The National Strategy for Biosurveillancedefines biosurveillance as “the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels.” However, the strategy does not specify how “essential information” is to be identified and integrated into the current biosurveillance enterprise, or what the metrics qualify information as being “essential”. Thequestion of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of “essential information” for use in biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.
Citation: Generous N, Margevicius KJ, Taylor-McCabe KJ, Brown M, Daniel WB, Castro L, et al. (2014) Selecting Essential Information for Biosurveillance—A Multi-Criteria Decision Analysis. PLoS ONE 9(1): e86601. https://doi.org/10.1371/journal.pone.0086601
Editor: Indra Neil Sarkar, University of Vermont, United States of America
Received: August 12, 2013; Accepted: December 11, 2013; Published: January 29, 2014
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: The Defense Threat Reduction Agency, Joint Science and Technology Office for Chemical and Biological Defense is acknowledged as the sponsor of this work, under a “work for others” arrangement, issued under the prime contract for research, development, test, and evaluation services between the U.S. Department of Energy and Los Alamos National Laboratory (#B114525l). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
As defined in the National Strategy , biosurveillance is “the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels.” The systems and processes that constitute the biosurveillance (BSV) enterprise rely on a wide range of data that encompass human, animal, and plant health. An approach to enhancing biosurveillance capability is to increase the variety and range of data sources that are gathered, analyzed, and interpreted. Through the inclusionof new data typesit is possible to enhance existing surveillance systems as well as develop new and improvedversions. However, the inclusion and integration of newdata streams iscomplicated by a multitude of factors, such as the sheer diversity of potential data streams, the technical specifications and limitations of a system, financial constraints of system operators, etc. Building capability in this manner requires significant investments of technical, financial and human resources.
There is a recognized need for better methods and techniques within the biosurveillance community that would enable practitioners and system developers to prioritize and select the ‘best’ data streams for a biosurveillance system's specific intended use. In part, this is due to a lack of reliable and tested evaluation methods and criteria for evaluation. This presents a major hurdle to improving the efficiency of biosurveillance systems , , one which this team set out to address.
We used Multi-Attribute Utility Theory (MAUT), a type of multi-criteria decision analysis (MCDA), to develop ananalytic framework for biosurveillance data stream evaluation. MAUT and, more broadly, MCDA has been applied to assist decision makers with evaluation in a variety of fields that range from healthcare policyto power plant risk and urban planning –. The evaluation of biosurveillance data streams is a natural applicationfor MAUT.
MAUT is both an approach and a technique to analyze complex problems that produces a ranked list of prioritized options, also known as decision alternatives. It is a systematic approach to structuring a complex decision model where the decision alternatives include tradeoffs between costs and benefits. It simulates the decision making process by aggregating multiple single utility functions that each describe a certain facet of a decision alternative. The final utility score of an alternative is defined as the weighted sum of its single utility functions , . The alternatives can then be ranked according to their final utility score, providing decision makers with a ranked list of prioritized decision alternativeswithunderlying assumptions and uncertainties explicitly defined.MAUT can also consider both quantitative and qualitative indicators as part of its analysis, a unique feature not frequently found in other types of evaluations. Ultimately, MAUT assists a decision maker in understanding the options available for solving a problem when the options presented have multiple attributes and where there is clearly no obvious best solution .
In the area of selection of data streams for BSV, there is a need to have a method that can systematically analyze the benefits and disadvantages of a data stream andby framing the question of biosurveillance data stream inclusion using MAUT, it is possible to build an evaluation framework that could be deployed for use by members of the biosurveillance enterprise.
While there have been no previous attempts identified to build an evaluation framework using MAUT for biosurveillance data streams, there are several studies that evaluate specific data streams using single a metric as well as a sizeable literature on the evaluation of biosurveillance systems –. Generic frameworks have been developed but they typically focus on certain categoriesor types of systemssuch as the utility of public health syndromic surveillance systems indetecting terrorist attacks  or for the evaluation of automated detection algorithms , . Given the need of biosurveillance to consider disease activity across animal, plant, and human health, the applicability of many of these evaluation frameworks beyond their particular scope is limited.
