is maximized, thus the difference between two estimated distances from the real outbreak sources is maximized. The set of observer nodes for which the VoI is the maximum allows to predict the real outbreak source and the observed outbreak pattern.
Figure 6 also shows the result of GSUA in which uncertainty is considered for SIR and mobility model input factors. Results show that topological factors of the mobility network, namely the average link length and the clustering coefficient L and C, are the most important factors in explaining outbreak patterns. The average mobility rate γ and the invasion threshold ε that defined the number of freemoving contaminated food and/or individuals are very important and interacting factors after topological factors. Epidemiological transition factors, α and β, are secondary importance and weakly interacting factors because they are strongly dependent on the aforementioned factors. These results underlines even more the importance of knowledge and controllability of the network on which epidemic may occur. Mobility networks define where outbreaks can occur but also where surveillance should be crucially designed. Because a fully extended surveillance network is likely unfeasible considering also the highly varying mobility network of food and people, an optimally designed surveillance system via maxVoI is ideal in terms of benefits and costs.
As for H5N1 the source is correctly identified in Vietnam (Hanoi as the outbreak source); subsequently the epidemic is predicted to move to Thailand, and within weeks to spread to ten countries and regions in Asia, including Indonesia, South Korea, Japan and China. As for the Salmonella outbreak in tuna, the outbreak source is detected in New York City if only the US food trade is considered, and in Kerala, India, if the whole worldwide food trade network is taken into account. For the cholera epidemic in Cameroon, the health districts of Logone (North), Mokolo (West), and Yagoua (South East) are detected as outbreak sources. Considering the epidemic starting some time before the peak of cases, the urban health district of Maroua is detected as outbreak source. This evidences that: (i) the cholera epidemic in Cameroon is very likely generated by infections in rural areas and urban cases are due to infected people mobility among cities where humanhuman transmission is higher (in this case Maroua that is the capital of the Far North Region); and (ii) the definition of outbreak sources depend on the time horizon considered. For instance, Maroua is a source only if previous cases are neglected. Equivalent considerations are related to the scale of analysis: outbreak sources are related to the scale of the system considered. For example, it is known that cholera is endemic in large SubSaharan countries and the migration of populations, in which there are also infected people, among such countries is very high. For Cameroon, a high migration is reported from Nigeria in the East and Chad in the North, thus real outbreak sources may be located outside Cameroon. These considerations highlight the need to be very careful when planning control strategies that may result very inefficient for decreasing the systemic risk of infection because of the wrong targeting of outbreak source areas.
We show that efficient surveillance in the human mobility network is sufficient to detect outbreak source rapidly; this is despite complications played by wild bird migratory pathways and infected domestic birds for H5N1, and waterdriven pathogen mobility for cholera. The almost immediate detection of outbreak sources has been shown in previous papers (Brockmann and Helbing 2013; Convertino and Hedberg 2014a). Certainly, in general it is important to detect which transmission network causes the fastest spreading infections and we believe that in our cases, for both cholera an H5N1, the fastest infections are driven by humanhuman contacts related to the human mobility network as shown by epidemiological studies.
Conclusions

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 spatiotemporal anisotropy of outbreaks the smallworld 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.

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.

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.
The model can work for other kinds of spreading processes (e.g, viruses in computer networks and biomarker activation in chronic diseases) in which information spreads on partially known networks, although networks can be backinferred with a reverse application of the model where the question is the identification of the networks (among all candidate ones) that leas to the generation of observed epidemic patterns. For instance, an important application of the model is related to the determination of the most effective preparedness information, for example provided by the surveillance network or other information networks, with the aim to decrease the systemic risk of outbreaks in populations. Such information can be organized considering public media and/or online networks that inform people about performing tasks related to control outbreaks. Reversely, information networks can also be used as tools to more rapidly inform surveillance networks about the spread of outbreaks in populations.
Notes
Declarations
Acknowledgments
M.C. acknowledges fundings form the Healthy Food Healthy Lives Institute (grant “Impact of Local and Organic Foods on Food Safety: Assessment of Supply Chain Controls and Value Outcomes for Quantitativebased Food Policies”), the Institute on the Environment “Discovery Grant” (grant “Food System Design for Resilient Population Health: The Minnesota Model”), and the Minnesota Discovery, Research and InnoVation Economy (MnDRIVE) Global Food Systems Faculty Scholar StartUp Funding. Y.L. acknowledges the School of Public Health Dean Fellowship for the academic year 20142015.
