Browsing by Author "Pusey, Anne"

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    Data, Model Documentation, and Output Supporting "Optimizing syndromic health surveillance in free ranging great apes: the case of Gombe National Park"
    (2018-05-24) Wolf, Tiffany, M; Wang, Wenchun, A; Lonsdorf, Elizabeth V; Gillespie, Thomas; Pusey, Anne; Gilby, Ian; Travis, Dominic A; Singer, Randall; wolfx305@umn.edu; Wolf, Tiffany M
    Syndromic surveillance is an incipient approach to early wildlife disease detection. Consequently, systematic assessments are needed for methodology validation in wildlife populations. We evaluated the sensitivity of a syndromic surveillance protocol for respiratory disease detection among chimpanzees in Gombe National Park, Tanzania. Empirical health, behavioral and demographic data were integrated with an agent-based, network model to simulate disease transmission and surveillance. Surveillance sensitivity was estimated as 66% (95% Confidence Interval: 63.1, 68.8%) and 59.5% (95% Confidence Interval: 56.5%, 62.4%) for two monitoring methods (weekly count and prevalence thresholds, respectively), but differences among calendar quarters in outbreak size and surveillance sensitivity suggest seasonal effects. We determined that a threshold weekly detection of ≥2 chimpanzees with clinical respiratory disease leading to outbreak response protocols (enhanced observation and biological sampling) is an optimal algorithm for outbreak detection in this population. Synthesis and applications: This is the first quantitative assessment of syndromic surveillance in wildlife, providing a model approach addressing disease emergence. Coupling syndromic surveillance with targeted diagnostic sampling in the midst of suspected outbreaks will provide a powerful system for detecting disease transmission and understanding population impacts.
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    Identifying Clusters in Marked Spatial Point Processes: A Summary of Results
    (2006-03-20) Mane, Sandeep; Kang, James; Shekhar, Shashi; Srivastava, Jaideep; Murray, Carson; Pusey, Anne
    Clustering of marked spatial point process is an important problem in many application domains (e.g. Behavioral Ecology). Classical clustering approaches handle homogeneous spatial points and hence cannot cluster marked spatial point process. In this paper, we propose a novel intuitive approach, Merge Algorithm, to hierarchically cluster marked spatial point process. This approach treats all spatial point processes in a dendrogram's sub-tree as a single spatial point process while clustering. The resulting dendrogram for marked spatial point process needs be analyzed by a domain expert to identify clusters. To remove the subjective nature of the clusters identified, we propose a novel statistical method, Cluster Identification Algorithm, to partition a dendrogram into clusters. This approach identifies (cuts) a dendrogram's sub-tree as a cluster if that subtree's intra-subtree similarity is significantly higher than inter-subtree similarity. Experiments with Jane Goodall Institute's chimpanzee ecological dataset from the Gombe National Park, Tanzania which shows that our proposed methods identified clusters which were compatible to those identified by domain experts.
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    Spatial Clustering Of Chimpanzee Locations For Neighborhood Identification
    (2005-09-15) Mane, Sandeep; Murray, Carson; Shekhar, Shashi; Srivastava, Jaideep; Pusey, Anne
    Since 1960, the chimpanzees (Pan troglodytes) of Gombe National Park, Tanzania, have been studied by behavioral ecologists, including Jane Goodall. Data has been collected for the last 40 years and it is now being further analyzed by researchers in order to increase our understanding of the social structure of chimpanzees. In this paper, we consider the following question of interest to behavioral ecologists Does clustering exist among female chimpanzees in terms of the spatial locations visited by them? The analysis of this question will help behavioral ecologists to learn about the space use and the social interactions between female chimpanzees. The data collected for this analysis are marked spatial point patterns over the park. Current spatial clustering methods lack the ability to handle such marked point patterns directly. This paper presents a novel application of spatial point pattern analysis and data mining techniques to the ecological problem of clustering female chimpanzees. We studied various spatial analysis techniques and found that the Ripleys K-function provides a powerful tool for evaluating clustering behavior among spatial point patterns. We then proposed two clustering approaches for marked point patterns based on this widely-used statistical K-function. Experimental results using the proposed clustering methods provide significant insight into the dynamics of female chimpanzee space use and into the overall social stucture of the species. In addition, the methods proposed here can be extended to also include temporal information.