Common methods of evaluation of biosurveillance systems use quantitative approaches that generally address only one or two attributes of a surveillance system . Qualitative approaches of attributes of these surveillance systems are applied much less frequently. Another common approach is theestimationof the relative sensitivities obtained through comparison amongst one or more biosurveillance systems.While many different attributes such as sensitivity, accessibility, timeliness, etc. have been identified as being important , , few evaluations could be considered comprehensive (i.e. assess more than one or two attributes) , . It is impossible for a single attribute to capture the full spectrum of criteria needed to make a robust evaluation. Even for evaluations that do describe multiple attributes, how the attributes are integrated or judged important israrely commented on .
Another characteristic lacking in traditional evaluation methods of biosurveillance systemsis the absence of context by which the system is being evaluated. Without understanding the goal, which in the case of biosurveillance can range from early detection of an outbreak to determining the effects of an outbreak control policy (such as vaccination), it is difficult to clearly define relevant measurableevaluative criteria. Evaluation methods that donot explicitly describe the context of evaluation weaken the rationale for selecting one attribute over because the evaluation metrics may change depending on the purpose of the biosurveillance system. The same metrics that are important for early detection (e.g. timeliness, time to detection, etc.) may not be as useful for another goal such as consequence management.Both the use of few attributes and the lack of explicitly defined biosurveillance objectives represent significant barriers to effective evaluation. Without these issues being addressed, it is not possible to have an unbiased and completeevaluation framework.
Using MAUT, we introduce a universal framework for evaluating biosurveillance data streams that can assess multiple attributes, both quantitative and qualitative, and that linksthese attributes to a specific (or defined) biosurveillance objective in order to provide a comprehensive and robust evaluation. By focusing on data streams used by biosurveillance systems, rather than evaluating the surveillance system itself, the framework becomes more universal in its application.This framework can be applied by biosurveillance practitioners, regardless of health domain, to assist in prioritizing the selection of data streams for inclusion into their surveillance program or system.
To demonstrate the utility of this framework, broad categories of biosurveillance data streams were evaluated. While the application of MAUT to evaluate specific data streams is the ultimate goal of the framework, this paper focused on broad categories of data streams in order to focus on the development of the framework (e.g. biosurveillance goals, metrics, decision criteria, etc.), thus laying the foundation for its eventual application to specific data stream evaluation. Having a tested, robust decision framework can assist practitioners and system developers in prioritizing the selection of data streams for inclusion in biosurveillance systems, thereby assisting in making valid, consistent, and justifiable programmatic decisions.
Proof of principle for the developed decision criteria framework is demonstrated by showing its applicability towards the evaluation of broad categories of data streams for inclusion in an integrated global infectious disease surveillance system.
Multi-attribute utility theory is a structured methodology that can calculate the overall desirability of an alternative in a single number thatrepresents the utility of that alternative. Theoverall desirability or utility of an alternative is calculated by the weighted sums of its measures (i.e. evaluation criteria). It is described by the following equation:Where U(x) is the overall utility score for the alternative X, n is the number of measures, w is the relative importance of the metric, and ui(x) is the score of alternative X on the ith metric, standardized in a scale from 0 to 1 .
The theoretical framework of MAUT relies on several assumptions: that the decision maker prefers more utility over less utility, that the decision maker has perfect knowledge about what is being evaluated, that the decision maker is consistent in his/her judgments, and that the evaluation criteria are independent from one another.
The commercially available software package Logical Decisions (LDW)  was used to implement MAUT for this project. While the implementation methodology was developed around the input requirements of the software, the MAUT framework can be used with a simple spreadsheet if needed and is therefore agnostic to the tool used, making it universal.
Development of Evaluation Framework
Our approach to the evaluation of data streams followed four broad stages—problem structuring, value elicitation, ranking, and sensitivity analysis—that could be sub-divided into seven steps, each of which were critically important to ensuring high confidence in our rankings (Table 1). The seven steps are described in the following paragraphs.