Authors’ Affiliations
References
 AMS: Agricultural marketing service2014. Tech. rep. [http://www.marketnews.usda.gov/portal/fv?&paf_gear_id=1200002&movNavClass=FVPHN&rowDisplayMax=25&repType=movementDaily&previousVal=&lastCommodity=&paf_dm=full&lastLocation=&volume=40000&locName=MEXICO&locAbr=MX&startIndex=51&dr=1; Accessed April 2014]
 Bajardi, P, Barrat A, Savini L, Colizza V: Optimizing surveillance for livestock disease spreading through animal movements. J R Soc Interface2012.Google Scholar
 Batz, M, Hoffmann S, Morris JJ: Ranking the risks, the 10 pathogenfood combinations with the greatest burden on public health2011. Tech. rep., University of Florida, Emerging Pathogens Institute. [https://folio.iupui.edu/bitstream/handle/10244/1022/72267report.pdf; Date of access: 06/06/2013]
 Belik, V, Geisel T, Brockmann D: Natural human mobility patterns and spatial spread of infectious diseases. Phys Rev X2011, 1:011001.Google Scholar
 Bertuzzo, E, Maritan A, Gatto M, RodriguezIturbe I, Rinaldo A: River networks and ecological corridors: reactive transport on fractals, migration fronts, hydrochory. Water Resour Res2007, 43(4).Google Scholar
 Brockmann, D, Helbing D: The hidden geometry of complex, networkdriven contagion phenomena. Science2013, 342(6164):1337–1342.View ArticleGoogle Scholar
 Campos, D, Mendez V: Reactiondiffusion wave fronts on comblike structures. Phys Rev E2005, 71:051104.MathSciNetView ArticleGoogle Scholar
 CIESIN: The gridded population of the world version 3 (GPWv3) population grids2014. Tech. rep. [http://sedac.ciesin.columbia.edu/gpw; Accessed April 2014]
 ComTrade: United Nations commodity trade statistics2014. Tech. rep. [http://comtrade.un.org/db/; Accessed April 2014]
 Colizza, V, Barrat A, Barthlemy M, Vespignani A: The role of the airline transportation network in the prediction and predictability of global epidemics. Proc Nat Acad Sci USA2006, 103(7):2015–2020.View ArticleGoogle Scholar
 Convertino, M: Neutral metacommunity clustering and SAR: River basin vs. 2D landscape biodiversity patterns. Ecol Model2011, 222(11):1863–1879.View ArticleGoogle Scholar
 Convertino, M, Valverde LJ Jr: Portfolio decision analysis framework for valuefocused ecosystem management. PLoS ONE2013, 8(6):e65056.View ArticleGoogle Scholar
 Convertino, M, Hedberg C: Epidemic intelligence cyberinfrastructure: realtime outbreak source detection and prediction for rapid response. PLoS Curr Outbreaks2014a.Google Scholar
 Convertino, M, Hedberg C: Intellifood: cyberinfrastructure for realtime outbreak source detection and rapid response. Lecture Notes in Computer Science2014b, 8549:181–196. [Smart Health book, Springer].View ArticleGoogle Scholar
 Convertino, M, Liang S: Probabilistic supply chain risk model for food safety. GRF Davos Planet@Risk2014. 2.[Special Issue on One Health].Google Scholar
 Convertino, M, Muneepeerakul R, Azaele S, Bertuzzo E, Rinaldo A, RodriguezIturbe I: On neutral metacommunity patterns of river basins at different scales of aggregation. Water Resour Res2009, 45(8).Google Scholar
 Convertino, M, MunozCarpena R, ChuAgor M, Kiker G, Linkov I: Untangling drivers of species distributions: Global sensitivity and uncertainty analyses of MaxEnt. Envir Model Softw2014a, 51:296–309.View ArticleGoogle Scholar
 Convertino, M, Foran CM, Keisler JM, Scarlett L, LoSchiavo A, Kiker GA, Linkov I: Enhanced adaptive management: integrating decision analysis, scenario analysis and environmental modeling for the everglades. Sci Rep2014c, 2922:.Google Scholar
 Convertino, M, MunozCarpena R, Kiker G, Perz S: Design of monitoring networks by value of information optimization: experiment in the Amazon. Stochastic Environ Res Risk Assess2014d. In press.Google Scholar
 den Broeck, WV, Gioannini C, Goncalves B, Quaggiotto M, Colizza V, Vespignani A: GLeaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infect Dis2011, 11:.Google Scholar
