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Calibration of agent‐based models (ABMs) is a major challenge due to the complex nature of the systems being modeled, the heterogeneous nature of geographical regions, the varying effects of model inputs on the outputs, and computational intensity. Nevertheless, ABMs need to be carefully tuned to achieve the desirable goal of simulating spatiotemporal phenomena of interest, and a well‐calibrated model is expected to achieve an improved understanding of the phenomena. To address some of the above challenges, this article proposes an integrated framework of global sensitivity analysis (GSA) and calibration, called GSA‐CAL. Specifically, variance‐based GSA is applied to identify input parameters with less influence on differences between simulated outputs and observations. By dropping these less influential input parameters in the calibration process, this research reduces the computational intensity of calibration. Since GSA requires many simulation runs, due to ABMs' stochasticity, we leverage the high‐performance computing power provided by the advanced cyberinfrastructure. A spatially explicit ABM of influenza transmission is used as the case study to demonstrate the utility of the framework. Leveraging GSA, we were able to exclude less influential parameters in the model calibration process and demonstrate the importance of revising local settings for an epidemic pattern in an outbreak. [ABSTRACT FROM AUTHOR]

The effects of water, sanitation, and hygiene (WASH) interventions have been well acknowledged to reduce the risk from diarrheal disease-causing pathogens. In spite of the recognized importance of WASH interventions on the reduction of diarrheal disease, there are still gaps in the understanding of the time-varying effects of interventions. To bridge this research gap, we developed agent-based models (ABMs) of diarrheal disease transmission in a community context. In the model, infections occur via two pathways: (i) between household members within the household environment and (ii) from the community environment outside the household. To measure the effectiveness of WASH interventions, we performed global sensitivity analysis (GSA) at the macro and micro temporal scales, varying the level of intervention coverage in the community. We simulated three intervention strategies, implemented separately in the experiments. The clean drinking water intervention, sanitation intervention, and hand washing intervention had similar success rates in the long-term. The handwashing intervention had the largest immediate effect. This highlights that proper short- and long-term intervention strategies need to be considered for disease control and the effective management of limited resources. [ABSTRACT FROM AUTHOR]

Alors que divers organismes internationaux ont récemment proposé des plans pour la création de systèmes de surveillance de la résistance antimicrobienne (RAM), la plupart des PRMF ne disposent pas des infrastructures et des ressources pour le faire efficacement. La RAM est donc un problème majeur d'équité en matière de santé, mais aussi un exemple de la connexion dans notre monde unique: les populations les plus à risque disposent des mécanismes de surveillance et de confinement les plus médiocres; dans le même temps, la migration mondiale, les voyages et le commerce nous rendent tous vulnérables. Les mesures que nous utilisons actuellement ne capturent pas de manière significative l'impact potentiel de la RAM aux niveaux sanitaire, économique et sociétal plus large. La surveillance mondiale de la RAM devrait inclure une surveillance étroite de l'utilisation d'antibiotiques chez les animaux, ainsi que les concentrations de divers produits chimiques libérés dans l'environnement. En fin de compte, les efforts coordonnés de divers scientifiques et parties prenantes, dans diverses disciplines et dans le monde, sont essentiels pour relever les défis pressants de la RAM mondiale croissante. [ABSTRACT FROM AUTHOR]

Objective To determine the temporal intervals at which spatial clustering of dengue hospitalisations occurs. Methods Space-time analysis of 262 people hospitalised and serologically confirmed with dengue virus infections in Kamphaeng Phet, Thailand was performed. The cases were observed between 1 January 2009 and 6 May 2011. Spatial coordinates of each patient's home were captured using the Global Positioning System. A novel method based on the Knox test was used to determine the temporal intervals between cases at which spatial clustering occurred. These intervals are indicative of the length of time between successive illnesses in the chain of dengue virus transmission. Results The strongest spatial clustering occurred at the 15-17-day interval. There was also significant spatial clustering over short intervals (2-5 days). The highest excess risk was observed within 200 m of a previous hospitalised case and significantly elevated risk persisted within this distance for 32-34 days. Conclusions Fifteen to seventeen days are the most likely serial interval between successive dengue illnesses. This novel method relies only on passively detected, hospitalised case data with household locations and provides a useful tool for understanding region-specific and outbreak-specific dengue virus transmission dynamics. [ABSTRACT FROM AUTHOR]

The creation of a spatial weights matrix by a procedure called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, is dependent on the use of a local spatial autocorrelation statistic. The result is (1) a vector that identifies those spatial units that are related and unrelated to contiguous spatial units and (2) a matrix of weights whose values are a function of the relationship of the ith spatial unit with all other nearby spatial units for which there is a spatial association. In addition, the AMOEBA procedure aids in the demarcation of clusters, called ecotopes, of related spatial units. Experimentation reveals that AMOEBA is an effective tool for the identification of clusters. A comparison with a scan statistic procedure (SaTScan) gives evidence of the value of AMOEBA. Total fertility rates in enumeration districts in Amman, Jordan, are used to show a real-world example of the use of AMOEBA for the construction of a spatial weights matrix and for the identification of clusters. Again, comparisons reveal the effectiveness of the AMOEBA procedure. [ABSTRACT FROM AUTHOR]

Spatial weights matrices are necessary elements in most regression models where a representation of spatial structure is needed. We construct a spatial weights matrix, W, based on the principle that spatial structure should be considered in a two-part framework, those units that evoke a distance effect, and those that do not. Our two-variable local statistics model (LSM) is based on the Gi * local statistic. The local statistic concept depends on the designation of a critical distance, dc , defined as the distance beyond which no discernible increase in clustering of high or low values exists. In a series of simulation experiments LSM is compared to well-known spatial weights matrix specifications--two different contiguity configurations, three different inverse distance formulations, and three semi-variance models. The simulation experiments are carried out on a random spatial pattern and two types of spatial clustering patterns. The LSM performed best according to the Akaike Information Criterion, a spatial autoregressive coefficient evaluation, and Moran's I tests on residuals. The flexibility inherent in the LSM allows for its favorable performance when compared to the rigidity of the global models. [ABSTRACT FROM AUTHOR]