Under the problem structuring step, identification of biosurveillance goals, objectives, data streams, and metrics were identified through three approaches: a review of local, national and international surveillance systems, consultation with subject matter experts (SME), and areview of literature , . The SME panel consisted of experts in human, animal, and plant health who worked in different sectors of biosurveillance (e.g. military, civilian, local, international, etc.). Only contact and affiliation information was collected about the individual SME responding to the questionnaire, and the survey was strictly a means to record expert opinion. Therefore, this survey did not involve human subjects research, and institutional review of the survey was deemed unnecessary (Common Rule(45 CFR 46), LANL Human Subjects Research Review Board (HSRRB)).
1. Identification of biosurveillance goals and objectives
Without describing the goals and objectives of biosurveillance in detail, it is not possible to structure an analysis framework and determine the relationship between evaluation criteria and the surveillance aims. Additionally, the prioritization and weighting of the different evaluation criteria are likely to differ depending on biosurveillance objectives.
We developed four BSV goals relevant for integrated global biosurveillance. Data streams wereevaluated for each goal separately. With this approach, it was possible to identify data streams that whilenot useful for one goal, were highly relevant for another. The four goals are arranged over a time scale that extends from pre-event to post-event (the event being a disease outbreak) regardless of origin. Data stream categories were evaluated for each of the following broad surveillance goals:
- Early Warning of Health Threats: Surveillance that enables identification of potential threats including emerging and re-emerging diseases that may be undefined or unexpected.
- Early Detection of Health Events: Surveillance that enables identification of disease outbreaks (either natural or intentional in origin), or events that have occurred, before they become significant.
- Situational Awareness: Surveillance that monitors the location, magnitude, and spread of an outbreak or event once it has occurred.
- Consequence Management: Surveillance that assesses impacts and determines response to an outbreak or an event
The overarching objective in our evaluation framework was to determine the most useful data stream(s) for each of these biosurveillance goals.
2. Selection of biosurveillance data streams
Determining the most relevant data streams is highly dependent on the biosurveillance objective. While the data streams identified should relate to the objective being considered, there is no need to limit the choice of data streams to a single type of data as it is important to assess the full range of possible data streams that may be useful for accomplishing the objective.
While we identified several hundred specific data streams that could be evaluated with the framework, it would have been impractical and of limited value to generate a prioritized list of several hundred data streams. Rather, webinned the data streams into broader categories/types of data streams and evaluated these categories in order to provide a moreusefuland informative result.Sixteen data steam categories were developed as shown in Table 2 . This approach requireda level of detail that struck a balance between being too specific and too broadand allowed us realistic data set sizes for initial studies. However, a more in-depth data stream analysis couldbe performed using the same framework we have developed.
3. Selection of Metrics
Metrics are the attributes, evaluation criteria, or measuresby which data streams are assessed. They should be carefully selected to be complete, measurable, mutually independent, and non-redundant. There is no optimal number of measures and the number will likely depend on the biosurveillance goal. However, if too few measures are chosen the results of the evaluation are likely not comprehensive. Conversely, if too many are chosen, it may needlessly complicate the analysis without necessarily leading to more useful results. A balance needs to be achievedbetween these two factors.
Similar to the objective formulation, we identified and selected measures (metrics) using a systematic and iterative process. It is important to note that unlike most of the previous literature, this project focused on describing metrics that would be used for evaluation of data streams not surveillance systems. Furthermore, because we evaluated data streams at a higher category level, many common metrics used to assess systems, such as positive predictive value, negative predictive value, sensitivity, and specificity etc. were not applicable –.
Table 3 shows the list of 11 metrics developed by us for the evaluation of biosurveillance data streams. The table also provides definitions for each metric.
These metrics and definitions were used and refined throughout the process of the evaluation of the data streams. Every effort was made to develop metrics that would assess unique features of a data stream and would not overlap. However, it was clear that many of the metrics might have some level ofinterdependency. For example, cost andaccessibility are likely to be related—the cheaper it is to access that data stream, the higher the accessibility. A similar correlation exists between credibility and timeliness—the more quickly the data is available, the less likely it is credible. This interdependency could not be captured in the tool that we employed for evaluation.