 Estrada, E, GómezGardeñes J: Communicability reveals a transition to coordinated behavior in multiplex networks. Phys Rev E2014, 89(4).Google Scholar
 ErcseyRavasz, M, Toroczkai Z, Lakner Z, Baranyi J: Complexity of the international agrofood trade network and its impact on food safety. PLoS ONE2012, 7(5):e37810. [http://dx.doi.org/10.1371%2Fjournal.pone.0037810]View ArticleGoogle Scholar
 FAOSTAT: Food balance sheet website of the food and agriculture organization of the United Nations2014. Tech. rep. [http://faostat.fao.org/site/368/default.aspx#ancor; Accessed April 2014]
 FAS: USDA foreign agricultural service2014. Tech. rep. [http://www.fas.usda.gov/data; Accessed April 2014]
 GATS: Multistate outbreak of salmonella bareilly and salmonella nchanga infections associated with a raw scraped ground tuna product2014a. Tech. rep. [http://www.cdc.gov/salmonella/bareilly0412/index.html?s_cid=cs_654; Accessed April 2014]
 GATS: USDA global agricultural trade system2014b. Tech. rep. [http://www.fas.usda.gov/gats/default; Accessed April 2014]
 Goncalves, B, Balcan D, Vespignani A: Human mobility and the worldwide impact of intentional localized highly pathogenic virus release. Scientific Reports2013, 3(810).Google Scholar
 GRUMP: Global ruralurban mapping project alpha version population grids2014. Tech. rep. [http://sedac.ciesin.columbia.edu/gpw; Accessed April 2014]
 Guevart, E, Noeske J, Solle J, Essomba J, Edjenguele M, Bita A, Mouangue A, Manga B: Factors contributing to endemic cholera in Douala, Cameroon. Med Trop (Mars)2006, 66(3).Google Scholar
 Haldane, AG, May RM: Systemic risk in banking ecosystems. Nature2011, 469(7330):351–355.View ArticleGoogle Scholar
 Halpern, BS, Longo C, Hardy D, McLeod KL, Samhouri JF, Katona SK, Kleisner K, Lester SE, O/’Leary J, Ranelletti M, Rosenberg AA, Scarborough C, Selig ER, Best BD, Brumbaugh DR, Chapin FS, Crowder LB, Daly KL, Doney SC, Elfes C, Fogarty MJ, Gaines SD, Jacobsen KI, Karrer LB, Leslie HM, Neeley E, Pauly D, Polasky S, Ris B, St Martin K, et al: An index to assess the health and benefits of the global ocean. Nature2012, 488:615–620. [http://dx.doi.org/10.1038/nature11397]View ArticleGoogle Scholar
 Hanson, L, Zahn E, Wild S, Dopfer D, Scott J, Stein C: Estimating global mortality from potentially foodborne diseases: an analysis using vital registration data. Popul Health Metrics2012, 10(5).Google Scholar
 Helbing, D: Globally networked risks and how to respond. Nature2013, 497(7447):51–59. [http://dx.doi.org/10.1038/nature12047]View ArticleGoogle Scholar
 Helbing, D, Brockmann D, Chadefaux T, Donnay K, Blanke U, WoolleyMeza O, Moussaid M, Johansson A, Krause J, Schutte S, et al: How to save human lives with complexity science. J Stat Phys2014. SSRN 2390049. DOI 10.1007/s1095501410249.Google Scholar
 Holmes, EE: Are diffusion models too simple? A comparison with telegraph models of invasion. Am Nat1993, 142:403–419.View ArticleGoogle Scholar
 Keisler, J: Value of information in portfolio decision analysis. Decis Anal2004, 1(3):177–189. [http://da.journal.informs.org/content/1/3/177.abstract]View ArticleGoogle Scholar
 Kilpatrick, AM, Chmura AA, Gibbons DW, Fleischer RC, Marra PP, Daszak P: Predicting the global spread of H5N1 avian influenza. Proc Nat Acad Sci2006, 103(51):19368–19373.View ArticleGoogle Scholar
 Knobler, S, Mahmoud A, Lemon S, Pray L, on Microbial Threats EF: The Impact of Globalization on Infectious Disease Emergence and Control: Exploring the Consequences and Opportunities, Workshop Summary  Forum on Microbial Threats: The National Academies Press; 2006. [http://www.nap.edu/openbook.php?record_id=11588]
 Manitz, J, Kneib T, Schlather M, Helbing D, Brockmann D: Origin detection during foodborne disease outbreaks a case study of the 2011 EHEC/HUS outbreak in Germany. PLOS Curr Outbreaks2014.Google Scholar
