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Background: Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from the early stages of an outbreak with the output of proposed models to forecast the future trend and prevalence of the pandemic. A significant problem in this area is the lack of standard well-defined evaluation measures to select the best algorithm among different ones, as well as for selecting the best possible configuration for a particular algorithm.Results: In this paper we present an evaluation framework which allows for combining different features, error measures, and ranking schema to evaluate forecasts. We describe the various epidemic features (Epi-features) included to characterize the output of forecasting methods and provide suitable error measures that could be used to evaluate the accuracy of the methods with respect to these Epi-features. We focus on long-term predictions rather than short-term forecasting and demonstrate the utility of the framework by evaluating six forecasting methods for predicting influenza in the United States. Our results demonstrate that different error measures lead to different rankings even for a single Epi-feature. Further, our experimental analyses show that no single method dominates the rest in predicting all Epi-features when evaluated across error measures. As an alternative, we provide various Consensus Ranking schema that summarize individual rankings, thus accounting for different error measures. Since each Epi-feature presents a different aspect of the epidemic, multiple methods need to be combined to provide a comprehensive forecast. Thus we call for a more nuanced approach while evaluating epidemic forecasts and we believe that a comprehensive evaluation framework, as presented in this paper, will add value to the computational epidemiology community. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers the maximum likelihood estimation of a class of structural vector autoregressive fractionally integrated moving-average (VARFIMA) models. The structural VARFIMA model includes the fractional cointegration model as one of its special cases. We show that the conditional likelihood Durbin–Levinson (CLDL) algorithm of Tsay (2010a) is a fast and reliable approach to estimate the long-run effects as well as the short- and long-term dynamics of a structural VARFIMA process simultaneously. In particular, the computational cost of the CLDL algorithm is much lower than that proposed in Sowell (1989) and Dueker and Startz (1998). We apply the CLDL method to the Congressional approval data of Durr et al. (1997) and find that the long-run effect of economic expectations on Congressional approval is at least 0.5718, which is over twice the estimate of 0.24 found in Table 2 of Box-Steffensmeier and Tomlinson (2000). This paper also tests the divided party government hypothesis with the CLDL algorithm. [Copyright &y& Elsevier]

Abstract: Up to now, of all the containers received in USA ports, roughly between 2% and 5% are scrutinized to determine if they could cause some type of danger or contain suspicious goods. Recently, concerns have been raised regarding the type of attack that could happen via container cargo leading to devastating economic, psychological and sociological effects. Overall, this paper is concerned with developing an inspection strategy that minimizes the total cost of inspection while maintaining a user-specified detection rate for “suspicious” containers. In this respect, a general model for describing an inspection strategy is proposed. The strategy is regarded as an (n+1)-echelon decision tree where at each of these echelons, a decision has to be taken, regarding which sensor to be used, if at all. Second, based on the general decision-tree model, this paper presents a minimum cost container inspection strategy that conforms to a pre-specified user detection rate under the assumption that different sensors with different reliability and cost characteristics can be used. To generate an optimal inspection strategy, an evolutionary optimization approach known as probabilistic solution discovery algorithm has been used. [Copyright &y& Elsevier]

This paper evaluates, for the first time, flash-flood guidance (FFG) values and recently developed gridded FFG (GFFG) used by the National Weather Service (NWS) to monitor and predict imminent flash flooding, which is the leading storm-related cause of death in the United States. It is envisioned that results from this study will be used 1) to establish benchmark performance of existing operational flash-flood prediction tools and 2) to provide information to NWS forecasters that reveals how the existing tools can be readily optimized. Sources used to evaluate the products include official reports of flash floods from the NWS Storm Data database, discharge measurements on small basins available from the U.S. Geological Survey, and witness reports of flash flooding collected during the Severe Hazards Analysis and Verification Experiment. Results indicated that the operational guidance values, with no calibration, were marginally skillful, with the highest critical success index of 0.20 occurring with 3-h GFFG. The false-alarm rates fell and the skill improved to 0.34 when the rainfall was first spatially averaged within basins and then reached 50%% of FFG for 1-h accumulation and exceeded 3-h FFG. Although the skill of the GFFG values was generally lower than that of their FFG counterparts, GFFG was capable of detecting the spatial variability of reported flash flooding better than FFG was for a case study in an urban setting. [ABSTRACT FROM AUTHOR]

This paper demonstrates the estimation and simulation of discharge and sediment concentration for two river basins in the USA and India. The first-order Sugeno fuzzy inference system was utilised to model the stage, discharge and sediment concentration relationship. A subtractive clustering algorithm, along with a least-squares estimation, was used to generate the fuzzy rules that describe the relationship between input and output data of stage, discharge and sediment concentration, which change over time. The fuzzy rules were tuned by a back-propagation algorithm. The results are illustrated using simulation and virtual reality. A comparison was made between the estimates provided by the neuro-fuzzy model and a multi-linear regression model. Different statistical criteria were used to evaluate the performance of both models in estimating discharge and sediment concentration. Comparison of the results reveals that, in general, the neuro-fuzzy model gives better estimates than the multi-linear regression model in terms of root mean square and sum of squares errors. Furthermore, compared with the multi-linear regression model, the neuro-fuzzy model yields statistical properties of estimates that are closer to actual historical data. [ABSTRACT FROM AUTHOR]

This paper describes methods for automatically analyzing formal, state-based requirements specifications for some aspects of completeness and consistency. The approach uses a low-level functional formalism, simplifying the analysis process. State-space explosion problems are eliminated by applying the analysis at a high level of abstraction; i.e., instead of generating a reachability graph for analysis, the analysis is performed directly on the model. The method scales up to large systems by decomposing the specification into smaller, analyzable parts and then using functional composition rules to ensure that verified properties hold for the entire specification. The analysis algorithms and tools have been validated on TCAS II, a complex, airborne, collision-avoidance system required on all commercial aircraft with more than 30 passengers that fly in U.S. airspace. [ABSTRACT FROM AUTHOR]