When identifying the measures that describe the data streams, it was also important to determine how they could be measured because each measure needs to be described by a single utility function that describes the relationship between the value input and the utility the input provides towards achieving the goal. The values can be either quantitative or qualitative and the relationship between the value and utility needs to be explicitly defined. If the values are qualitative, concrete indicators need to be developed so that the data streams can be uniformly assessed. For example, we developed descriptions for measuring or assigning values to accessibility with three options that have specific criteria:
- Difficult Accessibility—is when the data stream being analyzed has been used in at least one system and faces many (3 or more) obstacles in data access
- Medium Accessibility—is when the data stream being analyzed has been used in at least one system and faces some (less than 3) obstacles in data access
- Easy Accessibility—is when the data from a particular data stream is freely accessible.
Examples of obstacles include: privacy concern, passwords, subscription, membership/group affiliation, non-digitized information, etc. Table 4 displays the utility scores and labels that were used to describe the metrics. MAUT converts the values input for each metric to a common unit termed utility. It is important to note that the common unit utility is not the same as measuring utility (i.e. the “usefulness” of something). Utility is the unit that MAUT measures and works with in order to determine the overall utility (usefulness) of each alternative (data stream) from the evaluation criteria (metrics). Additionally, the relationship between utility and the values input for the criteria need to be defined (a utility function). For example, if the metric is cost, then the utility will decrease as the cost increases. The values can be specified as a quantity as well as by labels, which are text descriptions of the possible levels for each metric. Supplementary methods S1 contains information on the criteria used to assess the qualitative labels forthe metrics.
4. Assignment of Metric Weights
Not all metrics contribute equally to the utility of the data stream. Weights are assigned to metrics and can be used to define the relative importance of the metric towards achieving the biosurveillance goal. Many methods can be used to assign weights to metrics. Weights can be established via group discussion and deliberation, expert elicitation, or even direct rating of measures. To assign weights to the 11 metrics developed for the evaluation, weconsulted our SME panel and asked them to rank the metrics from 1 to 11 in order of importance for each objective. Definitions of the metrics and biosurveillance goal were provided and each SME was asked to rank according to the definitions provided (Table 3, Table 5). This approach reduced the possibility of individual biases in weighting based on one's interpretation of the terms. From the lists generated by the SMEs, the average rank of each metric was used to generate a priority list for each goal,.
The rankings were then converted into metric weights using a mathematical technique called swing weighting, which is used in Simple Multi-Attribute Rating Technique Extended to Ranking (SMARTER) , . By knowing the rank of the metrics, setting the value for the sum of weights to be 1 and giving equal weights to metrics if the preference is the same (i.e. if multiple metrics are ranked the same), the weights can be derived for each metric. Table 6 shows the weights derived for the metrics.
5. Collection of Information – assignment of values to alternatives
As we evaluated data stream categories instead of individual, specific data streams,we faced a challenge with assigning values for each of the metrics for categories. To address this challenge,wefocused on the properties of data streams that were functional within operational biosurveillance systems, tools, or organizations, preferably global ones. The underlying assumption was that the individual, specific data streams within these systems were representative of the data stream category as a whole. This approach then could derive results that were grounded within the operational context of data streams within current surveillance systems, and while not ideal, allowed for the problem to be structured in a way that would yield meaningful results for development of the MAUT methodology of biosurveillance. For several data stream categories, we looked at more than one surveillance system to inform our assignment of values. Table 7 identifies the surveillance systems used to represent the data stream category. It is important to note that because not all data streams binned in the category would have these representative metric values, the results cannot be used to understand a specific data stream. Our approach was to use the categories to develop the framework for an initial top-level comparison of data streams in order to see if MAUT could be applied to biosurveillance data streams, paving the way for understanding how to use MAUT to evaluate specific data streams.
Two members of our team independently reviewed the documentation of the surveillance systems for information regarding the properties of the data streams and applied the concrete indicators developed for the metrics to derive values for use in the analysis. These values were then placed into a matrix (Table 8) that contained the value assigned to each data stream for each metric. If there were differences in the two independent reviews, a consensus was built through detailed discussions and gathering evidence base for the values.
Results and Discussion
The results presented in this section are primarily to illustrate the application of the MAUT evaluation framework. There are several caveats to the assigned values for data streams as well as the assigned weights to the metrics that are being further investigated.
