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Model-driven development of covariances for spatiotemporal environmental health assessment

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Abstract

Known conceptual and technical limitations of mainstream environmental health data analysis have directed research to new avenues. The goal is to deal more efficiently with the inherent uncertainty and composite space-time heterogeneity of key attributes, account for multi-sourced knowledge bases (health models, survey data, empirical relationships etc.), and generate more accurate predictions across space-time. Based on a versatile, knowledge synthesis methodological framework, we introduce new space-time covariance functions built by integrating epidemic propagation models and we apply them in the analysis of existing flu datasets. Within the knowledge synthesis framework, the Bayesian maximum entropy theory is our method of choice for the spatiotemporal prediction of the ratio of new infectives (RNI) for a case study of flu in France. The space-time analysis is based on observations during a period of 15 weeks in 1998–1999. We present general features of the proposed covariance functions, and use these functions to explore the composite space-time RNI dependency. We then implement the findings to generate sufficiently detailed and informative maps of the RNI patterns across space and time. The predicted distributions of RNI suggest substantive relationships in accordance with the typical physiographic and climatologic features of the country.

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Acknowledgments

Partial support for this work was provided by a grant from the California Air Resources Board, USA (grant no. 55245A). Alexander Kolovos was supported by SpaceTimeWorks, LLC. José M. Angulo was partially supported by grants MTM2009-13250 of the SGPI, and P08-FQM-3834 of the Andalusian CICE, Spain.

Conflict of interest

The authors declare that they have no conflict of interest, nor has this ever been an issue in question during the entire period of research for this paper.

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Authors and Affiliations

  1. SpaceTimeWorks, LLC, 255 G St #105, San Diego, CA, 92101, USA

    Alexander Kolovos

  2. Departamento de Estadistica e Investigación Operativa, Universidad de Granada, Granada, Spain

    José Miguel Angulo

  3. School of Mining and Metallurgical Engineering, National Technical University of Athens, Athens, Greece

    Konstantinos Modis & George Papantonopoulos

  4. LREIS, Institute of Geographical Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Jin-Feng Wang

  5. Department of Geography, San Diego State University, San Diego, CA, 92182-4493, USA

    George Christakos

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  1. Alexander Kolovos
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  2. José Miguel Angulo
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Correspondence to Alexander Kolovos.

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Kolovos, A., Angulo, J.M., Modis, K. et al. Model-driven development of covariances for spatiotemporal environmental health assessment. Environ Monit Assess 185, 815–831 (2013). https://doi.org/10.1007/s10661-012-2593-1

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