 Mundt, CC, Sackett KE, Wallace LD, Cowger C, Dudley JP: Long?distance dispersal and accelerating waves of disease: empirical relationships. Am Nat2009, 173(4):456–466. [http://www.jstor.org/stable/10.1086/597220]View ArticleGoogle Scholar
 NAS: Improving food safety through a one health approach: workshop summary, Washington (DC): National Academies Press (US); 2012. ISBN13: 9780309259330ISBN10: 0309259339.Google Scholar
 Newman, M: The structure and function of complex networks. SIAM Rev2003, 45(2):167–256. [http://epubs.siam.org/doi/abs/10.1137/S003614450342480]MathSciNetView ArticleMATHGoogle Scholar
 Njoh ME: The cholera epidemic and barriers to healthy hygiene and sanitation in cameroon: a protocol study2010. Tech. rep., Umea University. [http://bvs.per.paho.org/texcom/colera/MENjoh.pdf]
 On Effectiveness of National Biosurveillance Systems: BioWatch C, the Public Health System NRC: BioWatch and Public Health Surveillance: Evaluating Systems for the Early Detection of Biological Threats: Abbreviated Version: The National Academies Press; 2011. [http://www.nap.edu/openbook.php?record_id=12688]
 Oakley, JE: Decisiontheoretic sensitivity analysis for complex computer models. Technometrics2009, 51(2):121–129. [http://www.tandfonline.com/doi/abs/10.1198/TECH.2009.0014]MathSciNetView ArticleGoogle Scholar
 Pandit, A, Crittenden J: Index of Network Resilience (INR) for Urban Water Distribution Systems. In Nature; 2012. [http://www.tisp.org/index.cfm?cdid=12519&pid=10261]
 Park, J, Seager TP, Rao PSC, Convertino M, Linkov I: Integrating risk and resilience approaches to catastrophe management in engineering systems. Risk Anal2012, 33:356–367. doi: 10.1111/j.15396924.2012.01885.x.View ArticleGoogle Scholar
 Pastore Y, Piontti, A, Gomes Da Costa, M, Samay N, Perra N, Vespignani A: The infection tree of global epidemics. Netw Sci2014, 2:132–137. [http://journals.cambridge.org/article_S2050124214000058]View ArticleGoogle Scholar
 Pinto, PC, Thiran P, Vetterli M: Locating the source of diffusion in largescale networks. Phys Rev Lett2012, 109:068702. [http://link.aps.org/doi/10.1103/PhysRevLett.109.068702]View ArticleGoogle Scholar
 Saltelli, A, Ratto TM, Andres, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S: Global Sensitivity Analysis  The Primer: John Wiley and Sons; 2008.Google Scholar
 Schlundt, J, Toyofuku H, Jansen J, Herbst S: Emerging foodborne zoonoses. Rev Sci Tech2004, 23(2).Google Scholar
 Scallan, E, Griffin P, Angulo F, Tauxe R, Hoekstra R: Foodborne illness acquired in the United statesUnspecified agents. Emerg Infect Dis2011a, 17:.Google Scholar
 Scallan, E, Hoekstra R, Angulo F, Tauxe R, Widdowson S, Roy J, Jones P, Griffin P: Foodborne illness acquired in the United StatesMajor Pathogens. Emerg Infect Dis2011b, 17(7).Google Scholar
 Simini, F, González MC, Maritan A, Barabási AL: A universal model for mobility and migration patterns. Nature2012, 484(7392):96–100. [http://dx.doi.org/10.1038/nature10856]View ArticleGoogle Scholar
 Sobol, I: Sensitivity analysis for nonlinear mathematical model. Math Comput Simul1993:407–414.Google Scholar
 Sobol, I: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul2001, 55:271–280.MathSciNetView ArticleMATHGoogle Scholar
 Stachenko, S: The role of surveillance and data use in the development of public health policies. Promot Educ2008, 15(3):27–29. [http://dx.doi.org/10.1177/1025382308095654]View ArticleGoogle Scholar
 TrainorGuitton, WJ, Mukerji T, Knight R: A methodology for quantifying the value of spatial information for dynamic Earth problems. Stochastic Environ Res Risk Assess2012:1–15. [http://dx.doi.org/10.1007/s0047701206194]
 Tatah, A, Pulcherie K, Mande N, Akum N: Investigation of water sources as reservoirs of Vibrio cholerae in Bepanda, Douala and determination of physicochemical factors maintaining its endemicity. Onderstepoort J Vet Res2012, 79(2).Google Scholar
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