The purpose of MAUT is not to serve as a decision maker but, rather, is to inform and support the decision making process. The decision maker should use this prioritized list to inform their thought process and to help make justifiable and transparent decisions.The utility values determined for each of the data streams can be used to create a prioritized list of options.
Data stream ranking was performed through the development of objectives hierarchies, a value tree that describes the hierarchy between the metrics and objectives. As the prioritizationof the metrics is dependent on the context of the biosurveillance objective, we had to design four hierarchies—one for each goal. While the hierarchies were the same for each, the objective specified and, thus, the metric weightswere different (Figure 1).
Following input of weights for metrics, values for each data stream for each metric and a single utility function for these values, the LDW tool generated four ranked lists of data streams, one for each surveillance goal, shown in Table 9.
Across the four biosurveillance objectives, there was a dichotomy exhibited between data streamcategory ranks in the early warning/early detection objectives and the situational awareness/consequence management objectives. As observed in Table 9, the ranks for the data streams are fairly consistent within the early warning/early detection goals and within the situational awareness/consequence management objectives. This seems to suggest that while we identified four distinct biosurveillance goals, functionally from the metric weights applied, there may only be two: pre/early event (i.e. the initial stages of an outbreak) and post-event.
Ranking results are a direct consequence of the values we assigned for each broad data stream category (as described in the methods), therefore, these results cannot be applied to specific data streams. Evaluation of specific data streams would invariably lead to differences in assigned values and would be reflected in the ranks attained. By generalizing, however, we were able to build a framework that could be easily applied to specific data streams.Four categories consistently ranked within the top five for every single goal: Internet Search Queries, ED/Hospital Records, Clinic/Healthcare Provider, and Laboratory Records. Three of these—ED/Hospital Records, Clinic/Healthcare Provider, and Laboratory Records—are commonly used in current systems; only Internet Search Queries are underutilized used as a data stream in operational biosurveillance systems.However, given how new Internet Search Queries are, it is not entirely unexpected and it may take time before this data stream category is adopted as a reliable source in systems. In the next level four data stream categories ranked consistently high among the similar goals (early warning/detection vs. situational awareness/consequence management): Official Reports, Personal Communication, News Aggregators, and Ambulance/EMT records. Official Reports ranked quite high for both situational awareness and consequence management, primarily due to the high values assigned for credibility and specificity of detection.
Social Media, Help Lines, and Sales data streams were all ranked at least once amongst the top five. After these data stream categories, there was a significant drop off in the ranks. In particular, five data streamscategorieswere consistently identified as being the least useful: Financial Records, Established Databases, Prediction Markets, Employment/School Records and Police/Fire Department Records. It is important to note that while certain data stream categories ranked low, it does not mean they are useless. It only means that for assigned values and the metric weights for this categorization ranked them low. Specific data streams that might have different values and weights could be evaluated differently depending on how the problem is being framed. For example, data stream categories such as Financial Records and Established Databases may be very useful when used together with more highly ranked data streams but given the limitations of this approach; it was not possible to take potential synergy of data streams into account.
The rankings of three data stream categories—Personal Communications, AmbulanceRecords, and News Aggregators—did not align with the experiences of several biosurveilance practitioners. While ranked highly for the situational awareness and consequence management goals, Personal Communication ranked towards the middle for the early warning and early detection goals. However, this data stream category was often cited byepidemiologists and biosurveillance practitioners as being one of the most important data streams they utilized to detect outbreaks in its early stages and to monitor its progress. Personal communications tend to be informal, highly unique and diverse in nature making it difficult to assign attributes using our approach—analysis by categories of data streams. A better understanding of the nature of these informal personal communication networks and what roles they may play in the decision making process leading up to an outbreak declaration may lead to some valuable insights that may lead to possible incorporation into future models.
Similarly, Ambulance Records ranked highly for both the Situational Awareness and Consequence Management goals. This result also did not align with the experiences of biosurveillance practitioners who described this data stream category as being highly useful for Early Warning. News Aggregators, while ranked highly for the Early Detection and Early Warning Goals, were deemed more useful for the other BSV goals by practitioners It is possible that the discrepancies in utility seen between the MAUT method and individual opinion is due to the fact that individuals may be inherently biased and may not take into account the multiple metrics that are considered in MAUT.
Given the highly customizable nature of MAUT, it was important to scope the problem and be able to obtain a defensible set of rankings for the data stream categories. The concept of “garbage in, garbage out” is equally as applicable to MAUT as it is to the field of computer science. Without properly structuring the problem and if poor data input choices are made, the output of the analysis is meaningless. The LDW tool as well as MCDA as a whole relies heavily on user input and customization and the rankings reported in this study may be influenced by the input parameters. It was important to make sure that the framework was robust as reflected in the stability of the rankings against variations in parameters. Sensitivity analysis was conducted by varying the dependent variables to understand their influence on data stream rankings. It is important to note that all changes for eachstrategy were applied simultaneously rather than looking at the effect of one variable sequentially, in order to maintain a realistic scope for the number of sensitivity analyses. The following strategies were used for this analysis:
- Varying the utility function that describes the relationship between metric value and utility. By varying the utility function, it is possible to assess the impact of our assumptions on the relationship between the metric value and utility.
- Varying weights of metrics; changing the weights in two ways assessed the impacts of the metric weights. The first was to set all metric weights equally so that each metric contributed to the final utility score. The second was to group the rankings of the metrics into three tiers (Table 10).
- Performing rankings without Geographic/Population metric; for each data stream with the exception of three—Financial Records, Sales, and Help Lines—Geographic/Population coverage was uniformly assigned a value of “Global”. To see what impact this metric had on the final rankings, the rankings were recomputed without the Geographic/Population coverage metric.
- Changing the most variable metric values in the matrix; we assigned values to data streams for each metric, using representative biosurveillance resources that routinely used specific data streams. To examine the influence of variable values on the final ranking of data stream categories, we ran Logical Decisions with an input of all low values for the data streamsthat showed most variability in certain metrics because in all cases, the final run utilized the higher values. This may also test the effect of individual biases.
Tables 11–14 show the comparison of rankings obtained following sensitivity analysis, for each of the four biosurveillance goals. Overall, with the different sensitivity analyses, the results of the modified rankings suggest that the results obtained in the final rankings are robust. The same data streams that tend to be ranked as being most useful remain the top ranked. Similarly, the same data streams that tend to be ranked in the middle and at the bottom in the final rankings are observed to do the same in the modified rankings. Also, overall, there were few rises in rankings amongst the data streams across the different biosurveillance objectives.
Through the development of an evaluation framework for determining the utility of biosurveillance data streams, and application of the MAUT tool to rank data streams, we have demonstrated aproof of principle for the application ofmulti-criteria decision analysis to the problem of data stream selection for biosurveillance systems. Thisuniversal evaluationframework offers biosurveillance practitioners a structured, methodological approach to evaluating data streams for inclusion into biosurveillance systems and forces systematic thinking. By employing MAUT, this framework seeks to address many of the shortcomings found in evaluations of biosurveillance systems. It is capable of evaluating multiple criteria, both qualitative and quantitative, thus allowing for a more comprehensive evaluation than if a single criterion were used. Additionally, MAUT is a flexible enough tool that can be configured to evaluate multiple types of biosurveillance data streams and can be configured to handle quantitative criteria, a feature we did not use. The danger in prescribing a single set of unvarying metrics or weights of metrics is that biosurveillance is a multi-faceted process and the metrics that may be useful for one surveillance goal may not be useful for another. This is extremely true as the target of surveillance changes species or disease type. The advantage of our framework is that it is species and disease agnostic and can be employed to evaluate the many types of data streams found in biosurveillance.
MAUT provides a systematic and structured methodology for biosurveillance practitioners presented with a complex decision—the selection of essential information. Most decision-making in the realm of public health or biosurveillance data stream evaluation has traditionally relied upon a highly subjective, ad hoc approach that favors intuition and personal experience or utilizes one or two quantitative metrics that are unable to capture the range of criteria needed to systematically evaluate data streams . Part of the challenge is that people are “quite bad at making complex, unaided decisions”  and relying just on intuition or personal experience does not lead to better decisions . Since most complex decisions require considering multiple metrics, including non-quantitative ones, a method that can incorporate both is essential. Simply relying upon the expertise of an individual or a small group of individuals will not, alone, address the common shortfalls currently in practice in the face of complex decision making. MAUT additionally provides biosurveillance practitioners with an open, explicit, and defensible approach that can also serve as an audit trail.
Using a MAUT framework and approach provides a formal technique that can assess both quantitative and qualitative metrics (which are characteristics of complex decisions), thereby providing the decision maker with an methodology that can deconstruct the complex decision into multiple, more manageable pieces, allow data and judgment to be made to the smaller pieces, and then to reconstruct the pieces into a more complete picture of the problem for the decision maker.
While the MAUT approach offers a systematic and objective approach to evaluating data streams, there are inherent limitations to this approach that must be carefully considered and accounted for when interpreting the results of such an analysis.
MAUT is heavily data driven and requires significant user input to structure the problem and elicit the values, and if this input is inaccurate, the results can be of little value. Facilitating an accurate analysisthrough rigorous stakeholder elicitation can be both time consuming and expensive. Because of this heavy dependence on user input, MAUT is sensitive to omitted or inaccurate input. In the case of our application of the method and framework, this potential limitation is observed in howthe values to input for themetrics were determined. By focusing on using values and properties of data streams in use within a biosurveillance system that was representative of that type of data stream category, the results maybe biased towards more traditional data stream categories.Another limitation that also stems from the heavy user input dependence wasobserved in the non-representative group opinionwe used to elicit metric weights. In particular, the composition of this project's SME panel exhibited a bias towards experts in human health that represented an understanding of surveillance practiced predominantly within the developed world, and was largely academic. As a result, their opinions onmetric weights may not accurately align with operational practice. This in particular is observed by the near universal ranking of cost as being a metric of low importance, in spite of biosurveillance practitioners identifying it as one of the most important. This bias was additionallyobserved and supported by Gajweski et al.  who in the course of reviewing evaluationsof electronic event-based biosurveillance systems noted that the least frequently considered attribute was cost.
As MAUT treatseach metric independently, interdependencies amongst metrics cannot be taken into account—the interrelation between accessibility and cost being an example. Similarly, this framework does not consider the synergistic effects that may emerge when utilizing multiple,different but complementary data streams. For example, certain data streams, such as personal communications and established databases, lend themselves as being useful in a synergistic fashion. Historical climate data that can be used to establish baseline levels of weather, while not indicative of a disease outbreak directly, can be used to predict mosquito incidence. Both of these limitations are present in this evaluation of data streams. An additional limitation to MAUT is that it assumes that maximization of utility is the most important criteria in the decision making process which may not be true. For example, in some cases political factors may play a larger role to the decision maker than sheer maximization of utility. This project demonstrates a proof of principle for the application of MAUT to biosurveillance data stream evaluation, and hopes to build upon this to refine the use of MAUT.
The ranking of data streams served to illustrate the application of our evaluation framework. While we used a certain approach assigning values to data streams and weights to metrics, we acknowledge that this is by no means the best approach and that there may be several, better ways to do so. We hope to primarily convey the systematic and structural approach to thinking about how to select “essential information”.
In today's economic and political climate, there is even more of a need for evaluation of potential and current biosurveillance systems and data streams to ensure that limited financial and human resources are being effectively deployed. We have demonstrated the utility of an evaluation framework based on MAUT that can assist practitioners in their decision making process. This framework balancesthe complexity of biosurveillance data stream evaluation by minimizing the subjectivity of evaluation and by providing documentation that allows for increased transparency and consistency. This evaluation framework and MAUT model should be used with careful consideration to the sensitivity and robustness of results and should be seen not so much as a decision making tool but rather as a decision aid to supportthe prioritization and selection of data streams for specific biosurveillance goals.
Conceived and designed the experiments: NG KJM KJT MB WBD LC AH AD. Performed the experiments: NG KJM KJT MB WBD LC AH AD. Analyzed the data: NG KJM KJT MB AD. Wrote the paper: NG.
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